Bivariate Correlations & Interrogating Association Claims

Research Methods

Chapter 8: Bivariate Correlations & Interrogating Association Claims

  • Objective: Understand the role of measured variables in correlational studies.
  • Key Distinction: Measured variables, rather than statistics, define a study as correlational.
  • Validity of Association Claims:
    • Critical to interrogate the validity of association claims.
    • A correlational study supports an association claim but not a causal claim.

Research Scenario: Dr. Singh's Study

Description of the Study

  • Dr. Singh's aim: Explore the relationship between self-esteem and students' willingness to participate in class.
  • Methodology:
    • Students fill out a self-report measure of self-esteem consisting of the following items:
    1. I feel confident.
    2. I am capable.
    3. I rarely feel good about myself.
    • Response scale: 1 (strongly disagree) to 5 (strongly agree).
    • Data collection: Two research assistants tally the number of times students raise their hands in class.

Research Questions

  1. Variables Identified:
    • Self-esteem (measured).
    • Willingness to talk in class (measured).
  2. Type of Variables:
    • Both variables are measured, none are manipulated.
  3. Nature of Claim:
    • Dr. Singh will make an association claim, not a causal claim.
  4. Validity Concerns:
    • Important validities: Construct validity of self-esteem measure.
  5. Reliability Concerns:
    • Significant reliability: Internal consistency of the self-esteem measure.
  6. Construct Validity Assessment:
    • Method to assess: Compare self-esteem measure with established measures (e.g., Rosenberg Self-Esteem Scale).
    • Desired evidence: High correlation with established measures, showing convergent validity.
  7. Question Type for Self-Esteem:
    • The self-esteem measure uses declarative statements for responses.
  8. Concerns About Acquiescence:
    • Dr. Singh could include opposite wording statements to balance responses.
  9. Observer Bias Reduction:
    • Ensuring that research assistants are trained to be objective in observation,
  10. Participant Reactivity Reduction:
    • Implementing indirect methods for counting participation, such as unaware observation.

Bivariate Correlation

Definition

  • Bivariate Correlation: An association between exactly two measured variables.
  • Association Claim: Describes the relationship between these two measured variables, asserting that two things are related without demonstrating causation.

Examples of Bivariate Correlation Data

Well-Being and Deep Talk Study (Mehl et al., 2010)

  • ```plaintext
    PERSON | SCORE ON WELL-BEING SCALE | PERCENTAGE OF CONVERSATIONS RATED AS DEEP TALK

A | 1.2 | 80
B | 0.3 | 52
C | -0.4 | 35
D | 1.4 | 42
ZZ | -1.6 | 16

  - Note: Data are fabricated for illustration purposes.
  - Correlation coefficient: r = .26.

## Sitting Time and MTL Thickness Study (Siddarth et al., 2018)
- ```plaintext
  ADULT | TIME SPENT SITTING (HOURS) | MTL TOTAL THICKNESS (MM)
  ------------------------------------------------------------
  A     | 10                          | 2.67
  B     | 11                          | 2.56
  C     | 5                           | 2.70
  D     | 7                           | 2.51
  JJ    | 3                           | 2.34
  • Correlation coefficient: r = -.37.

Interrogating Association Claims

Key Validities to Assess

  • Three types of validities: Construct validity, statistical validity, and external validity.
  • Importance of internal validity: Correlational studies do not establish internal validity.

Construct Validity

  • How well operationalized were the variables?
  • Assessing validity:
    • Measurement methods must capture the constructs of interest accurately.

Statistical Validity

Considerations:

  • Strength and precision of the association:
    • Effects of outliers on correlation coefficients.
    • Replication of findings across multiple studies.
    • Examination for restriction of range.
    • Assessing if a zero association is curvilinear.

Three Criteria for Causation

1. Covariance

  • Are the results showing that variable A changes with variable B?
  • Example: Watching deeper conversations correlates with higher well-being.

2. Temporal Precedence

  • Establishes that one variable must occur before the other.
  • Important to resolve directionality issues.

3. Internal Validity

  • Ensure that there are no plausible alternative explanations (third-variable problem) for the relationship between A and B.

External Validity

Generalization of Findings

  • Can findings be generalized across various populations and contexts?
  • Example: MTL Thickness and sitting study concluded using convenience sampling (may limit generalizability).

External Validity Concerns

  • Considerations about the sample: Gender, age, how participants were recruited, etc.

Moderating Variables

Definition

  • A variable that influences the strength or direction of the relationship between two other variables.
  • Example: Relationship between attendance and team success moderated by local community ties.

Applications of Moderating Variables

  • Assess whether certain groups have stronger relationships between A and B.
  • Understanding how factors like parental discussion influence aggression related to media.