case study vs correlational study

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 12 methods for correlational studies.

Francis Lau .

12.1. Introduction

Correlational studies aim to find out if there are differences in the characteristics of a population depending on whether or not its subjects have been exposed to an event of interest in the naturalistic setting. In eHealth, correlational studies are often used to determine whether the use of an eHealth system is associated with a particular set of user characteristics and/or quality of care patterns ( Friedman & Wyatt, 2006 ). An example is a computerized provider order entry ( cpoe ) study to differentiate the background, usage and performance between clinical users and non-users of the cpoe system after its implementation in a hospital.

Correlational studies are different from comparative studies in that the evaluator does not control the allocation of subjects into comparison groups or assignment of the intervention to specific groups. Instead, the evaluator defines a set of variables including an outcome of interest then tests for hypothesized relations among these variables. The outcome is known as the dependent variable and the variables being tested for association are the independent variables. Correlational studies are similar to comparative studies in that they take on an objectivist view where the variables can be defined, measured and analyzed for the presence of hypothesized relations. As such, correlational studies face the same challenges as comparative studies in terms of their internal and external validity. Of particular importance are the issues of design choices, selection bias, confounders, and reporting consistency.

In this chapter we describe the basic types of correlational studies seen in the eHealth literature and their methodological considerations. Also included are three case examples to show how these studies are done.

12.2. Types of Correlational Studies

Correlational studies, better known as observational studies in epidemiology, are used to examine event exposure, disease prevalence and risk factors in a population ( Elwood, 2007 ). In eHealth, the exposure typically refers to the use of an eHealth system by a population of subjects in a given setting. These subjects may be patients, providers or organizations identified through a set of variables that are thought to differ in their measured values depending on whether or not the subjects were “exposed” to the eHealth system.

There are three basic types of correlational studies that are used in eHealth evaluation: cohort, cross-sectional, and case-control studies ( Vandenbroucke et al., 2014 ). These are described below.

• Cohort studies – A sample of subjects is observed over time where those exposed and not exposed to the eHealth system are compared for differences in one or more predefined outcomes, such as adverse event rates. Cohort studies may be prospective in nature where subjects are followed for a time period into the future or retrospective for a period into the past. The comparisons are typically made at the beginning of the study as baseline measures, then repeated over time at predefined intervals for differences and trends. Some cohort studies involve only a single group of subjects. Their focus is to describe the characteristics of subjects based on a set of variables, such as the pattern of ehr use by providers and their quality of care in an organization over a given time period.
• Cross-sectional studies – These are considered a type of cohort study where only one comparison is made between exposed and unexposed subjects. They provide a snapshot of the outcome and the associated characteristics of the cohort at a specific point in time.
• Case-control studies – Subjects in a sample that are exposed to the eHealth system are matched with those not exposed but otherwise similar in composition, then compared for differences in some predefined outcomes. Case-control studies are retrospective in nature where subjects already exposed to the event are selected then matched with unexposed subjects, using historical cases to ensure they have similar characteristics.

A cross-sectional survey is a type of cross-sectional study where the data source is drawn from postal questionnaires and interviews. This topic will be covered in the chapter on methods for survey studies.

12.3. Methodological Considerations

While correlational studies are considered less rigorous than rct s, they are the preferred designs when it is neither feasible nor ethical to conduct experimental trials. Key methodological issues arise in terms of: (a) design options, (b) biases and confounders, (c) controlling for confounding effects, (d) adherence to good practices, and (e) reporting consistency. These issues are discussed below.

12.3.1. Design Options

There are growing populations with multiple chronic conditions and healthcare interventions. They have made it difficult to design rct s with sufficient sample size and long-term follow-up to account for all the variability this phenomenon entails. Also rct s are intended to test the efficacy of an intervention in a restricted sample of subjects under ideal settings. They have limited generalizability to the population at large in routine settings ( Fleurence, Naci, & Jansen, 2010 ). As such, correlational studies, especially those involving the use of routinely collected ehr data from the general population, have become viable alternatives to rct s. There are advantages and disadvantages to each of the three design options presented above. They are listed below.

• Cohort studies – These studies typically follow the cohorts over time, which allow one to examine causal relationships between exposure and one or more outcomes. They also allow one to measure change in exposure and outcomes over time. However, these studies can be costly and time-consuming to conduct if the outcomes are rare or occur in the future. With prospective cohorts they can be prone to dropout. With retrospective cohorts accurate historical records are required which may not be available or complete ( Levin, 2003a ).
• Case-control studies – These studies are suited to examine infrequent or rare outcomes since they are selected at the outset to ensure sufficient cases. Yet the selection of exposed and matching cases can be problematic, as not all relevant characteristics are known. Moreover, the cases may not be representative of the population of interest. The focus on exposed cases that occur infrequently may overestimate their risks ( Levin, 2003b ).
• Cross-sectional studies – These studies are easier and quicker to conduct than others as they involve a one-time effort over a short period using a sample from the population of interest. They can be used to generate hypotheses and examine multiple outcomes and characteristics at the same time with no loss to follow-up. On the other hand, these studies only give a snapshot of the situation at one time point, making it difficult for causal inference of the exposure and outcomes. The results might be different had another time period been chosen ( Levin, 2006 ).

12.3.2. Biases and Confounders

Shamliyan, Kane, and Dickinson (2010) conducted a systematic review on tools used to assess the quality of observational studies. Despite the large number of quality scales and checklists found in the literature, they concluded that the universal concerns are in the areas of selection bias, confounding, and misclassification. These concerns, also mentioned by Vandenbroucke and colleagues (2014) in their reporting guidelines for observational studies, are summarized below.

• Selection bias – When subjects are selected through their exposure to the event rather than by random or concealed allocation, there is a risk that the subjects are not comparable due to the presence of systematic differences in their baseline characteristics. For example, a correlational study that examines the association between ehr use and quality of care may have younger providers with more computer savvy in the exposed group because they use ehr more and with more facility than those in the unexposed group. It is also possible to have sicker patients in the exposed group since they require more frequent ehr use than unexposed patients who may be healthier and have less need for the ehr . This is sometimes referred to as response bias, where the characteristics of subjects agreed to be in the study are different from those who declined to take part.
• Confounding – Extraneous factors that influence the outcome but are also associated with the exposure are said to have a confounding effect. One such type is confounding by indication where sicker patients are both more likely to receive treatments and also more likely to have adverse outcomes. For example, a study of cds alerts and adverse drug events may find a positive but spurious association due to the inclusion of sicker patients with multiple conditions and medications, which increases their chance of adverse events regardless of cds alerts.
• Misclassification – When there are systematic differences in the completeness or accuracy of the data recorded on the subjects, there is a risk of misclassification in their exposures or outcomes. This is also known as information or detection bias. An example is where sicker patients may have more complete ehr data because they received more tests, treatments and outcome tracking than those who are healthier and require less attention. As such, the exposure and outcomes of sicker patients may be overestimated.

It is important to note that bias and confounding are not synonymous. Bias is caused by finding the wrong association from flawed information or subject selection. Confounding is factually correct with respect to the relationship found, but is incorrect in its interpretation due to an extraneous factor that is associated with both the exposure and outcome.

12.3.3. Controlling for Confounding Effects

There are three common methods to control for confounding effects. These are by matching, stratification, and modelling. They are described below ( Higgins & Green, 2011 ).

• Matching – The selection of subjects with similar characteristics so that they are comparable; the matching can be done at the individual subject level where each exposed subject is matched with one or more unexposed subjects as controls. It can also be done at the group level with equal numbers of exposed and unexposed subjects. Another way to match subjects is by propensity score, that is, a measure derived from a set of characteristics in the subjects. An example is the retrospective cohort study by Zhou, Leith, Li, and Tom (2015) to examine the association between caregiver phr use and healthcare utilization by pediatric patients. In that study, a propensity score-matching algorithm was used to match phr -registered children to non-registered children. The matching model used registration as the outcome variable and all child and caregiver characteristics as the independent variables.
• Stratification – Subjects are categorized into subgroups based on a set of characteristics such as age and sex then analyzed for the effect within each subgroup. An example is the retrospective cohort study by Staes et al. (2008) , examining the impact of computerized alerts on the quality of outpatient lab monitoring for transplant patients. In that study, the before/after comparison of the timeliness of reporting and clinician responses was stratified by the type of test (creatinine, cyclosporine A, and tacrolimus) and report source (hospital laboratory or other labs).
• Modelling – The use of statistical models to compute adjusted effects while accounting for relevant characteristics such as age and sex differences among subjects. An example is the retrospective cohort study by Beck and colleagues (2012) to compare documentation consistency and care plan improvement before and after the implementation of an electronic asthma-specific history and physical template. In that study, before/after group characteristics were compared for differences using t -tests for continuous variables and χ 2 statistics for categorical variables. Logistic regression was used to adjust for group differences in age, gender, insurance, albuterol use at admission, and previous hospitalization.

12.3.4. Adherence to Good Practices in Prospective Observational Studies

The ispor Good Research Practices Task Force published a set of recommendations in designing, conducting and reporting prospective observational studies for comparative effectiveness research ( Berger et al., 2012 ) that are relevant to eHealth evaluation. Their key recommendations are listed below.

• Key policy questions should be defined to allow inferences to be drawn.
• Hypothesis testing protocol design to include the hypothesis/questions, treatment groups and outcomes, measured and unmeasured confounders, primary analyses, and required sample size.
• Rationale for prospective observational study design over others (e.g., rct ) is based on question, feasibility, intervention characteristics and ability to answer the question versus cost and timeliness.
• Study design choice is able to address potential biases and confounders through the use of inception cohorts, multiple comparator groups, matching designs and unaffected outcomes.
• Explanation of study design and analytic choices is transparent.
• Study execution is carried out in ways that ensure relevance and reasonable follow-up is not different from the usual practice.
• Study registration takes place on publicly available sites prior to its initiation.

12.3.5. The Need for Reporting Consistency

Vandenbroucke et al. (2014) published an expanded version of the Strengthening the Reporting of Observational Studies in Epidemiology ( strobe ) statement to improve the reporting of observational studies that can be applied in eHealth evaluation. It is made up of 22 items, of which 18 are common to cohort, case-control and cross-sectional studies, with four being specific to each of the three designs. The 22 reporting items are listed below (for details refer to the cited reference).

• Title and abstract – one item that covers the type of design used, and a summary of what was done and found.
• Introduction – two items on study background/rationale, objectives and/or hypotheses.
• Methods – nine items on design, setting, participants, variables, data sources/measurement, bias, study size, quantitative variables and statistical methods used.
• Results – five items on participants, descriptive, outcome data, main results and other analyses.
• Discussion – four items on key results, limitations, interpretation and generalizability.
• Other information – one item on funding source.

The four items specific to study design relate to the reporting of participants, statistical methods, descriptive results and outcome data. They are briefly described below for the three types of designs.

• Cohort studies – Participant eligibility criteria and sources, methods of selection, follow-up and handling dropouts, description of follow-up time and duration, and number of outcome events or summary measures over time. For matched studies include matching criteria and number of exposed and unexposed subjects.
• Cross-sectional studies – Participant eligibility criteria, sources and methods of selection, analytical methods accounting for sampling strategy as needed, and number of outcome events or summary measures.
• Case-control studies – Participant eligibility criteria, sources and methods of case/control selection with rationale for choices, methods of matching cases/controls, and number of exposures by category or summary measures of exposures. For matched studies include matching criteria and number of controls per case.

12.4. Case Examples

12.4.1. cohort study of automated immunosuppressive care.

Park and colleagues (2010) conducted a retrospective cohort study to examine the association between the use of a cds (clinical decision support) system in post-liver transplant immunosuppressive care and the rates of rejection episode and drug toxicity. The study is summarized below.

• Setting – A liver transplant program in the United States that had implemented an automated cds system to manage immunosuppressive therapy for its post-liver transplant recipients after discharge. The system consolidated all clinical information to expedite immunosuppressive review, ordering, and follow-up with recipients. Prior to automation, a paper charting system was used that involved manually tracking lab tests, transcribing results into a paper spreadsheet, finding physicians to review results and orders, and contacting recipients to notify them of changes.
• Subjects – The study population included recipients of liver transplants between 2004 and 2008 who received outpatient immunosuppressive therapy that included tacrolimus medications.
• Design – A retrospective cohort study with a before/after design to compare recipients managed by the paper charting system against those managed by the cds system for up to one year after discharge.
• Measures – The outcome variables were the percentages of recipients with at least one rejection and/or tacrolimus toxicity episode during the one-year follow-up period. The independent variables included recipient, intraoperative, donor and postoperative characteristics, and use of paper charting or cds . Examples of recipient variables were age, gender, body mass index, presence of diabetes and hypertension, and pre-transplant lab results. Examples of intraoperative data were blood type match, type of transplant and volume of blood transfused. Examples of donor data included percentage of fat in the liver. Examples of post-transplantation data included the type of immunosuppressive induction therapy and the management method.
• Analysis – Mean, standard deviation and t -tests were computed for continuous variables after checking for normal distribution. Percentages and Fisher’s exact test were computed for categorical variables. Autoregressive integrated moving average analysis was done to determine change in outcomes over time. Logistic regression with variables thought to be clinically relevant was used to identify significant univariable and multivariable factors associated with the outcomes. P values of less than 0.05 were considered significant.
• Findings – Overall, the cds system was associated with significantly fewer episodes of rejection and tacrolimus toxicity. The integrated moving average analysis showed a significant decrease in outcome rates after the cds system was implemented compared with paper charting. Multivariable analysis showed the cds system had lower odds of a rejection episode than paper charting ( or 0.20; p < 0.01) and lower odds of tacrolimus toxicity ( or 0.5; p < 0.01). Other significant non-system related factors included the use of specific drugs, the percentage of fat in the donor liver and the volume of packed red cells transfused.

12.4.2. Cross-sectional Analysis of EHR Documentation and Care Quality

Linder, Schnipper, and Middleton (2012) conducted a cross-sectional study to examine the association between the type of ehr documentation used by physicians and the quality of care provided. The study is summarized below.

• Setting – An integrated primary care practice-based research network affiliated with an academic centre in the United States. The network uses an in-house ehr system with decision support for preventive services, chronic care management, and medication monitoring and alerts. The ehr data include problem and medication lists, coded allergies and lab tests.
• Subjects – Physicians and patients from 10 primary care practices that were part of an rct to examine the use of a decision support tool to manage patients with coronary artery disease and diabetes ( cad/DM ). Eligible patients were those with cad/DM in their ehr problem list prior to the rct start date.
• Design – A nine-month retrospective cross-sectional analysis of ehr data collected from the rct . Three physician documentation styles were defined based on 188,554 visit notes in the ehr : (a) dictation, (b) structured documentation, and (c) free text note. Physicians were divided into three groups based on their predominant style defined as more than 25% of their notes composed by a given method.
• Measures – The outcome variables were 15 ehr -based cad/DM quality measures assessed 30 days after primary care visits. They covered quality of documentation, medication use, lab testing, physiologic measures, and vaccinations. Measures collected prior to the day of visit were eligible and considered fulfilled with the presence of coded ehr data on vital signs, medications, allergies, problem lists, lab tests, and vaccinations. Independent variables on physicians and patients were included as covariates. For physicians, they included age, gender, training level, proportion of cad/DM patients in their panel, total patient visits, and self-reported experience with the ehr . For patients, they included socio-demographic factors, the number of clinic visits and hospitalizations, the number of problems and medications in the ehr , and whether their physician was in the intervention group.
• Analysis – Baseline characteristics of physicians and patients were compared using descriptive statistics. Continuous variables were compared using anova . For categorical variables, Fisher’s exact test was used for physician variables and χ 2 test for patient variables. Multivariate logistic regression models were used for each quality measure to adjust for patient and physician clustering and potential confounders. Bonferroni procedure was used to account for multiple comparisons for the 15 quality measures.
• Findings – During the study period, 234 physicians documented 18,569 visits from 7,000 cad/DM patients. Of these physicians, 146 (62%) typed free-text notes, 68 (25%) used structured documentation, and 20 (9%) dictated notes. After adjusting for cluster effect, physicians who dictated their notes had the worst quality of care in all 15 measures. In particular, physicians who dictated notes were significantly worse in three of 15 measures (antiplatelet medication, tobacco use, diabetic eye exam); physicians who used structured documentation were better in three measures (blood pressure, body mass, diabetic foot exam); and those who used free-text were better in one measure (influenza vaccination). In summary, physicians who dictated notes had worse quality of care than those with structured documentation.

12.4.3. Case-control Comparison of Internet Portal Use

Nielsen, Halamka, and Kinkel (2012) conducted a case-control study to evaluate whether there was an association between active Internet patient portal use by Multiple Sclerosis ( ms ) patients and medical resource utilization. Patient predictors and barriers to portal use were also identified. The study is summarized below.

• Setting – An academic ms centre in the United States with an in-house Internet patient portal site that was accessed by ms patients to schedule clinic appointments, request prescription refills and referrals, view test results, upload personal health information, and communicate with providers via secure e-mails.
• Subjects – 240 adult ms patients actively followed during 2008 and 2009 were randomly selected from the ehr ; 120 of these patients had submitted at least one message during that period and were defined as portal users. Another 120 patients who did not enrol in the portal or send any message were selected as non-users for comparison.
• Design – A retrospective case-control study facilitated through a chart review comparing portal users against non-users from the same period. Patient demographic and clinical information was extracted from the ehr , while portal usage, including feature access type and frequency and e-mail message content, were provided by it staff.
• Measures – Patient variables included age, gender, race, insurance type, employment status, number of medical problems, disease duration, psychiatric history, number of medications, and physical disability scores. Provider variables included prescription type and frequency. Portal usage variables included feature access type and frequency for test results, appointments, prescription requests and logins, and categorized messaging contents.
• Analysis – Comparison of patient demographic, clinical and medical resource utilization data from users and non-users were made using descriptive statistics, Wilcoxon rank sum test, Fisher’s exact test and χ 2 test. Multivariate logistic regression was used to identify patient predictors and barriers to portal use. Provider prescribing habits against patient’s psychiatric history and portal use were examined by two-way analysis of variance. All statistical tests used p value of 0.05 with no adjustment made for multiple comparisons. A logistic multivariate regression model was created to predict portal use based on patient demographics, clinical condition, socio-economic status, and physical disability metrics.
• Findings – Portal users were mostly young professionals with little physical disability. The most frequently used feature was secure patient-provider messaging, often for medication requests or refills, and self-reported side effects. Predictors and barriers of portal use were the number of medications prescribed ( or 1.69, p < 0.0001), Caucasian ethnicity ( or 5.04, p = 0.007), arm and hand disability ( or 0.23, p = 0.01), and impaired vision ( or 0.31, p = 0.01). For medical resource utilization, portal users had more frequent clinic visits, medication use and prescriptions from centre staff providers. Patients with a history of psychiatric disease were prescribed more ms medications than those without any history ( p < 0.0001). In summary, ms patients used the Internet more than the general population, but physical disability limited their access and need to be addressed.

12.4.4. Limitations

A general limitation of a correlational study is that it can determine association between exposure and outcomes but cannot predict causation. The more specific limitations of the three case examples cited by the authors are listed below.

• Automated immunosuppressive care – Baseline differences existed between groups with unknown effects; possible other unmeasured confounders; possible Hawthorne effects from focus on immunosuppressive care.
• ehr documentation and care quality – Small sample size; only three documentation styles were considered (e.g., scribe and voice recognition software were excluded) and unsure if they were stable during study period; quality measures specific to cad/DM conditions only; complex methods of adjusting for clustering and confounding that did not account for unmeasured confounders; the level of physician training (e.g., attending versus residents) not adjusted.
• Internet portal use – Small sample size not representative of the study population; referral centre site could over-represent complex patients requiring advanced care; all patients had health insurance.

12.5. Summary

In this chapter we described cohort, case-control and cross-sectional studies as three types of correlational studies used in eHealth evaluation. The methodological issues addressed include bias and confounding, controlling for confounders, adherence to good practices and consistency in reporting. Three case examples were included to show how eHealth correlational studies are done.

1 ISPOR – International Society for Pharmacoeconomics and Outcomes Research

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This publication is licensed under a Creative Commons License, Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0): see https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Lau F. Chapter 12 Methods for Correlational Studies. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Introduction
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• Methodological Considerations
• Case Examples

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Correlational Research | Guide, Design & Examples

Correlational Research | Guide, Design & Examples

Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.

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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.

Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

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Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

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Correlation Studies in Psychology Research

Determining the relationship between two or more variables.

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin

• Characteristics

Potential Pitfalls

Frequently asked questions.

A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.

A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.

• Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
• Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
• No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

Characteristics of a Correlational Study

Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.

When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.  

Can inspire ideas for further research

Option if lab experiment not available

Variables are viewed in natural setting

Can be time-consuming and expensive

Extraneous variables can't be controlled

No scientific control of variables

Subjects might behave differently if aware of being observed

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.   Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.  

Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.  

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a  random sample  of participants complete a survey, test, or questionnaire related to the variables of interest.   Random sampling is vital to the generalizability of a survey's results.

Cheap, easy, and fast

Can collect large amounts of data in a short amount of time

Results can be affected by poor survey questions

Results can be affected by unrepresentative sample

Outcomes can be affected by participants

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.  

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak.   Data is also affected by specific faults, such as unrepresented or underrepresented samples .

The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.

For example, in an experiment known as  "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).

Large amount of data

Can be less expensive

Researchers cannot change participant behavior

Can be unreliable

Information might be missing

No control over data collection methods

Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.

If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.

The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.

The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research. 

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Non-Experimental Research

29 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Methods

• Research Process
• Research Design & Method

Qualitative vs. Quantiative

Correlational vs. experimental, empirical vs. non-empirical.

• Survey Research
• Survey & Interview Data Analysis
• Resources for Research
• Ethical Considerations in Research

Qualitative Research gathers data about lived experiences, emotions or behaviors, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorized through statistical analysis. It assists with uncovering patterns or relationships, and for making generalizations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Correlational Research cannot determine causal relationships. Instead they examine relationships between variables.

Experimental Research can establish causal relationship and variables can be manipulated.

Empirical Studies are based on evidence. The data is collected through experimentation or observation.

Non-empirical Studies do not require researchers to collect first-hand data.

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AP® Psychology

Correlational study examples: ap® psychology crash course.

• The Albert Team
• Last Updated On: March 1, 2022

Do you remember what a correlational study is? Knowing the main types of psychology research is a key point for the Advanced Placement (AP) Psychology exam as it makes up for 8-10% of the content in the multiple choice and free response questions. However, understanding the characteristics, advantages and disadvantages of each research method is only half of mastering this subject. The other half is understanding in concrete and practical terms how the research methods have been applied to studies in different fields of psychology. In this AP® Psychology crash course review, we will see three correlational study examples that have contributed to the history of psychology, changing the way we perceive our nature, our personality, and our health.

Review: What is a Correlational Study and why is it Important?

Psychology is a science, and like any other, its knowledge must be scientifically obtained, verified and validated. For this, psychologists conduct three types of research:

• Experimental research – the most empirical type of research, where variables can be manipulated in laboratory conditions and different situations can be studied and compared to establish relations of cause and effect between variables.
• Clinical research – done through case studies under the premise that certain individual characteristics can be generalized to the rest of the population.
• Correlational research – seeks the relationship between two variables. The necessary data is gathered through surveys (questionnaires and interviews), archival research (past studies that present the data) and naturalistic observation (observation of the phenomena as they naturally happen, without intervening). The data is then statistically analyzed to verify the relationship between the variables.

The correlation between the variables is shown through a value that goes from -1.00 to +1.00. This value is called the correlational coefficient . When the correlational coefficient is close to +1.00, there is a positive correlation between the variables. In other words, an increase in X accompanies an increase in Y. When the correlational coefficient is close to -1.00, there is a negative correlation between the variables or an increase in X is followed by a decrease in Y. And when the correlational coefficient is close to 0.00 there is no relationship between the variables. The closer the value is to +1.00 or -1.00, the strongest the relationship is. We will see real examples of this later on this post.

Now, the most important thing to remember about correlational studies is that correlation does not imply causation . For example, let’s say that “marriage” has a negative correlation with “cancer,” meaning that people who are married are less likely to develop cancer throughout their lives than those who remain single. This doesn’t necessarily mean that one causes the other or that marriage directly avoids cancer. Maybe one variable does cause the other, but even if it does, in correlational studies it is not possible to determine the direction of causation or what is causing what. And it could also be that a third unknown variable is what causes the correlation. Keep this in mind as we see the correlational study examples.

You might be wondering: if correlational studies only show this – correlations – why are they important in the first place if you could just conduct an experiment manipulating the relevant variables and getting to more solid conclusions?

Indeed, the disadvantages of correlational studies are that they cannot establish causal relationships nor direction of causal influence, there is no control of the variables, they don’t explain behavior, and they could result in illusory correlations. Illusory correlation is when there is a perceived relationship between variables that does not exist, like “a higher ice cream consumption leads to higher crime rate.”

On the other hand, one of the main advantages of a correlational study is that it is a useful way to describe and analyze data especially in cases where experimental research would lead to ethical issues. Take for instance a research that aims to investigate the relationship between child abuse and coping abilities later in adulthood. You obviously can’t take a random group of healthy children and expose them to abusive or traumatic situations to compare it with a control group. In the earlier stages of psychology, researchers could get away with teaching a phobia to a baby or leading participants to think they had electrocuted someone to death and get away with it in the name of science. Such practices are no longer acceptable, and correlational studies play an important role in developing knowledge in psychology.

Other advantages are that correlational studies are usually less expensive and easier to conduct than experiments and they allow for general predictions. They can also represent the first steps in a new field of research, leading to further studies and advances.

Now that you’ve reviewed the main concepts of correlational studies and why they matter, let’s see three important research examples in different fields of psychology and understand how all of this comes to life!

Study #1: Biological Basis of Behavior – A Debate on Nature Versus Nurture

We can easily think of how our genetics influence physical traits like height, hair and eye color. But have you ever considered that your genetics might also play a big role on psychological traits like personality and interests? In 1990 psychologists Thomas Bouchard, David Lykken, and their associates investigated the influence our genes have in psychological attributes. This was hard research to accept at the time considering that for the past fifty years, psychology was mainly focused on behaviorism and how the environment determines behavior. Bouchard and Lykken’s study brought the debate of nature versus nurture back to the spotlight, determined to clarify the genes’ and the environment’s role in who we are.

For this, Bouchard and Lykken conducted a study with monozygotic twins (identical twins) who had been separated at birth and raised in different environments and compared the results with identical twins who had been raised together. Note that this is a study in which one couldn’t simply replicate the situation in laboratory conditions, so a correlational study was the best way to analyze the data of real individuals in this situation.

Bouchard and Lykken gathered a huge amount of data from each pair of twins. They used a variety of personality trait scales, aptitude and occupational interest inventories, intelligence tests, family environment scales and interviews. At the end of the first part of the research, Bouchard and Lykken had information concerning the twins’ physiological traits, intelligence, personality, psychological interests and social attitudes. Next, Bouchard and Lykken analyzed the correlation between the twins in all these fields.

The results were surprising. If the environment were responsible for individual differences, identical twins reared together should be more similar than identical twins reared apart. However, that was not what the results showed. Both categories of twins had a very similar correlational coefficient that neared +1.00. This means that regardless of having being raised in the same or different environments, each person was very similar to his twin in all traits.

Based on this we can say that genetic factors strongly influence human behavior in a variety of ways, both physiological and psychological. This could be seen as a problematic conclusion since we like to put so much importance on environmental factors like education and parenting as if that alone determined who we grow to be, what interests we develop, what careers we choose and so on. However, it is not the case for giving up on all our efforts in life thinking that eventually the genes will just take over and determine our fate.

Bouchard and Lykken emphasize that although intelligence is mainly determined by genetic factors, it can still be enhanced by experiences. Approximately 70% of intelligence is genetically determined, which means there is still 30% that can be worked on or ignored in the environment, either at home with parents or at school with teachers and mentors.

The same can be applied to the other traits. For example, even if your genes hold a natural strength towards communication skills, none of it will matter if you don’t get an opportunity in your environment to make that skill emerge and develop. Recent research on identical twins shows that the older the twins, the more similar they are. Another way to say this is that the more experiences you have, the more your genes can be expressed.

As human beings, we are determined by a combination of genetic and environmental influences. We are nature and nurture. Genes don’t mean destiny, but that doesn’t mean we can ignore their influences on our physiological and psychological characteristics. Let’s truly understand the components of our behavior and overcome the genes versus environment dichotomy.

Study #2: Personality – Who is in Control of Your Life?

Do you think your actions are what matter the most for the outcome of your life? Or do you think that external forces like fate and luck have a major influence in the paths you take? This kind of personal belief, called  locus of control , is associated with all sorts of behaviors we show in different areas of life. The locus of control and its influence on behavior was first studied by the influential psychologist and behaviorist  Julian Rotter in 1966.

Rotter proposed that the way individuals interpret what happens to them and where they put the responsibility for the events in their lives is an important part of the personality that can be used to predict tendencies in certain behaviors. When a person attributes the consequences of their behavior to factors such as luck, fate, and other greater forces, this person believes in an external locus of control . On the other hand, a person that identifies the consequences of her behavior to her own actions believes in an  internal locus of control .

To measure locus of control, Rotter developed a scale called I-E Scale, where “I” stands for “Internal” and “E” for “External.” The scale contains many pairs of statements, and the participant must choose the one that best fits his beliefs. A few examples of the pair of statements are “Many of the unhappy things in people’s lives are partly due to bad luck” versus “People’s misfortunes result from the mistakes they make,” and “Becoming a success is a matter of hard work; luck has little or nothing to do with it” versus “Getting a good job depends mainly on being in the right place at the right time.”

After measuring the locus of control of a relevant quantity of participants, Rotter analyzed the correlation between internal or external locus of control and behaviors such as gambling, persuasion, smoking and achievement motivation. His findings demonstrated that:

• External individuals are more likely to gamble on risky bets while internal individuals prefer “sure things” and moderate odds on the long run.

• Internal individuals are more efficient on persuading peers to change their attitudes and more resistant to manipulation than external individuals.

• Because an internal locus of control is related to self-control, smokers tend to be significantly more external oriented. Those who successfully quit smoking are more internally oriented.

• Internal individuals are more motivated to achieve success than those who believe their lives are ruled by forces outside of their control. Examples of achievements included plans to attend college and time spent on homework.

So translating into terms of correlational studies, there was, for example, a strong correlation between “internal locus of control” and “achievement motivation,” as the correlation coefficient between these two variables neared +1.00.

Furthermore, Rotter identified three sources for the development of an external or internal locus of control: cultural differences, socioeconomic differences, and parenting style. In conclusion, Rotter proposed that locus of control is an important component of personality that explains the differences in behavior between two people who are faced with the same situation. This belief determines the way we interpret the consequences of our behavior and influences the actions we take in our lives.

Study #3: Motivation and Emotion – The Effects of Stress on Our Health

Nowadays it’s almost common sense that stress has an impact on our health, but this was not always an easily accepted idea. In 1967, Thomas Holmes and Richard Rahe  studied the correlation between stress and illness. This was a psychosomatic  research because it studied the connection between psychological factors and physical problems.

Since it wouldn’t be ethical to put people under stressful situations to study whether or not they developed more health problems than a comfortable control group, this research was made using the correlational method. First, Holmes and Rahe designed a scale to measure stress in a variety of life situations, which included both happy and unhappy events, like Christmas and death of a spouse. This was because, according to Holmes and Rahe, stress happens in any situation where there is a need for psychological readjustment. This scale was called the Social Readjustment Rating Scale (SRRS). After having a huge amount of participants answer the scale, Holmes and Rahe studied the correlations between high levels of stress and illnesses.

As you may have already predicted, a strong positive correlation between stress and illness was found. The participants who had had a low level of stress in the past six months reported an average of 1.4 illnesses for the same period. A medium level of stress had an average of 1.9 illnesses and a high level of stress, 2.1 illnesses.

However, we also know that stress is only one component that influences health, and the connection between stress and illness is way more complex than a correlational study can show. Aware of that, Holmes and Rahe cited other factors that must be taken into consideration to help predict psychosomatic problems. They are:

• Your experience with stressful events

• Your coping skills

• The strength of your immune system

• Your way of dealing with health problems when they occur

Psychologists and doctors now recognize that the vast majority of illnesses are influenced by psychological factors, either at their development or in the way they are treated. This puts an end to Descartes’ classical view of split mind and body. Humans are complex beings, who must be understood and treated in their wholeness for an efficient prevention of illness and promotion of health.

So what do you think of each of these correlational study examples? They are in different areas of psychology (Biological Bases of Behavior, Personality, and Motivation and Emotion), so you can encounter this type of research in many questions of the AP® Psychology exam. How do you understand the influence of genetics on your behavior? Is your locus of control more internal or external? What examples of psychosomatic problems have you seen in your day to day experience? Share in the comments below!

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1.10: Correlational and Experimental Research

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Learning Outcomes

• Explain correlational research
• Describe the value of experimental research

Correlational Research

When scientists passively observe and measure phenomena it is called correlational research . Here, researchers do not intervene and change behavior, as they do in experiments. In correlational research, the goal is to identify patterns of relationships, but not cause and effect. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

So, what if you wanted to test whether spending money on others is related to happiness, but you don’t have $20 to give to each participant in order to have them spend it for your experiment? You could use a correlational design—which is exactly what Professor Elizabeth Dunn (2008) at the University of British Columbia did when she conducted research on spending and happiness. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.

Understanding Correlation

To find out how well two variables correlate, you can plot the relationship between the two scores on what is known as a scatterplot . In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month (x-axis) and how happy the person felt in the past month (y-axis). Which variable is plotted on which axis does not matter.

The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as r). A correlation coefficient provides information about the direction and strength of the association between two variables. For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.

With a positive correlation , the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The r value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the r value is .81.

A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 2 shows the association between the average height of males in a country (y-axis) and the pathogen prevalence (or commonness of disease; x-axis) of that country. In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus (–) sign in front of it. Here, it is –.83.

The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness; the more money people reported spending on others, the happier they reported to be. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable! Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.

The r value of a strong correlation will have a high absolute value (a perfect correlation has an absolute value of the whole number one, or 1.00). In other words, you disregard whether there is a negative sign in front of the r value, and just consider the size of the numerical value itself. If the absolute value is large, it is a strong correlation. A weak correlation is one in which the two variables correspond some of the time, but not most of the time. Figure 3 shows the relation between valuing happiness and grade point average (GPA). People who valued happiness more tended to earn slightly lower grades, but there were lots of exceptions to this. The r value for a weak correlation will have a low absolute value. If two variables are so weakly related as to be unrelated, we say they are uncorrelated, and the r value will be zero or very close to zero. In the previous example, is the correlation between height and pathogen prevalence strong? Compared to Figure 3, the dots in Figure 2 are tighter and less dispersed. The absolute value of –.83 is large (closer to one than to zero). Therefore, it is a strong negative correlation.

Problems with correlation

If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can’t be certain. For example, in the first case, it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists.

In this video, University of Pennsylvania psychologist and bestselling author, Angela Duckworth describes the correlational research that informed her understanding of grit.

A TED element has been excluded from this version of the text. You can view it online here: http://pb.libretexts.org/lsdm/?p=66

You can view the transcript for “Grit: The power of passion and perseverance | Angela Lee Duckworth” here (opens in new window) .

link to learning

Click through this interactive presentation to examine actual research studies.

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Experimental Research

Experiments are designed to test hypotheses (or specific statements about the relationship between variables ) in a controlled setting in efforts to explain how certain factors or events produce outcomes. A variable is anything that changes in value. Concepts are operationalized or transformed into variables in research which means that the researcher must specify exactly what is going to be measured in the study. For example, if we are interested in studying marital satisfaction, we have to specify what marital satisfaction really means or what we are going to use as an indicator of marital satisfaction. What is something measurable that would indicate some level of marital satisfaction? Would it be the amount of time couples spend together each day? Or eye contact during a discussion about money? Or maybe a subject’s score on a marital satisfaction scale? Each of these is measurable but these may not be equally valid or accurate indicators of marital satisfaction. What do you think? These are the kinds of considerations researchers must make when working through the design.

The experimental method is the only research method that can measure cause and effect relationships between variables. Three conditions must be met in order to establish cause and effect. Experimental designs are useful in meeting these conditions:

• The independent and dependent variables must be related. In other words, when one is altered, the other changes in response. The independent variable is something altered or introduced by the researcher; sometimes thought of as the treatment or intervention. The dependent variable is the outcome or the factor affected by the introduction of the independent variable; the dependent variable depends on the independent variable. For example, if we are looking at the impact of exercise on stress levels, the independent variable would be exercise; the dependent variable would be stress.
• The cause must come before the effect. Experiments measure subjects on the dependent variable before exposing them to the independent variable (establishing a baseline). So we would measure the subjects’ level of stress before introducing exercise and then again after the exercise to see if there has been a change in stress levels. (Observational and survey research does not always allow us to look at the timing of these events which makes understanding causality problematic with these methods.)
• The cause must be isolated. The researcher must ensure that no outside, perhaps unknown variables, are actually causing the effect we see. The experimental design helps make this possible. In an experiment, we would make sure that our subjects’ diets were held constant throughout the exercise program. Otherwise, the diet might really be creating a change in stress level rather than exercise.

A basic experimental design involves beginning with a sample (or subset of a population) and randomly assigning subjects to one of two groups: the experimental group or the control group . Ideally, to prevent bias, the participants would be blind to their condition (not aware of which group they are in) and the researchers would also be blind to each participant’s condition (referred to as “ double blind “). The experimental group is the group that is going to be exposed to an independent variable or condition the researcher is introducing as a potential cause of an event. The control group is going to be used for comparison and is going to have the same experience as the experimental group but will not be exposed to the independent variable. This helps address the placebo effect, which is that a group may expect changes to happen just by participating. After exposing the experimental group to the independent variable, the two groups are measured again to see if a change has occurred. If so, we are in a better position to suggest that the independent variable caused the change in the dependent variable . The basic experimental model looks like this:

The major advantage of the experimental design is that of helping to establish cause and effect relationships. A disadvantage of this design is the difficulty of translating much of what concerns us about human behavior into a laboratory setting.

Link to Learning

Have you ever wondered why people make decisions that seem to be in opposition to their longterm best interest? In Eldar Shafir’s TED Talk Living Under Scarcity , Shafir describes a series of experiments that shed light on how scarcity (real or perceived) affects our decisions.

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[glossary-page] [glossary-term]control group:[/glossary-term] [glossary-definition]a comparison group that is equivalent to the experimental group, but is not given the independent variable[/glossary-definition]

[glossary-term]correlation:[/glossary-term] [glossary-definition]the relationship between two or more variables; when two variables are correlated, one variable changes as the other does[/glossary-definition]

[glossary-term]correlation coefficient:[/glossary-term] [glossary-definition]number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r[/glossary-definition]

[glossary-term]correlational research:[/glossary-term] [glossary-definition]research design with the goal of identifying patterns of relationships, but not cause and effect[/glossary-definition]

[glossary-term]dependent variable:[/glossary-term] [glossary-definition]the outcome or variable that is supposedly affected by the independent variable[/glossary-definition]

[glossary-term]double-blind:[/glossary-term] [glossary-definition]a research design in which neither the participants nor the researchers know whether an individual is assigned to the experimental group or the control group[/glossary-definition]

[glossary-term]experimental group:[/glossary-term] [glossary-definition]the group of participants in an experiment who receive the independent variable[/glossary-definition]

[glossary-term]experiments:[/glossary-term] [glossary-definition]designed to test hypotheses in a controlled setting in efforts to explain how certain factors or events produce outcomes; the only research method that measures cause and effect relationships between variables[/glossary-definition]

[glossary-term]hypotheses:[/glossary-term] [glossary-definition]specific statements or predictions about the relationship between variables[/glossary-definition]

[glossary-term]independent variable:[/glossary-term] [glossary-definition]something that is manipulated or introduced by the researcher to the experimental group; treatment or intervention[/glossary-definition]

[glossary-term]negative correlation:[/glossary-term] [glossary-definition]two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation[/glossary-definition]

[glossary-term]operationalized:[/glossary-term] [glossary-definition]concepts transformed into variables that can be measured in research[/glossary-definition]

[glossary-term]positive correlation:[/glossary-term] [glossary-definition]two variables change in the same direction, both becoming either larger or smaller[/glossary-definition]

[glossary-term]scatterplot:[/glossary-term] [glossary-definition]a plot or mathematical diagram consisting of data points that represent two variables[/glossary-definition]

[glossary-term]variables:[/glossary-term] [glossary-definition]factors that change in value[/glossary-definition] [/glossary-page]

Contributors and Attributions

• Modification, adaptation, and original content. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Psyc 200 Lifespan Psychology. Authored by : Laura Overstreet. Located at : http://opencourselibrary.org/econ-201/ . License : CC BY: Attribution
• Research Designs. Authored by : Christie Napa Scollon. Provided by : Singapore Management University. Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Vocabulary and review about correlational research. Provided by : Lumen Learning. Located at : https://courses.lumenlearning.com/waymaker-psychology/wp-admin/post.php?post=1848&action=edit . License : CC BY: Attribution
• Grit: The power of passion and perseverance. Authored by : Angela Lee Duckworth. Provided by : TED. Located at : https://www.ted.com/talks/angela_lee_duckworth_grit_the_power_of_passion_and_perseverance . License : CC BY-NC-ND: Attribution-NonCommercial-NoDerivatives

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Descriptive Correlational: Descriptive vs Correlational Research

Descriptive research and Correlational research are two important types of research studies that help researchers make ambitious and measured decisions in their respective fields. Both descriptive research and correlational research are used in descriptive correlational research. 

Descriptive research is defined as a research method that involves observing behavior to describe attributes objectively and systematically. A descriptive research project seeks to comprehend phenomena or groups in depth.

Correlational research , on the other hand, is a method that describes and predicts how variables are naturally related in the real world without the researcher attempting to alter them or assign causation between them.

The main objective of descriptive research is to create a snapshot of the current state of affairs, whereas correlational research helps in comparing two or more entities or variables.

What is descriptive correlational research?

Descriptive correlational research is a type of research design that tries to explain the relationship between two or more variables without making any claims about cause and effect. It includes collecting and analyzing data on at least two variables to see if there is a link between them. 

In descriptive correlational research, researchers collect data to explain the variables of interest and figure out how they relate. The main goal is to give a full account of the variables and how they are related without changing them or assuming that one thing causes another.

In descriptive correlational research, researchers do not change any variables or try to find cause-and-effect connections. Instead, they just watch and measure the variables of interest and then look at the patterns and relationships that emerge from the data.

Experimental research involves the independent variable to see how it affects the dependent variable, while descriptive correlational research just describes the relationship between variables. 

In descriptive correlational research, correlational research designs measure the magnitude and direction of the relationship between two or more variables, revealing their associations. At the outset creating initial equivalence between the groups or variables being compared is essential in descriptive correlational research

The independent variable occurs prior to the measurement of the measured dependent variable in descriptive correlational research. Its goal is to explain the traits or actions of a certain population or group and look at the connections between independent and dependent variables.

How are descriptive research and correlational research carried out?

Descriptive research is carried out using three methods, namely:  

• Case studies – Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they don’t have the capacity to make accurate predictions.
• Surveys – A survey is a set of questions that is administered to a population, also known as respondents. Surveys are a popular market research tool that helps collect meaningful insights from the respondents. To gather good quality data, a survey should have good survey questions, which should be a balanced mix of open-ended and close-ended questions .
• Naturalistic Observation – Naturalistic observations are carried out in the natural environment without disturbing the person/ object in observation. It is much like taking notes about people in a supermarket without letting them know. This leads to a greater validity of collected data because people are unaware they are being observed here. This tends to bring out their natural characteristics.

Correlational research also uses naturalistic observation to collect data. However, in addition, it uses archival data to gather information. Archival data is collected from previously conducted research of a similar nature. Archival data is collected through primary research.

In contrast to naturalistic observation, information collected through archived is straightforward. For example, counting the number of people named Jacinda in the United States using their social security number.  

Descriptive Research vs Correlational Research

Features of Descriptive Correlational Research

The key features of descriptive correlational research include the following:

01. Description

The main goal, just like with descriptive research, is to describe the variables of interest thoroughly. Researchers aim to explain a certain group or event’s traits, behaviors, or attitudes. 

02. Relationships

Like correlational research, descriptive correlational research looks at how two or more factors are related. It looks at how variables are connected to each other, such as how they change over time or how they are linked.

03. Quantitative analysis

Most methods for analyzing quantitative analysis data are used in descriptive correlational research. Researchers use statistical methods to study and measure the size and direction of relationships between variables.

04. No manipulation

As with correlational research, the researcher does not change or control the variables. The data is taken in its natural environment without any changes or interference.

05. Cross-sectional or longitudinal

Cross-sectional or longitudinal designs can be used for descriptive correlational research. It collects data at one point in time, while longitudinal research collects data over a long period of time to look at changes and relationships over time. 

Examples of descriptive correlational research

For example, descriptive correlational research could look at the link between a person’s age and how much money they make. The researcher would take a sample of people’s ages and incomes and then look at the data to see if there is a link between the two factors.

• Example 1 : A research project is done to find out if there is a link between how long college students sleep and how well they do in school. They keep track of how many hours kids sleep each night and what their GPAs are. By studying the data, the researcher can describe how the students sleep and find out if there is a link between how long they sleep and how well they do in school.
• Example 2 : A researcher wants to know how people’s exercise habits affect their physical health if they are between the ages of 40 and 60. They take notes on things like how often and how hard you work out, your body mass index (BMI), blood pressure, and cholesterol numbers. By analyzing the data, the researcher can describe the participants’ exercise habits and physical health and look for any links between these factors.
• Example 3 : Let’s say a researcher wants to find out if college students who work out feel less stressed. Using a poll, the researcher finds out how many hours students spend exercising each week and how stressed they feel. By looking at the data, the researcher may find that there is a moderate negative correlation between exercise and stress levels. This means that as exercise grows, stress levels tend to go down. 

Descriptive correlational research is a good way to learn about the characteristics of a population or group and the relationships between its different parts. It lets researchers describe variables in detail and look into their relationships without suggesting that one variable caused another. 

Descriptive correlational research gives useful insights and can be used as a starting point for more research or to come up with hypotheses. It’s important to be aware of the problems with this type of study, such as the fact that it can’t show cause and effect and relies on cross-sectional data. 

Still, descriptive correlational research helps us understand things and makes making decisions in many areas easier.

QuestionPro is a very useful tool for descriptive correlational research. Its many features and easy-to-use interface help researchers collect and study data quickly, giving them a better understanding of the characteristics and relationships between variables in a certain population or group. 

The different kinds of questions, analytical research tools, and reporting features on the software improve the research process and help researchers come up with useful results. QuestionPro makes it easier to do descriptive correlational research, which makes it a useful tool for learning important things and making decisions in many fields.

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Exploring Molecular Dynamics with Raster Image Correlation Spectroscopy (RICS)

In this series "From Image to Results", explore various case studies explaining how to reach results from your demanding samples and acquired images in an efficient way. For each case study, we highlight different samples, imaging systems, and research questions.

In this case study, we explore in vitro molecular dynamics with Raster Image Correlation Spectroscopy (RICS).

Key Learnings:

• Understand how RICS can be used to measure diffusion coefficients.
• Explore the effects of protein size on diffusion speed.
• Learn how RICS can allow you to visualize localized diffusion speeds within an image.

Case Study Overview

Introduction.

Figure 1 : Extracting molecular dynamics such as diffusion and concentration from intensity-based images is key in understanding how proteins behave inside cellular environments.

Molecular dynamics of proteins describe their movement, speed, and diffusion patterns. For life scientists and drug developers, analyzing molecular dynamics can bring insights to understanding cell signaling pathways, membrane dynamics, protein-protein interactions, enzyme function, transport processes, cellular organization, and disease mechanisms. The rate of protein diffusion can be influenced by many different factors, including protein size, temperature, concentration gradient, solvent viscosity, and protein charge. In a cellular environment, factors such as molecular crowding, cytoplasmic streaming, binding to other molecules, post-translational modifications, and membrane compartmentalization can also impact protein diffusion. Understanding the interplay between these factors and their effects on protein diffusion is essential for understanding how the protein behaves. In this case study, we will discuss how protein size affects the diffusion of a protein in cells.

Multiple techniques used to study molecular dynamics are centered around the principle of monitoring the movement of fluorescent molecules in and out of the imaging volume. One very interesting technology in this category is Raster Image Correlation Spectroscopy (RICS) .

RICS leverages the mechanics of the laser scanning confocal, which scans a laser beam across the sample, typically in a raster pattern, and measures the fluorescence intensity at each point. The intensity data is then analyzed to calculate the spatiotemporal correlation function, which describes the correlation between the fluorescence signals at different points in space and time. The spatiotemporal correlation function is calculated by taking the autocorrelation of the fluorescence intensity at each point in the image, and then averaging the autocorrelation functions over all points in the image. The result is a 2D correlation function that describes the spatial and temporal correlation of the fluorescence signals.

To dissect the spatiotemporal correlation further, the spatial correlation function describes the likelihood of finding fluorescent molecules at a certain distance from their current location, extracting the information from the known speed and direction of movement of the laser on the sample. The temporal correlation function describes the correlation between the fluorescence signals at different times, and it is used to estimate the diffusion time of the fluorescent molecules. By analyzing the spatiotemporal correlation function, RICS can extract the diffusion properties of the fluorescently labeled molecules, including the diffusion coefficient. The shape of the correlation function indicates the degree of molecular mobility, with faster diffusing molecules exhibiting little correlation along the slow scanning axis, and slower diffusing molecules exhibiting more correlation. The maximal amplitude of the correlation function indicates the concentration of the molecules. 

What makes RICS particularly useful for studying molecular dynamics is its ability to analyze large numbers of molecules simultaneously. This allows scientists to observe the behavior of complex systems, such as biological membranes or cellular compartments with multiple components. RICS is also a relatively fast and easy technique to implement for users with a ZEISS laser scanning confocal, making it accessible to researchers in a wide range of fields.

Material and Methods

Figure 2 : LSM 980 system and Spectral RICS module.

For these experiments, U2OS cells were transfected with either 1xGFP, 2xGFP, 3xGFP, or 4xGFP oligomers. The cells were imaged the day after transfection with the ZEISS LSM 980 laser scanning confocal using the solid state 488 nm laser to excite GFP at 1 % laser power. For the RICS acquisition, settings with a pixel size of 50 nm and frame speed of 8.19 μs were used, with a time series of 100 frames (unless explicitly mentioned otherwise). The ZEISS QUASAR detector was used in photon counting mode for the measurements comparing diffusion across the GFP oligomers. Ten cells were imaged for each GFP oligomer. The same cells were imaged with the detector in photon counting and integration mode (5 cells per condition were imaged).

Figure 3 : Single images from the GFP oligomer expression in cells. Nu, Nucleoplasm; No, Nucleolous; Cy, Cytoplasm

1) Effects of Protein Size on Diffusion Coefficient

To investigate the effects of protein size on diffusion, GFP oligomers of various sizes were transfected into cells. When looking at the intensity images of the different GFP oligomers (Figure 3), they appear very similar, with progressively higher definition of the nuclear and nucleolar substructures as the GFP number increases in size. To extract quantitative information about the behavior of GFP, timelapses were analyzed with the ZEISS Spectral RICS module. This software addition for ZEISS LSM 980 allows data acquisition and analysis to map mobility, concentration, and stoichiometry of fluorescently labeled diffusing molecules in vitro and in living cells as well as interactions between differently labeled molecules.

Figure 4 : RICS analysis of monomeric GFP. A&B: Average spatial correlation function, with each spatial correlation function calculated separately from each image in the experimental image series (A = top view, B = side view). C&D: Data graphs depicting the fit model. C: Surface plot depicting the fit model color-coded for weighted residuals. D: The lower plot depicts the fit model (solid lines), with the parameters optimized to fit the experimental data (error bars). The upper plot shows the weighted residuals.

Figure 4 depicts the average spatial correlation function, with each spatial correlation function calculated separately from each image in the experimental image series. The purpose of the RICS spatial correlation function is to inform the user what the likelihood is to find molecules at a distance (ξ, ψ) from their current location, given their diffusion constant and given the fact that the laser is imaging the molecules by raster scanning over them with a given scan speed along a line and between lines. The center correlation point (at ξ = 0 and ψ = 0; Figure 4A) depicts the maximal amplitude of the autocorrelation function, N (Figure 4B). The ξ axis is the fast-scanning axis of the confocal microscope along a line. The ψ axis is the slow scanning axis of the microscope as it scans line after line.

The spatial correlation function surface plot (Figure 4A) provides direct information on the molecular mobility. Fast diffusing molecules will exhibit little spatial correlation along the ψ axis, while slower diffusing molecules exhibit more correlation along the ψ axis. An ideal RICS correlation has correlation both in ξ and ψ, yet more pronounced correlation in ξ than in ψ. This visually translates to faster movements being displayed as an asymmetric, line-like correlation, whereas slower movements as a symmetric, circular correlation function (equal correlation in ξ and ψ). If the correlation is a perfect circle, then molecules do not diffuse significantly within one image scan.

The maximal amplitude of the correlation function (indicated in red, peak of the surface plot, Figure 4B) defines the number (N) of fluorescent molecules in the confocal volume, from which the concentration of the diffusing molecule can be assessed by N = 1/G. The larger the amplitude, the lower the concentration. The standard deviation on the correlation function points that surround the actual correlation (the blue carpet, Figure 4A&B) informs on the quality of the RICS data. The lower the standard deviation (more homogeneously blue and smooth carpet), the better the data.

RICS analysis fits the experimental data to a predefined model to extract the diffusion coefficient (D), and the number of molecules (N). During RICS analysis, for each data point of this average correlation function, the standard deviation is also calculated to allow for error-weighted analysis. In Figure 4D (lower graph), two sections of the experimental RICS data (error bars are the standard deviation) and fit model (solid lines) are shown: the blue section along the fast-scanning axis, G(ξ ,0), and the red section along the orthogonal slow scanning axis, G(0,ψ). In the top part of Figure 4C, the weighted residuals are shown, which are the difference between the data and fit model, divided by the error.

Visual inspection of the fit results allows qualitative assessment of the goodness-of-fit: In Figure 4C, color scaling depicts the weighted residuals. A more intense blue or red color indicates that the fit model value at a given location of the spatial correlation is lower or higher, respectively, than the experimental correlation data value at that location. The absence of any color for a given fit model in the left graph illustrates a perfect fit, i.e., weighted residuals equal to zero. The lower the error on the experimental data, the better the fit model needs to fit the experimental correlation data to lead to a small, weighted residual.

Figure 6: Plot and table of diffusion coefficients (D) for the different GFP oligomers. Unpaired Student’s t-test was used to compare the average D between 1xGFP and 2xGFP, 2xGFP and 3xGFP, 3xGFP and 4xGFP. The fold and percentage decrease were calculated to express the degree of decrease in D relative to the smaller GFP oligomer. N=10.

To quantify this qualitative observation, the diffusion coefficient (D) for the GFP oligomers is plotted with a standard t-Test to compare D between the different GFP oligomers. The statistical analysis reveals that there is a significant decrease in the diffusion coefficient of GFP after each size increase. The decrease in the diffusion coefficient is proportional to the number of oligomers added, with D decreasing by 66-78 % each time an additional oligomer is added.

Figure 7:  Plot and table of diffusion coefficients (D) for the different GFP oligomers for the same cells imaged in Photon Counting and Integration mode. Student’s t-test was used to compare the average D value between Photon Counting and Integration mode. There is no statistically significant difference in D between the two imaging modes.

2) Photon Counting vs. Integration Mode Effects on Diffusion Coefficients

When imaging with a laser scanning microscope, the signal is collected by the detector. The detector can be operated in either photon counting or integration mode. The choice between the two modes depends on the specific experimental requirements and the characteristics of the sample being studied.

Photon counting mode is typically used when the sample is relatively dim, and the signal-to-noise ratio needs to be optimized. In photon counting mode, each detected photon is precisely counted, which can be used to calculate the diffusion coefficient of the fluorescent particles in the sample. Photon counting mode is also useful for detecting low levels of fluorescence, as the sensitivity of the detector is typically higher than in integration mode.

Conversely, integration mode has a higher dynamic range, making it the preferred choice for brighter samples. In integration mode, the detector integrates the signal from each pixel over a certain period of time, resulting in a continuous output signal that represents the average intensity of the fluorescent particles in the sample. Integration mode is useful for measuring the distribution and concentration of fluorescent particles in the sample, as it provides a more accurate measure of the average intensity of the fluorescence signal.

To investigate if the mode of detection affects the calculated diffusion coefficient, the same cells were imaged with ZEISS Spectral RICS in both photon counting and integration mode. The RICS analysis revealed that there is no significant difference in the diffusion coefficient calculated regardless of the mode of acquisition. This provides evidence that either mode of the detector can be used to accurately characterize protein behavior.

Figure 8:  Grid-based Heatmap. A. Intensity image of timepoint 0 in the RICS timelapse. Nucleoli are indicated by arrows, ER is indicated by an asterisk. B. Grid-based heatmap, 32x32 sectors. The color bars indicate the range of diffusion coefficient values. Grid elements which cannot be analyzed due to the lack of usable data are shown in black.

3) Diffusion Across Subcellular Compartments

Previously, we showed how the number of GFP oligomers affects the diffusion coefficient of the protein, however, cells are not homogeneous environments. Cells are composed of compartmentalized and compact structures with barriers that restrict protein diffusion. The diffusion coefficient discussed so far represent the average speed across the entire image, which covers a large part of the cell.

One of the greatest advantages of using RICS is that it can provide positional information along with the molecular dynamics information by performing Grid-based heatmaps. In this type of analysis, the image is divided into small sectors and RICS analysis is performed consecutively in these sectors. The resulting parameters are displayed in heatmaps overlayed on the original image. Grid-heatmapping requires a high signal-to-noise ratio, therefore it is necessary to select brighter cells, image in integration mode, and acquire longer timeframes.

The cells expressing 4xGFP were selected for this experiment since subcellular structures are more pronounced due to the size of the protein hindering diffusion. A bright cell was selected and imaged in integration mode for 300 frames. As seen in Figure 8 (left), the nucleus, cytoplasm, nucleoli (labelled with arrows) and endoplasmic reticulum (ER- labelled with asterisks), can be easily visualized in the intensity image. After the analysis, the grid-based heatmap (32x32 sectors; Figure 8, right) shows how the GFP diffusion varies throughout the cell. The heatmap is color-coded for diffusion coefficient, with red and blue representing faster and slower diffusion coefficient, respectively. The heatmap reveals that GFP is diffusing slower inside the nucleoli (darker blue color) compared to the nucleoplasm (lighter blue). Additionally, the diffusion in some areas of the nucleoplasm is faster (red) compared to areas of the cytoplasm (blue). Grid elements that could not be analyzed due to lack of usable data are shown in black, which in this case coincides with the ER (asterisk). One reason for this may be the constant movement of the ER which doesn’t allow for spatial cross correlation analysis. Other parameters, such as the number of molecules and concentration, can also be analyzed with grid-based heatmaps, allowing visualization of the parameter of interest in different cellular compartments.

This case study discusses how RICS can uncover molecular dynamics, specifically diffusion coefficient, in cells, and how it can measure the effects that protein size has on its diffusion coefficient. The RICS analysis revealed that the diffusion coefficient decreases with increasing GFP oligomer size, with D decreasing by 22-34 % each time a GFP monomer is added. Additionally, this case study demonstrates how grid-based heatmaps can allow visualization of diffusion coefficient variation throughout cellular compartments.

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Raster Imaging Correlation Spectroscopy with spectral unmixing: Uncover the true behavior of proteins in living cells​

• Open access
• Published: 07 June 2024

The impact of maternal intrahepatic cholestasis during pregnancy on the growth trajectory of offspring: a population-based nested case‒control cohort study

• Xueqi Li 1 , 2 ,
• Yao Kong 1 , 2 ,
• Yuxin Ren 1 ,
• Yaqian Li 1 , 2 ,
• Jinfeng Xu 1 , 2 ,
• Yongchi Zhan 1 , 2 ,
• Shu Zhou 1 , 2 ,
• Fan Yang 2 , 3 ,
• Tingting Xu 1 , 2 &
• Xiaodong Wang 1 , 2  

BMC Pregnancy and Childbirth volume  24 , Article number:  413 ( 2024 ) Cite this article

Metrics details

Intrahepatic cholestasis of pregnancy (ICP) is associated with an increased risk of adverse fetal outcomes, yet its influence on offspring growth remains unclear. Our study dynamically tracks growth rates in children from ICP and healthy mothers and investigates the link between maternal liver function and developmental abnormalities in offspring.

Our case‒control study involved 97 women with ICP and 152 with uncomplicated pregnancies nested in a cohort of their offspring, including 50 from the ICP group and 87 from the uncomplicated pregnancy group. We collected pediatric growth and development data, with a maximum follow-up duration of 36 months. Stratified analyses of children's height, weight, and head circumference were conducted, and Spearman's rank correlation was applied to examine the relationships between maternal serological markers and pediatric growth metrics.

Maternal liver and renal functions, along with serum lipid profiles, significantly differed between the ICP and normal groups. In the ICP group, the offspring showed elevated alanine aminotransferase (ALT), direct bilirubin (DBIT), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (APOB) levels. Notably, the length-for-age z score (LAZ), weight-for-age z score (WAZ), and head circumference-for-age z score (HCZ) were lower in ICP offspring compared with those from normal pregnancies within the 1- to 12-month age range ( P  < 0.05). However, no significant differences in LAZ, weight-for-length z score (WLZ), BMI-for-age z score (BAZ), or HCZ were observed between groups in the 13- to 36-month age range. Maternal maximum lactate dehydrogenase (LDH) and total bile acids (TBA) levels during pregnancy were inversely correlated with LAZ and WAZ in the first year. Furthermore, offspring of mothers with ICP exhibited a greater incidence of stunting (24% vs. 6.9%, P  = 0.004) and abnormal HCZ (14% vs. 3.7%, P  = 0.034).

Conclusions

Growth disparities in offspring of ICP-affected pregnancies were most significant within the 1- to 12-month age range. During this period, maximum maternal LDH and TBA levels were negatively correlated with LAZ and WAZ values of offspring. The observation of similar growth rates between ICP and control group offspring from 13 to 36 months suggested catch-up growth in the ICP group.

Peer Review reports

Introduction

Intrahepatic cholestasis of pregnancy (ICP), also called “obstetric cholestasis”, is a multifactorial condition of pregnancy that occurs in the second or third trimester and is characterized by elevated serum bile acid and/or liver aminotransferase levels and pruritus in the absence of a skin rash [ 1 ]. The incidence of ICP varies based on ethnic group and geographical region. The risk factors for ICP include multiple pregnancies, advanced maternal age, in vitro fertilization-embryo transfer (IVF-ET), a history of prior pregnancy, and a family history of ICP. The exact etiology of ICP is not completely understood, but reproductive hormones and genetic, endocrine and environmental factors may account for its incidence [ 2 , 3 ].

Adverse fetal outcomes include preterm birth, preterm rupture of membranes, meconium-stained amniotic fluid and even sudden stillbirth, which are significant concerns for both patients and clinicians [ 4 , 5 ] Iatrogenic premature birth significantly elevates the incidence of premature birth in cases of ICP to prevent sudden stillborn infants. Higher levels of serum total bile acids (TBA) also contribute significantly to spontaneous premature birth [ 6 ].

Nevertheless, studies of ICP outcomes should not be limited to its short-term effects, exploring the long-term impacts of ICP on both mothers and their offspring is also crucial for advancing maternal–fetal medicine. Improving pregnancy health significantly benefits both maternal and offspring well-being. Maternal characteristics during pregnancy have a profound impact on fetal and child outcomes. The placenta plays an indispensable role in fetal development and can act as a selective barrier, critically regulating the transfer of maternal bile acids to the fetus, thereby preventing potential adverse effects induced by excessive exposure to these compounds [ 7 ]. Elevated bile acid may cause vasoconstriction and affect the development of the placental villous tree and its consequences for fetal growth [ 8 , 9 ].

This connection between maternal health and subsequent child development underscores the importance of identifying and understanding these relationships [ 10 ]. One study revealed that ICP is typically not associated with severe maternal morbidity but may increase the risk of subsequent hepatobiliary disease [ 11 ]. Emerging studies have focused on the association between ICP patients and their offspring. Recent studies have shown that the ICP may increase the risk of abnormal neurodevelopment and heart function in children [ 12 , 13 , 14 , 15 ].

Although an association between ICP and a heightened risk of adverse obstetric and neonatal outcomes has been established, with evidence suggesting that higher serum TBA levels amplify this risk [ 6 , 16 ], there appears to be a gap in the literature regarding the long-term impact of maternal ICP on the developmental trajectory of offspring. Moreover, disruption of bile acid homeostasis can affect metabolic regulatory mechanisms, potentially influencing lipid metabolism, insulin resistance, and renal function [ 17 , 18 , 19 , 20 , 21 ], but the effect of ICP on offspring metabolism has not been reported to date.

Hence, in this study, we aimed to assess the impact of ICP on offspring development, including growth, weight, and head circumference, by dynamically tracking pediatric data from the Department of Child Health, West China Second Hospital, Sichuan University. Additionally, we collected and analyzed biochemical indicators from both offspring and maternal blood serum to explore the potential metabolic effects of ICP, thereby highlighting the potential impact of ICP on child development and metabolic health.

Sample size calculation

This sample size calculation was conducted using G*Power software. Based on the set parameters of a medium effect size (Cohen's d = 0.5), a significance level (α = 0.05), and a statistical power of 80%, approximately 64 participants per group are needed. Thus, a total of approximately 128 participants are required to ensure that the study has sufficient statistical power to detect the anticipated effect size, assuming a two-sided test design. Considering potential losses due to follow-up, we decided to double the initial calculated sample size to enhance the robustness of our study outcomes. Consequently, with a case to control ratio of approximately 1:1.5, the final sample size will include 97 participants with ICP and 152 control participants.

Maternal study population

This nested case‒control cohort study was conducted at West China Second University Hospital from July 23, 2018, to August 25, 2021. A total of 249 pregnant women who had received regular and meticulous prenatal care were enrolled in the study; 97 of these women were diagnosed with ICP (72 singleton and 25 twin pregnancies), and 152 were considered normal controls without any complicated pregnancy (Fig.  1 ). All of the participants provided written informed consent, and the study was approved by the Research Ethics Committee of the West China Second University Hospital of Sichuan University.

Selection of women and offspring for assessing the impacts of ICP on metabolism and child growth

The inclusion criteria for the participants with ICP were a) pregnant women who experienced pruritus without rash and who had fasting TBA levels ≥ 10 µmol/L; b) patients whose gestational age was determined based on the first day of their last menstrual period and/or first trimester ultrasonographic measurements; and c) patients whose complete obstetric and serum data were available.

The exclusion criteria for ICP were HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome; acute fatty liver during pregnancy; primary biliary cirrhosis; viral hepatitis; and any ultrasound abnormalities.

Non-ICP comparators were healthy pregnancies obtained from the hospital’s birth records between 2018 and 2022. The inclusion criteria for an uncomplicated pregnancy were as follows: a) no history of liver disease; b) complete serologic data throughout the entire pregnancy; and c) prenatal care and delivery at our study hospital.

Maternal serum data collection

All participants underwent serological testing during the entire pregnancy. Peripheral venous blood was drawn into vacutainer tubes, whereas serum samples for biochemical assays were collected in standard gel separator tubes. Total bile acid (TBA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), albumin (ALB), albumin/globulin (A/G), alkaline phosphatase (ALP), γ-glutamyl transferase (γ-GT), lactate dehydrogenase (LDH), total protein (TP) and prealbumin (PA) levels were measured using standard laboratory methods. Renal function was assessed based on the levels of uric acid (UA), creatinine (Cr), cystatin C (CYSC), and urea (UN). Lipid profiles were evaluated by measuring total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (APOA1) and apolipoprotein B (APOB), and homocysteine (HCY) levels.

Child data collection

Our study initially included a total of 122 newborns in the ICP group, including 25 sets of twins, and 152 newborns in the control group. Given the widespread availability of pediatric preventive care in Chinese communities and considering the challenges of follow-up in community settings and with nonlocal women, we ultimately included anthropometric measurements (height, weight, and head circumference) of 50 offspring from ICP participants and 87 offspring from normal pregnancies from our health care clinic's systematic child health care program (Fig.  1 ). Follow-up for these children began one month postbirth and extended to the longest follow-up date of January 17, 2024. Each child had at least one set of pediatric health records documented during this period.

Weight, length and head circumference were assessed by trained and standardized interviewers. In China, routine pediatric health care typically continues until the age of six, with our longitudinal follow-up data extending up to 36 months. To better evaluate the growth and development of the children, the weight-for-age z score (WAZ), length-for-age z score (LAZ), BMI-for-age z score (BAZ), weight-for-length z score (WLZ) and head-circumference-for-age z score (HCZ) were computed utilizing the reference standards set forth by the World Health Organization (WHO) in 2006 for further analysis, enabling health care professionals to evaluate a child's growth in relation to a globally recognized benchmark [ 22 , 23 ]. Infants with values outside of the 2006 World Health Organization growth reference standards for WAZ (− 6 to + 5 SD), LAZ (− 6 to + 6 SD), WLZ (− 5 to + 5 SD), BAZ (-5 TO + 5 SD) and HCZ (-5 to + 5 SD) were considered invalid and excluded from analysis.

The serological data for the children were obtained from the hospital's laboratory information system following a process similar to that used for the mothers. In total, 76 offspring of ICP patients and 112 offspring of non-ICP women had at least one serological data point (liver function, renal function and blood lipid profile).

Monitoring pregnancy outcomes and child growth

Adverse neonatal outcomes included neonatal pneumonia, hypoproteinemia, neonatal hypoglycemia, neonatal anemia, pathological jaundice of the newborn (PJON), neonatal apnea, meconium staining of amniotic fluid (MSAF), neonatal respiratory distress syndrome (NRDS), small for gestational age (SGA) and neonatal asphyxia.

Preterm birth was defined as a liveborn infant with a gestational age < 37 completed weeks. Small for gestational age (SGA) was defined as a birthweight < 10th percentile. PJON refers to a condition characterized by elevated levels of bilirubin in a neonate's blood, manifesting within the first 24 h postbirth. NRDS is characterized by progressive respiratory difficulty, cyanosis, expiratory groaning, inspiratory retraction signs, and respiratory failure manifesting shortly after birth.

Infants born to mothers with known outcomes, baseline characteristics and at least one type of pediatric data were included in the anthropometry analyses. Children were defined as having abnormal development if they had at least one instance of LAZ, WAZ, BAZ, WLZ or HCZ score below -2 [ 24 ] or if they were diagnosed with global developmental delay language in outpatient medical records. Underweight was defined as a WAZ < -2, and stunting was defined as a LAZ < -2 according to the 2006 WHO growth standards [ 22 , 24 ].

Statistical methods

Data were verified, filtered, and subsequently analyzed using R version 4.3.1. For analyzing categorical variables, chi-square tests were employed. Continuous variables that were normally distributed were described using the mean and standard deviation (mean [SD]) and were analyzed with independent sample t tests. Conversely, variables exhibiting skewed distributions were described using medians and interquartile ranges (medians [IQRs]) and analyzed using the Mann‒Whitney U test. Growth indices such as LAZ, WAZ, WLZ, BAZ, and HCZ were calculated based on the 2006 WHO growth reference values using the ‘anthro’ package in R, and stratified analysis was conducted based on month. Correlation analyses were performed using the ‘state’ package to examine pairwise associations among various variables of interest. This approach, utilizing pairwise complete observations, ensures that each correlation coefficient is based on the maximum available data for each variable pair. Spearman correlation analysis was employed to identify relationships between the maximum levels of maternal LDH and TBA and indicators of abnormal child development, including LAZ and WAZ. We employed univariate logistic regression to determine the odds ratio (OR) for the likelihood of abnormal development in offspring and examined the association between elevated levels of TBA and LDH in the two groups. Significance was ascribed to two-tailed p values less than 0.05.

Clinical details of the participants

The characteristics of the women enrolled in the study are detailed in Table  1 . No significant difference was observed in maternal age at delivery or pregestational BMI between the groups. Approximately 8.3% of women in the ICP group reported a history of ICP, whereas none of the women in the non-ICP comparison group reported a history of ICP. The participants with ICP exhibited a greater incidence of preterm birth (62% vs. 85%, p  < 0.05) and cesarean Sect. (81.4% vs. 30.9%, p  < 0.05). Significant differences were also noted in birth weight, birth height, and placental weight between offspring of mothers with ICP and offspring of healthy mothers, consistent with our previous study in a mouse model of ICP induced by 17α-ethynyl estradiol [ 25 ].

There were 122 live births recorded in the ICP group and 152 live births in the control (Con) group. Of these, 68 neonates born to mothers diagnosed with ICP were admitted to the neonatal care unit (NCU) in contrast to 82 offspring from pregnancies without complications ( P  = 0.862). A greater proportion of newborns from mothers with ICP were admitted to the neonatal intensive care unit (NICU) (19.6% vs. 3.3%, P  < 0.001). The pregnancy outcomes for newborns of mothers with ICP and those of mothers with normal pregnancies are detailed in Additional file 1: Supplementary Table 1. Compared with newborns from normal pregnancies, newborns from mothers with ICP exhibited increased rates of MASF, neonatal pneumonia, hypoproteinemia, hypoglycemia, neonatal anemia, PJON, NRDS, neonatal apnea, neonatal respiratory failure, SGA, and asphyxia. However, only hypoglycemia reached statistical significance ( P  = 0.029).

Comparison of maternal liver function, renal function and blood lipid profiles between the ICP and Con groups

We collected serum biomarkers, including liver function, renal function and blood lipids, throughout pregnancy and recorded the maximum values of these serum biomarkers for further study. The results are shown in Table  2 .

In patients with ICP, we observed a marked increase in the TBA level (20.1 vs. 2.5) and significant differences in the ALT and AST levels, both of which are indicators of hepatic injury. The maternal concentrations of TB, IBIL, and DBIL were significantly greater in the group of pregnant women with ICP than in those with normal pregnancies. Although TP and ALB were greater in the ICP group, the differences did not reach statistical significance. ALP, γ-GT, LDH, and PA levels were also significantly elevated in the ICP groups.

It is widely known that the ICP is associated with impaired liver function, but research on the detection of renal function is rare. Renal function, which is less frequently associated with ICP in research, revealed significant differences in Cr, UA, UN, and CYSC levels ( P  < 0.05) in our study (Table  2 ).

Furthermore, our analysis indicates a potential link between ICP and maternal dyslipidemia, with ICP patients showing significantly higher TG and altered HDL-C and LDL-C levels, consistent with our previous findings [ 21 ]. TC levels were not significantly different between the two groups (median 6.2 vs. 6.3). APOB (median 1.4 vs. 1.1) and HCY (median 7.3 vs. 6.5) concentrations were significantly increased in the ICP group, whereas APOA1 (median 1.8 vs. 2.1) levels were significantly reduced (Table  2 ).

Comparison of offspring liver function, renal function and blood lipid profiles between the ICP and Con groups

Our findings (Table  3 ) indicate a significant increase in ALT concentrations in offspring from the ICP group (median 21 vs. 16, P  < 0.05), with no notable differences in ALT, TB, IBIL, TP, ALB, A/G, LDH, γ-GT, or PA levels. Despite maternal renal impairment, biomarkers of renal function in offspring serum showed no differences between the groups. Lipid profile analysis revealed higher median HDL-C (1.05 vs. 0.90, P  < 0.05), LDL-C (2.0 vs. 1.3, P  < 0.05), and APOB (0.59 vs. 0.38, P  < 0.05) levels in offspring from ICP-affected pregnancies (Table  3 ), suggesting potential long-term metabolic implications.

Stratified analysis of offspring LAZ, WAZ, WLZ, BAZ and HCZ by month

To further explore the long-term effects of ICP on offspring growth development compared with those of normal pregnancies, we compared LAZ, WAZ, WLZ, BAZ, and HCZ based on the timing of pediatric health care visits (Table  4 ). In the 1- to 3-month age range, median BAZ values were similar between the groups. However, LAZ, WAZ, WLZ and HCZ values were significantly different between the ICP and Con groups ( P  < 0.01). In the following three-month period, significant differences were observed in LAZ (median -0.42 vs. 0.12), WAZ (median -0.12 vs. 0.49), WLZ (median 0.31 vs. 0.61), BAZ (median 0.16 vs. 0.57), and HCZ (median -0.21 vs. 0.32) values between the offspring of ICP-affected pregnancies and the normal control group. In the 7- to 9-month age range, evidence of differences in LAZ (median -0.19 vs. 0.28), WAZ (median 0.12 vs 0.47), and HCZ (median 0.04 vs. 0.5) values were found between the two groups, whereas BAZ (median 0.31 vs. 0.42) and WLZ (0.39 vs. 0.52) values were similar. At 10–12 months, there was little evidence of a difference in the BAZ (median 0.26 vs. 0.57), while statistically significant differences were observed in the LAZ, WAZ, WLZ, and HCZ ( P  < 0.05). Between 1 and 2 years, LAZ, WAZ, WLZ, BAZ, and HCZ values showed minimal differences. Similarly, in the 2- to 3-year age range LAZ, WLZ, BAZ, and HCZ values were comparable between the groups. However, WAZ levels displayed evidence of difference, with ICP participants showing a median of -0.46 compared with 0.22 in the normal control group. Intriguingly, after the exclusion of a child with global developmental delay from the ICP group, WAZ levels were found to be similar between the two groups.

The diagnosis of abnormal development

In the ICP group, one child was diagnosed with global developmental delay, and two children exhibited language delays. In the control group, one child experienced language delay ( P  = 0.554). A significant difference in the incidence of stunting was noted between the two groups (12 vs. 6, P  = 0.004). Conversely, the difference in the underweight rate between the two groups was not statistically significant (8 vs. 7, P  = 0.166). Furthermore, the offspring in the ICP group demonstrated a greater rate of abnormality in HCZ (P = 0.034) (Additional file 1 : Supplementary Table 2).

Correlation analysis between maternal serum biomarkers and offspring serum biomarkers

To investigate the associations between abnormal offspring development and serum biomarkers, we conducted a correlation analysis. The results (Fig.  2 A) indicate that abnormal development in offspring is negatively associated with singleton birth, maternal APO-A1 levels, gestational week, birth weight, and birth height ( P  < 0.05). Conversely, factors such as preterm birth, PJON, elevated maternal TBA levels, and maternal LDH levels are associated with an increased incidence of abnormal offspring development ( P  < 0.05).

Correlation analysis of baseline characteristics, serological test results and offspring growth indicators between pregnant women with ICP and pregnant healthy controls. A Correlations among baseline characteristics, serological tests and abnormal offspring development. “*” represents P  < 0.05. Red indicates a positive correlation, and blue signifies a negative correlation. The intensity of the color directly correlates with the strength of the relationship. Scatter plots depicting the correlation between maximum maternal LDH levels (left) and maximum maternal TBA levels (right) and offspring LAZ or WAZ at different time intervals. LAZ ( B ) and WAZ ( C ) values in offspring during the 1- to 6-month age range, LAZ ( D ) and WAZ ( E ) values in offspring during the 7- to 12-month age range, and LAZ ( F ) values in offspring during the 13- to 18-month age range. WAZ values in the 7- to 12-month age range, LAZ values in the 13- to 36-month age range, and WAZ values in the 13- to 36-month age range can be found in Additional file 2: Figure S1. Green represents the control group, and yellow represents the ICP group. Correlation coefficients (R) and p values are provided for each group to indicate the strength and significance of the correlations. Abbreviations: GWG, gestational weight gain; BH, birth height; BW, birth weight; K, child; M, mother; Ab_Dev, abnormal development (LAZ, WAZ, WLZ, BAZ or HCZ < -2); PB, preterm birth; NICU, neonatal intensive care unit; PJON, pathological jaundice of newborn. LAZ, length-for-age score. WAZ, weight-for-age z score. All serum markers are represented by the highest values

Furthermore, our findings revealed that maternal maximum TBA levels were positively correlated with markers of liver function, including ALT, AST, DBIL, IBIL, and GGT ( P  < 0.05). TBA concentrations were also correlated with maternal blood lipid indicators such as TC, TG, and APOB ( P  < 0.05). However, maternal APOA1 levels were negatively correlated with TBA levels ( P  < 0.05) (Fig.  2 A).

The relationship between maternal TBA levels and offspring development

To further elucidate the relationship between serum markers and the abnormal growth development of offspring, we analyzed data concerning offspring health care alongside maternal TBA and LDH levels.

During the first 12 months, positive correlations were observed between the maximum maternal TBA and LDH levels and decreased LAZ and WAZ in both the ICP and normal groups. In the analysis of anthropometric data from 1 to 6 months, a statistically significant negative correlation was noted between LAZ scores and maternal TBA levels in both the ICP and normal groups (Fig.  2 B, C). Specifically, maternal TBA levels were moderately negatively correlated with LAZ values, with a correlation coefficient of -0.42 in the ICP group (Fig.  2 B). In the control group, the correlation coefficient was -0.2. WAZ scores also demonstrated a negative correlation with TBA concentration in both groups (Fig.  2 C).

From 7 to 12 months, both offspring LAZ (R = -0.35) and WAZ (R = -0.3) scores were negatively correlated with maternal TBA levels in the ICP group, whereas only WAZ scores showed a statistical difference in the normal group (Fig.  2 D, E). However, at 13 to 24 months and 25 to 36 months, no significant differences were found between the maximum maternal TBA levels and LAZ or WAZ scores in either group (Fig.  2 E, Additional file 2: Supplementary Fig. 1).

Moreover, univariate logistic regression analysis identified TBA levels as an independent risk factor for the abnormal development of offspring (Additional file 1: Supplementary Table 3). TBA levels greater than 10 μmol/L and lesser than 40 μmol/L were associated with a 2.05-fold increase in the odds of abnormal development in offspring; however, this association did not reach statistical significance (OR = 2.05, 95% CI = 0.71 to 5.88, P value = 0.183). A more pronounced effect was observed for TBA levels exceeding 40 μmol/L, which were associated with a 5.85-fold increase in the odds of abnormal development (OR = 5.85, 95% CI = 1.55 to 22.04, P value = 0.009).

The relationship between maternal LDH levels and offspring development

Building upon previous analysis, we also detected relationships between maternal LDH levels and LAZ and WAZ values. From 1 to 6 months and 7 to 12 months (Fig.  2 B-E), maternal LDH levels were significantly negatively correlated with LAZ and WAZ values in the ICP group ( P  < 0.05). However, in the Con group, minimal differences were observed. From 13 to 18 months and 19 to 24 months (Fig.  2 E, Additional file 2: Supplementary Fig. 1), a significant difference was observed in the relationship between maternal LDH concentration and LAZ and WAZ scores within the ICP group. However, in normal pregnancies, this relationship did not reach statistical significance.

From 25 to 36 months, no evidence was found to suggest a relationship between maternal LDH and LAZ concentrations in either group (Additional file 2: Supplementary Fig. 1). Additionally, in both the normal and the ICP groups, WAZ was not associated with the highest maternal LDH levels.

On the other hand, univariate logistic regression analysis demonstrated a statistically significant association between LDH levels and the risk of abnormal offspring development in the ICP group (OR = 1.004, 95% CI = 1.00 to 1.01; P  = 0.012) (Additional file 1: Supplementary Table 3). This indicates a modest but significant increase in risk with increasing LDH levels. However, LDH levels were not significantly associated with the risk of abnormal development in offspring from the normal group (OR = 1.003, 95% CI = 0.98 to 1.01, P  = 0.667) (Additional file 1: Supplementary Table 4).

In our nested case‒control cohort study, we found that offspring of ICP-affected pregnancies show impaired growth and development. However, after 12 months, no differences in LAZ, WLZ, BAZ or HCZ values were observed. Moreover, ICP mothers were more likely to have renal function and blood lipid abnormalities in addition to liver function abnormalities; however, only minimal differences in offspring metabolism were observed.

Elevated bile acids due to liver dysfunction can contribute to renal impairment by inducing vasoconstriction, oxidative stress and inflammation in renal tissues [ 26 , 27 ]. Elevated bile acids can impair kidney function in patients with chronic renal failure [ 26 ]. The relationship between bile acids and lipid metabolism is complex and plays a crucial role in the regulation of lipid homeostasis and cardiovascular health. Bile acids not only are essential for the digestion and absorption of lipids but also regulate lipid levels through signaling pathways such as FXR (Farnesoid X Receptor) and TGR5 [ 28 ], which influence various aspects of lipid metabolism, including cholesterol homeostasis, triglyceride levels, and lipoprotein synthesis, contributing to the maintenance of overall metabolic health [ 18 , 29 ]. In our study, offspring LDL-C, HDL-C and APOB values were significantly increased in the ICP group.

Specifically, differences in liver function and blood lipid levels were noted between children born to mothers diagnosed with ICP and those born to mothers with normal pregnancies. The resolution of bile acid metabolism and dyslipidemia in newborns of mothers with ICP after birth could be attributed to their own metabolic adjustments. Research highlights significant changes in bile acid metabolism during pregnancy progression in ICP patients compared to normal pregnant women, indicating that altered BA metabolism in ICP patients could impact fetal development [ 30 ]. However, after birth, the neonatal liver and gastrointestinal system begin functioning independently, potentially normalizing these metabolic pathways​. Additionally, the gut microbiota plays a crucial role in BA metabolism [ 31 ] and is significantly altered postnatally as newborns are exposed to the external environment and begin feeding. Hence, abnormal maternal metabolism may affect fetal outcomes. Abnormal maternal lipid levels are associated with the risk of small for gestational age (SGA) infants [ 32 ]. These changes can significantly impact fetal development and lead to complications such as fetal growth restriction and preterm birth [ 33 ]. However, research related to the abnormal growth and development of ICP-affected offspring has not been reported.

To the best of our knowledge, we are the first to trace the development trajectory of children born to mothers with ICP versus those with normal pregnancies from birth to a maximum of 36 months. From 1 to 12 months, LAZ, WAZ and HCZ values of the offspring in the ICP group significantly decreased, indicating relatively poor development. Low birth weight (LBW) was used to identify infants born with insufficient growth because this metric exclusively relies on weight at birth [ 34 ]. Our study revealed that neonatal birth weight and birth height were lower in the ICP group than in the normal pregnancy group, which may partly explain the difference in birth age. However, between 12 and 36 months, similar values were observed between the two groups, suggesting that the impact of maternal TBA levels on offspring anthropometric measures may diminish as the child ages beyond the first year of life. WAZ values were slightly different between the ICP group and the normal group at 25 to 36 months, and the P value obtained was potentially influenced by the diagnosis of global developmental delay in one child (Supplementary Fig. 1). Global developmental delay (GDD) is defined as a significant delay in two or more developmental domains, including gross and fine motor skills, speech and language, cognition, social and personal skills, and activities of daily living, in children under the age of 5 years. After removing the children of mothers with ICP who were diagnosed with GDD, WAZ reanalysis results showed no significant difference.

Catch-up growth (CUG) is commonly defined as a height velocity that is more rapid than average for individuals of a comparable age or maturity, allowing them to 'catch up' to the growth trajectory of their full-term peers [ 34 ]. The difference in growth velocity suggested the possibility of catch-up growth in the offspring of mothers with ICP. A potential mediator linking ICP to offspring growth and development is preterm birth. A study revealed that over 80% of late preterm and small for gestational age (SGA) infants exhibited catch-up growth in weight and length at 3 and 6 months of corrected age. However, this growth trajectory seemed to slow after the first 6 months, particularly when solid foods were introduced, which could cause feeding challenges and potentially affect growth [ 35 ].

Adequate and appropriate nutritional support is crucial for catch-up growth [ 36 ]. Advocating for exclusive breastfeeding and implementing strategies for the prevention and prompt management of pneumonia are also critical interventions that may contribute to optimizing child growth outcomes [ 37 ]. Preterm infants undergo various metabolic adaptations after birth to support catch-up growth. For neonates born to mothers with ICP, the reasons for catch-up growth may be similar. After birth, the resolution of exposure to elevated maternal bile acids, along with appropriate nutritional and medical support, enable these infants to undergo catch-up growth. Environmental factors may also influence catch-up growth [ 32 ].

Interestingly, initial rapid catch-up growth leads to morphological abnormalities in pancreatic islets and fibrosis, which are linked to alterations in the expression of cell adhesion-related proteins. This process subsequently results in glucose intolerance and dyslipidemia in male rats [ 38 ]. Although the offspring of individuals with ICP may achieve relatively normal growth trajectories, the long-term impacts of ICP on these offspring necessitate ongoing surveillance. Although catch-up growth is possible and does occur in infants with ICP, it is variable and may be influenced by a range of factors, including nutrition, socioeconomic status, gestational age at birth, and health care practices. Monitoring the growth of ICP infants closely and providing guidance on nutrition and care to support optimal growth outcomes are needed.

Growth stunting is associated with concurrent and longer-term deficits in cognition, behavior, motor skills, and school performance [ 39 ]. The HCZ is generally regarded as an important indicator of neurodevelopmental progress in infants and young children. A smaller than average HCZ potentially indicates issues with brain development, potentially leading to cognitive, motor, and language development delays [ 40 , 41 ]. In our study, the incidence of stunting (LAZ < -2) and abnormal HCZ (HCZ < -2) significantly increased in the ICP group. Interestingly, a cohort study revealed that offspring of an ICP-affected pregnancy are more likely to be diagnosed with neurodevelopmental conditions [ 14 ]. Although catch-up growth was noted after the age of one and offspring from the ICP cohort displayed no significant developmental disparities compared to those from the control group, the ICP group had relatively lower HAZ, WAZ, WLZ, BAZ and HCZ values and a significantly greater incidence of stunting and abnormal HCZ. These findings indicate an elevated risk of developmental anomalies despite catch-up growth and the need for dynamic monitoring of the offspring of ICP participants.

In liver diseases, particularly cholestasis, LDH and TBA serve as important biomarkers. LDH levels indicate liver cell damage because LDH is released into the bloodstream during cell injury. ICP patients exhibit elevated LDH levels in serum liver function tests [ 42 ]. Elevated TBA levels are more specific to cholestasis, reflecting impaired bile flow and accumulation of bile acids in the liver and blood. In our study, we found that maximum maternal LDH and TBA levels were positively related to lower LAZ and WAZ values from 1 to 12 months. Total bile acid (TBA) levels greater than 40 µmol/L are associated with severe intrahepatic cholestasis of pregnancy (ICP). This condition not only poses significant maternal risks but also increases the likelihood of developmental abnormalities in offspring. Close monitoring of these biomarkers in mothers could be crucial, emphasizing the importance of proactive management in cases of liver disease and cholestasis to mitigate adverse outcomes in offspring.

A key innovation from this nested case‒control cohort study is that we dynamically tracked the pediatric health care data in offspring from 1 month to a maximum of 36 months. In addition, we performed serological tests in the ICP groups and analyzed the relationship between offspring growth and maternal serum biochemistry markers.

A significant limitation of our study is the incomplete collection of pediatric health data due to the difficulty in gathering community health records and the nonlocal status of some women, resulting in a relatively small sample size of offspring. A larger, multicenter population-based cohort is needed to track the development of offspring in ICP groups over a more extended period of time and to explore the potential underlying mechanisms of this relationship.

The maximum values of maternal liver function, renal function, and blood lipid tests were significantly greater in the ICP groups, with only slight differences detected in the offspring of women with ICP. From 1 to 12 months, offspring from pregnant women with ICP exhibit lower growth velocities. However, after 12 months, these children demonstrate catch-up growth until 3 years of age. Elevated LDH and TBA levels in women with ICP are associated with an increased risk of diminished LAZ and WAZ values during the first 12 months. Additionally, offspring with ICP have a greater incidence of stunting and abnormal HCZ (HCZ < -2).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Neonatal care unit

Neonatal intensive care unit

Pathological jaundice of the newborn

Meconium staining of amniotic fluid

Neonatal respiratory distress syndrome

Small for gestational age

Total bile acid

Alanine aminotransferase

Aspartate aminotransferase

γ-Glutamyl transferase

Total bilirubin

Direct bilirubin

Indirect bilirubin

Albumin/globulin

Alkaline phosphatase

Lactate dehydrogenase

Total protein

Total cholesterol

Triglycerides

Low-density lipoprotein cholesterol C

High-density lipoprotein cholesterol C

Apolipoproteins A1

Apolipoproteins B

Homocysteine

Length-for-age Z score

Weight-for-age Z score

Weight-for-length Z score

BMI-for-age Z score

Head-circumference-for-age Z score

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Acknowledgements

We extend our sincere gratitude to the physicians of the Pediatric Healthcare.

Department for their invaluable assistance in providing the data on the children involved in this study.

This work was supported by the general program of the Key Research and Development Project by Department of Science and Technology in Sichuan Province (2022YFS0042) and the National Natural Science Foundation for Young Scientists of China (grant number 82001560), and the Science Foundation for Young Scientists of Sichuan Province (2023NSFSC1608).

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Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Renmin Nan Road, Chengdu, 610041, Sichuan, China

Xueqi Li, Yao Kong, Yuxin Ren, Yaqian Li, Jinfeng Xu, Yongchi Zhan, Shu Zhou, Tingting Xu & Xiaodong Wang

Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610041, Sichuan, China

Xueqi Li, Yao Kong, Yaqian Li, Jinfeng Xu, Yongchi Zhan, Shu Zhou, Fan Yang, Tingting Xu & Xiaodong Wang

Department of Child Health, West China Second University Hospital, Sichuan University, Chengdu, China

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Contributions

Tingting Xu and Xiaodong Wang conceived the study. Xuqi Li, Yao Kong, Yuxin Ren, Yaqian Li, Jinfeng Xu, Yongchi Zhan contributed to the acquisition and interpretation of the data for this study. Xueqi Li and Tingting Xu analyzed the data. Xueqi Li contributed to the preparation of the tables and figures and drafted the manuscript. Shu Zhou and Fan Yang provided professional advice and technical support. Tingting Xu and Xiaodong Wang critically revised the manuscript for important intellectual content and supervised the entire study, they contributed equally to this work.

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Supplementary Information

12884_2024_6559_moesm1_esm.docx.

Additional file 1: Supplementary Table 1. Comparison of the Neonatal Baseline Characteristics between Pregnant Patients with ICP and Patients with Normal Pregnancies. Supplementary Table 2. Diagnosis of abnormal development in offspring between the ICP group and the normal group. Supplementary Table 3. Logistic regression of abnormal development in offspring in the ICP group. Supplementary Table 4. Logistic regression of abnormal offspring development in the normal group.

12884_2024_6559_MOESM2_ESM.tif

Additional file 2: Figure 1. Correlation Analysis of Serological Markers in Mothers from the ICP and Normal Groups with Child Development Indicators. Scatter plots depicting the correlation between maximum maternal LDH levels (left) and maximum maternal TBA levels (right) and offspring LAZ or WAZ values at different time intervals. WAZ (A) values in offspring during the 13- to 18-month age range, LAZ (B) and WAZ (C) values in offspring during the 19- to 24-month age range, and LAZ (D) and WAZ (E) values in offspring during the 25- to 36-month age range. Green represents the control group, and yellow represents the ICP group. Correlation coefficients (R) and p values are provided for each group to indicate the strength and significance of the correlations. Abbreviations: GWG, gestational weight gain; BH, birth height; BW, birth weight; K, child; M, mother; Ab Dev, abnormal development (LAZ, WAZ, WLZ, BAZ or HCZ <-2); PB, preterm birth; NICU, neonatal intensive care unit; PJON, pathological jaundice of newborn. LAZ, length-for-age z score. WAZ, weight-for-age z score.

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Li, X., Kong, Y., Ren, Y. et al. The impact of maternal intrahepatic cholestasis during pregnancy on the growth trajectory of offspring: a population-based nested case‒control cohort study. BMC Pregnancy Childbirth 24 , 413 (2024). https://doi.org/10.1186/s12884-024-06559-z

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Received : 15 March 2024

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DOI : https://doi.org/10.1186/s12884-024-06559-z

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