Correlational design

Page 1: Correlational Design Overview

• Introduction to correlational design concepts.

• Cost analysis represented in various formats.

Page 2: Lecture Outline

Key Learning Objectives

• Understanding correlational design:

  • Definition and terminology.

  • Identifying benefits and limitations.

  • Methods employed:

    • Observation.

    • Survey/Questionnaire research.

  • Ensuring reliability and validity in correlational research.

Page 3: Understanding Correlational Design

Definition

• Correlational design involves data collection without manipulating variables.

• Alternative names include:

  • Non-manipulation.

  • Correlational Survey study.

  • Observational study.

  • Non-experimental design.

Page 4: Purpose of Correlational Design

Applications

• Explore relationships in various contexts:

  • Relationship between advertisement spending and profit.

  • Connection between physical activity levels and depression severity.

  • Correlation between impulsive behavior scores and juvenile criminal offenses.

Objectives of Correlational Design

• Investigate whether variables are related.

• Determine if a change in one variable relates to a change in another.

• Understand the co-varying behaviors of variables.

Page 5: When to Use Correlational Design

Situations for Application

• Ethical constraints prevent variable manipulation.

• Interest in managing multiple variables outside a laboratory environment.

• Exploration of natural data trends for ecological validity.

• Evaluating the relative influence of correlated variables.

• Refining variables in questionnaire design.

Page 6: Additional Examples of Correlational Design

Practical Scenarios

• Assessing the effectiveness of varying lectures by different educators through student exams.

• Observational study of children's playtime and self-rated happiness.

• Analyzing the effect of caffeine on attention levels.

• Measuring the relationship between ice cream sales and summer tourism in Aberystwyth.

Page 7: Limitations of Correlational Design

Key Limitations

• Correlation does not imply causation.

• Presence of potential confounding variables affecting relationships (Third variable problem).

• Emphasis on experimental designs for determining causal relationships.

Examples of Limitations

• Correlation classifications: aggression, personality traits, video gaming.

Page 8: Challenges in Correlational Analysis

Considerations

• Researcher bias in interpreting data patterns.

• Illogical patterns may emerge, complicating meaningful outcomes.

Page 9: Types of Correlational Methods

Research Approaches

• Types of correlation methods include:

  • Observation.

  • Surveys/Questionnaires.

  • Analysis of existing secondary data.

Page 10: Observational Research

Observational Techniques

• Observing behavior in natural environments.

• Different approaches:

  • Naturalistic observation: maintain distance or covert observation.

  • Participant observation: active involvement requiring consent.

Page 11: Reflection on Observation Task

Key Questions

• What did you observe?

• Important observational aspects:

  • Observational method (yes/no or counts).

  • Comparability of observations.

  • Defining subjective experiences (e.g., smile, aggression).

Page 12: Challenges of Observation

Defining Observational Challenges

• Managing behavior definitions for observation.

• Unstructured vs. structured observation:

  • Unstructured: descriptive, hard to analyze.

  • Structured: checklist or specific counts for clarity.

Page 13: Reliability and Validity in Research

Ensuring Effective Measurement

• Reliability: Stability and consistency of measures.

• Validity: Effectiveness in measuring the intended variable.

Page 14: Enhancing Reliability and Validity of Observation

Strategies

• Increasing reliability by having multiple observers (Inter-rater reliability).

• Validity enhanced through comprehensive research of target behaviors.

Page 15: Questionnaire Research

Importance of Questionnaires

• Effective tool for assessing beliefs, opinions, and attitudes.

• Development of questionnaires requires thorough research for optimal reliability and validity.

• Standardized questionnaires are preferable when available.

Page 16: Benefits of Questionnaires

Advantages

• Quick and cost-effective method of data collection.

• Ability to reach large samples for a broad spectrum of responses.

• Responses are straightforward to code and analyze.

Page 17: Challenges of Questionnaires

Limitations

• Limited response options may restrict depth of data.

• Risk of response bias complicates data accuracy.

Page 18: Sampling Considerations

Critical Sampling Factors

• Define target response group carefully.

• Prevent sampling bias due to time and location.

• Ensure comprehension of questions by participants.

• Check for honesty in responses to minimize measurement error.

Page 19: Assessing Reliability and Validity of Questionnaires

Reliability and Validity Measures

• Reliability:

  • Internal consistency tests (correlation of similar variable questions).

  • Test-retest consistency over time.

• Validity:

  • Participants’ understanding of what is being measured.

  • Expert judgment on accuracy of measurement.

  • Correlate findings with similar measure outcomes.

Page 20: Analyzing Correlational Data

Statistical Methods

• Chi-square tests for categorical data associations.

• Pearson correlation for parametric data relationships.

• Spearman correlation for non-parametric data relationships.

• Regression analysis for examining predictions based on correlations.

Page 21: Summary

Key Takeaways

• Correlational design allows exploration of variable relationships without manipulation.

• Core methods include observation, surveys/questionnaires, and existing data analysis.

• Data types influence statistical analyses based on specific research questions.