RFC1 Chapter 9 Causal-Comparative Research

Causal-Comparative Research Overview

• Aims to identify causes or reasons for differences in groups or individuals.
• Ex post facto design: researchers analyze existing differences without manipulation.

Types of Causal-Comparative Research

• Retrospective Causal-Comparative Research:

  • Focuses on effects first, then investigates causes.
  • Common in education (e.g., comparing stress levels between those in and not in an exercise program).

• Prospective Causal-Comparative Research:

  • Begins with potential causes and looks into their effects.
  • Example: Comparing self-esteem in middle school children based on their prior dance class attendance.

Key Characteristics of Causal-Comparative Research

• Does not establish clear cause-effect relationships; rather, it identifies correlations.
• Typically involves multiple groups and at least one dependent variable.
• Independent variables are pre-existing and not manipulated by the researcher.

Variables in Causal-Comparative Research

• Organismic Variables: Age, ethnicity, sex.
• Ability Variables: Achievement levels.
• Personality Variables: Self-concept.
• Family-Related Variables: Socioeconomic status (SES).
• School-Related Variables: Type and size of school.

Limitations of Causal-Comparative Research

• Limited control over variables by the researcher.
• Interpretation of results must be cautious due to inability to establish definite cause-effect chains.

Practical Applications

• Useful for scenarios where manipulation of variables is impractical or unethical.
• Can enhance decision-making and provide insights for future experimental studies.
• Less costly compared to experimental studies.

Conducting a Causal-Comparative Study

• Involves selecting two distinct groups based on a variable of interest for comparison.
• Critical to define and select comparison groups accurately.
• Grouping variables must be operationally defined for clear differentiation.
• Comparable groups reduce potential extraneous explanations for findings.

Basic Study Designs

• Group Exposure:
  • One group experiences an independent variable while the other does not; both measured on a dependent variable.
• Dual Group Exposure:
  • Two groups receive different independent conditions, followed by measurement on the dependent variable.

Control Procedures for Enhancing Research Validity

• Matching:

  • Groups can be equated on specific variables to control confounding factors (e.g., pairing high achievers in instructional studies).

• Homogeneous Group Comparison:

  • Focuses on groups that share similar characteristics regarding extraneous variables for more accurate comparisons.

• Analysis of Covariance (ANCOVA):

  • Adjusts scores on dependent variables based on initial differences in related variables, enhancing the validity of conclusions.

Data Analysis and Interpretation

• Utilizes both descriptive and inferential statistics for data assessment.
• Descriptive statistics frequently measure means and standard deviations.
• Common inferential tests include t-tests, analyses of variance (ANOVA), and chi-square tests.