key term - Factor Analysis

Definition

Factor analysis is a statistical method used to identify underlying relationships between variables by grouping them into factors or components. This technique helps in reducing data complexity, allowing researchers to uncover latent constructs that influence observable behaviors, particularly in the study of attitudes and their predictive power regarding behavior.

5 Must Know Facts For Your Next Test

1. Factor analysis helps researchers determine whether several measured variables can be grouped together based on their correlations, leading to the identification of broader, underlying dimensions.
2. This technique is commonly used in psychology for survey and questionnaire design to ensure that items intended to measure the same construct load onto the same factor.
3. Factor analysis can be exploratory or confirmatory; exploratory factor analysis looks for potential underlying relationships without preconceived notions, while confirmatory factor analysis tests specific hypotheses about those relationships.
4. It aids in simplifying complex data sets by reducing the number of variables to a smaller set of factors, making interpretation easier and more meaningful.
5. In the context of attitude measurement, factor analysis can reveal how different aspects of attitudes interact and predict behaviors, thus enhancing the understanding of the attitude-behavior link.

Review Questions

• How does factor analysis assist in understanding the relationship between attitudes and behaviors?
  • Factor analysis assists in understanding the relationship between attitudes and behaviors by revealing latent constructs that may underlie observable attitudes. By grouping related items together, researchers can identify how these constructs predict behavioral outcomes. This statistical method helps clarify which specific attitudes correlate strongly with certain behaviors, allowing for more accurate predictions and deeper insights into human psychology.
• Discuss the difference between exploratory and confirmatory factor analysis and their relevance in attitude measurement.
  • Exploratory factor analysis (EFA) is used when researchers want to explore potential underlying factors without pre-established theories about how many factors exist or what they might be. In contrast, confirmatory factor analysis (CFA) is used to test hypotheses about the relationships between variables after EFA has suggested potential groupings. Both methods are vital in attitude measurement because they help validate whether the items on a survey truly reflect the intended underlying constructs and predict behaviors accurately.
• Evaluate the impact of construct validity on factor analysis results and how it influences attitude-behavior predictions.
  • Construct validity significantly impacts factor analysis results as it determines whether the factors identified truly represent the theoretical constructs they are supposed to measure. If a factor does not have high construct validity, the relationships identified may lead to inaccurate conclusions about how attitudes predict behavior. Ensuring strong construct validity allows researchers to trust that their findings from factor analysis will effectively inform the connections between attitudes and actual behaviors, thereby enhancing predictive accuracy and contributing to more robust theories in social psychology.

Related terms

Latent Variables: Variables that are not directly observed but are inferred from other measured variables, representing underlying constructs in psychological research.

Reliability: The consistency of a measure or assessment tool, which factor analysis can help determine by showing how closely related different items are within a factor.

Construct Validity: The extent to which a test or instrument measures the theoretical construct it is intended to measure, often evaluated using factor analysis.