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Related Concept Videos

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Related Experiment Video

Updated: Mar 27, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Commentary: Mediation Analysis, Causal Process, and Cross-Sectional Data.

Patrick E Shrout1

  • 1a New York University.

Multivariate Behavioral Research
|January 7, 2016
PubMed
Summary
This summary is machine-generated.

Cross-sectional data analyses cannot reveal longitudinal mediation processes, even with intervening variables. Exploring alternative causal models is crucial for accurate psychological research and understanding psychopathology.

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Area of Science:

  • Psychological research methodology
  • Longitudinal data analysis
  • Causal inference in psychology

Background:

  • Previous work questioned the utility of cross-sectional data for understanding longitudinal mediation.
  • Maxwell, Cole, and Mitchell (2011) extended this research, considering partially explained longitudinal processes.

Purpose of the Study:

  • To evaluate the conclusions of Maxwell et al. regarding cross-sectional data and longitudinal mediation.
  • To advocate for the exploration of alternative causal models beyond the autoregressive model.

Main Methods:

  • Critically analyzing the findings of Maxwell et al. (2011).
  • Illustrating the derivation of different causal models using psychopathology research examples.

Main Results:

  • Cross-sectional data analyses do not adequately reveal longitudinal mediation processes.
  • Different causal models yield distinct patterns of bias when compared to cross-sectional analyses.

Conclusions:

  • Cross-sectional analyses are insufficient for understanding longitudinal mediation.
  • A broader exploration of diverse causal models is essential for advancing psychological science and research on psychopathology.