09_Lecture_for_PSYC2018H_F24

Chapter 9: Non-Experimental Design I: Survey Methods

Chapter Objectives

• Explanation of the significance of sampling in survey research compared to other psychology research.

• Principles of good survey construction to ensure data validity.

• Problems encountered in interpreting survey data.

• Four methods of collecting survey data along with their advantages and disadvantages.

Probability Sampling

Varieties of Probability Sampling

• Simple Random Sampling: Every individual has an equal chance of being selected. Used to generalize findings to a larger population.

• Stratified Sampling: Population divided into strata (groups) and random samples taken from each stratum. Useful for ensuring representation across major characteristics.

• Cluster Sampling: Population divided into clusters (often geographically) and entire clusters are randomly selected. Cost-effective and efficient for large populations.

Correlation

Types of Correlations

• Positive Correlation: Both variables increase together (illustrated through scatterplots).

• Negative Correlation: One variable increases as the other decreases.

• Coefficient of Determination (r²): Measurement of the proportion of variability in one variable that can be explained by the other.

Regression Analysis

• Simple Linear Regression: Predicting scores using a single predictor variable. Formula: Y = a + bX (where Y is the predicted value, X is the predictor).

• Multiple Regression: Predicts scores using multiple variables, exploring the interaction between predictors.

Directionality Issues

Challenges in Correlation Interpretation

• Directionality Problem: Difficulty determining which variable is the cause and which is the effect.

• Cross-Lagged Panel Study: Helps ascertain the temporal order of variables, addressing directionality issues.

• Third Variable Problem: Other variables might contribute to the observed relationship. Evaluated through partial correlation.

Survey Research: Historical Context

• Darwin: Study of facial expressions as a means to understand emotion.

• Galton: Questions regarding the innate aspect of scientific interests.

• Titchener & James: Issues with methodology in psychological sampling.

Sampling Issues in Survey Research

• Bias vs. Representation: Importance of ensuring representative samples to avoid biases that can affect results and interpretations.

• Non-Probability vs. Probability Sampling: Differences and implications for survey outcomes.

• Self-Selection Bias: Historical example of Literary Digest's failed election predictions due to sampling bias (subscribers + ownership of phones).

Types of Survey Questions

• Open-ended Questions: Allow detailed responses, but are harder to analyze.

• Closed Questions: Easier to analyze, but can limit responses.

• Likert Scales: Useful for gauging attitudes but may lead to response biases.

• Demographic Information: Should be included at the end of surveys to avoid influencing responses.

Creating Effective Surveys

• Wording and Clarity: Important to avoid ambiguity and leading questions.

• Pilot Study: Helps identify potential issues in survey questions before widespread deployment.

Data Collection Methods

In-Person Interviews

• Pros: Comprehensive understanding, follow-ups possible.

• Cons: High cost, interviewer bias, potential for unrepresentative samples.

Mailed Surveys

• Pros: Ease of scoring, lower cost compared to in-person.

• Cons: Nonresponse bias, low rates of return.

Phone Surveys

• Pros: Cost-effective, efficient.

• Cons: Must be brief; risk of SUGging (selling under the guise of surveying).

Electronic Surveys

• Pros: Efficiency, low cost.

• Cons: Potential sampling issues, ethical concerns in data collection.

Analyzing Data

Correlation and Prediction

• Correlation techniques allow researchers to find relationships between variables without inferring causation.

• Scatterplots: Visual representation of data relationships.

Regression Predictions

• Regression Analysis: Helps in making predictions about one variable based on another; essential for understanding predictor effects on outcomes.

Directionality and Third Variables

Interpretation of Correlational Results

• Mediators vs. Moderators: Distinction between variables that explain how and why relationships exist versus those that highlight the conditions under which relationships exist.

• Importance of thorough analysis of variable interactions when interpreting results.

Summary

• Surveys are essential tools for gathering data on attitudes, beliefs, and projected behaviors.

• Careful survey design and analysis techniques such as correlation and regression are vital for understanding data.

• Researchers must remain vigilant about directionality challenges and third variable influences throughout their analysis of non-experimental data.