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Questions and Answers
What does a Pearson's correlation coefficient of 0.409 indicate about the relationship between the variables?
What does a Pearson's correlation coefficient of 0.409 indicate about the relationship between the variables?
If $r^2$ equals 0.36, what percentage of the variability in one variable is explained by the other variable?
If $r^2$ equals 0.36, what percentage of the variability in one variable is explained by the other variable?
What is the main purpose of the regression process in data analysis?
What is the main purpose of the regression process in data analysis?
How do outliers affect the results of Pearson's correlation?
How do outliers affect the results of Pearson's correlation?
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In the regression equation $Y = a + bX$, what does 'Y' represent?
In the regression equation $Y = a + bX$, what does 'Y' represent?
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What is the main issue with self-selection bias in survey research?
What is the main issue with self-selection bias in survey research?
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Which of the following is a key characteristic that distinguishes positive from negative bivariate correlations?
Which of the following is a key characteristic that distinguishes positive from negative bivariate correlations?
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What advantage does multiple regression analysis have over simple linear regression?
What advantage does multiple regression analysis have over simple linear regression?
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Which of the following is NOT a method of collecting survey data?
Which of the following is NOT a method of collecting survey data?
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What is the function of calculating the coefficient of determination ($r^2$) in regression analysis?
What is the function of calculating the coefficient of determination ($r^2$) in regression analysis?
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What challenge does directionality present in interpreting correlations?
What challenge does directionality present in interpreting correlations?
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Which type of sampling is likely to lead to a representative sample for survey research?
Which type of sampling is likely to lead to a representative sample for survey research?
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How do mediators differ from moderators in the context of third variables in correlation analysis?
How do mediators differ from moderators in the context of third variables in correlation analysis?
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What is the role of the slope (b) in a regression line?
What is the role of the slope (b) in a regression line?
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In regression analysis, what is meant by a mediator variable?
In regression analysis, what is meant by a mediator variable?
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What major issue does the directionality problem highlight in correlational research?
What major issue does the directionality problem highlight in correlational research?
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Which of the following statements regarding survey data is accurate?
Which of the following statements regarding survey data is accurate?
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When conducting multiple regression, which of the following is true about predictor variables?
When conducting multiple regression, which of the following is true about predictor variables?
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In the context of correlational results, what does the concept of 'shared variance' refer to?
In the context of correlational results, what does the concept of 'shared variance' refer to?
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Which outcome can result from the concept of moderation in a relationship between two variables?
Which outcome can result from the concept of moderation in a relationship between two variables?
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What is a characteristic of non-experimental research?
What is a characteristic of non-experimental research?
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What problem might arise when using Likert scales in surveys?
What problem might arise when using Likert scales in surveys?
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Which of the following describes a key feature of negative correlation?
Which of the following describes a key feature of negative correlation?
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In survey design, why is it important to avoid asking two things in one question?
In survey design, why is it important to avoid asking two things in one question?
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What is a primary disadvantage of using mailed written surveys?
What is a primary disadvantage of using mailed written surveys?
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Which statement correctly describes the use of scatterplots in data analysis?
Which statement correctly describes the use of scatterplots in data analysis?
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What is the purpose of using a pilot study in survey design?
What is the purpose of using a pilot study in survey design?
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Which type of correlation coefficient is suitable for ranking data?
Which type of correlation coefficient is suitable for ranking data?
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What information is typically collected at the end of a survey?
What information is typically collected at the end of a survey?
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What must be considered when utilizing phone surveys?
What must be considered when utilizing phone surveys?
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Which of the following represents an advantage of electronic surveys?
Which of the following represents an advantage of electronic surveys?
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Study Notes
Chapter 9: Non-Experimental Design I: Survey Methods - Chapter Objectives
- Understand why sampling issues are more important in survey research compared to other psychology research.
- Understand the principles of good survey design.
- Recognize potential problems that make interpreting survey data challenging.
- Identify four methods for collecting survey data and their respective advantages and disadvantages.
Chapter Objectives
- Describe three types of probability sampling and their appropriate contexts.
- Differentiate between positive and negative bivariate correlations and visually represent them using scatterplots.
- Calculate and interpret the coefficient of determination (r²).
- Explain how regression analysis assists in prediction and distinguish between simple and multiple linear regression techniques.
Chapter Objectives
- Understand how directionality affects the interpretation of correlations and how cross-lagged panel studies address this issue.
- Recognize and control for the third-variable problem using partial correlation procedures.
- Differentiate between mediators and moderators in the context of third variables in correlations.
Survey Research - Learning From History
- Darwin's work on facial expressions of emotion (leading, overly specific).
- Galton's inquiries into the origins of scientific interests (huh-type questions; "How far do your scientific tastes appear to be innate?").
- Hall's research on children's minds (methodological issues).
- Titchener & James' work (flames for the pests).
Survey Research - Sampling
- Sampling issues in survey research: biased vs. representative samples, non-probability vs. probability sampling (refer to Chapter 4).
- Self-selection bias (election of 1936 example, Literary Digest; subscribers + phones and cars; demand characteristics).
- Surveys < Psychological Assessment (attitudes, opinions, beliefs, projected behaviors vs. psychological functioning).
Types of Survey Questions
- Open-ended vs. closed questions ("Do you like this example?", "the most important problem" question).
- Use of Likert scales (avoiding response bias).
- Assessing memory and knowledge.
- Moderate use of "don't know" (DK) alternative.
- Adding demographic information (basic identifying data like age and income) at the end of a survey.
Creating an Effective Survey
- Survey wording: avoid ambiguity (pilot studies help); "Research methods can be fun."
- Don't ask two things in one question ("Do you want America to be great again by getting rid of all the environment? in this country").
- Avoid biased and leading questions ("Do you beat your students less often?").
Collecting Survey Data
- In-person interview surveys (e.g., Kinsey): advantages (in-person, comprehensive, follow-ups possible), disadvantages (representative samples, cost, logistics, interviewer bias).
- Mailed written surveys: advantages (in-person, ease of scoring), disadvantages (cost, response rate (nonresponse bias), social desirability bias).
- Phone surveys: advantages (cost, efficiency), disadvantages (must be brief, response rate, SUGging).
- Electronic surveys: advantages (cost, efficiency), disadvantages (sampling issues, ethics).
Analyzing Data from Non-Experimental Designs - Correlation
- Correlation describes relationships without implying causality.
- Correlation techniques determine the degree of relationship between variables.
- Types of correlations: positive, negative, no correlation.
Analyzing Data from Non-Experimental Designs - Positive Correlation
- Example: Higher study hours correlate with higher GPAs.
- Data presented in table format (study hours and GPAs for multiple students).
Analyzing Data from Non-Experimental Designs - Negative Correlation
- Example: Higher goof-off hours correlate with lower GPAs.
- Data presented in table format (goof-off hours and GPAs for multiple students).
Analyzing Data from Non-Experimental Designs - Scatterplots
- Visual representations of relationships between variables.
- One variable on the X-axis, another on the Y-axis.
- Examples (graph images included) illustrate weak/strong positive and negative correlations, and no correlation.
Analyzing Data from Non-Experimental Designs - Scatterplots (Fig. 9.2)
- Creating a scatterplot from data.
- Each point represents an individual subject.
- Data in table form showing Variable A and Variable B for different subjects.
Analyzing Data from Non-Experimental Designs - Scatterplots (Fig. 9.3)
- Scatterplots of hypothetical GPA data for positive and negative correlations.
- Visual presentation of the relationships (weak/strong).
Analyzing Data from Non-Experimental Designs - Correlation Coefficients
- Measuring the strength and direction of relationships (Pearson's r, Spearman's rho, phi coefficient).
- Numerical value represents strength; closer to ±1.00 indicates stronger correlation.
- Sign (+ or -) indicates direction (positive or negative).
Analyzing Data from Non-Experimental Designs - Coefficient of Determination (r²)
- Proportion of variability in one variable explained by another.
- Example: r² = .36 means 36% of variability in one variable is explained by the other; 64% attributable to other factors.
Analyzing Data from Non-Experimental Designs - Assignment to Test Correlation
- Example data showing Pearson's r and p-value for an assignment.
- Visual representation of the correlation (scatterplot).
Analyzing Data from Non-Experimental Designs - Outliers
- Outliers can affect correlation coefficients.
- Potential for impacting Type I errors (false positive results).
Analyzing Data from Non-Experimental Designs - Regression
- Predicting scores and assessing accuracy.
- Regression line summarizing relationships on a scatterplot.
- Equation: Y = a + bX (Y = criterion variable, X = predictor variable; a = Y-intercept, b = slope).
- Using predictor variable to predict criterion variable.
Analyzing Data from Non-Experimental Designs - Regression Lines (Fig. 9.4)
- Regression lines for GPA scatterplots, showing how study time and goof-off time predict GPA.
Analyzing Data from Non-Experimental Designs - Regression (Example)
- Example using passion for studying as predictor variable and academic engagement/burnout as criterion variables. (linear/multiple regression).
Analyzing Data from Non-Experimental Designs - Interpreting Correlational Results
- Understanding the directionality problem (possible causal directions of variables A and B).
- Examples (TV preference and aggression).
- Cross-lagged panel techniques (correlation at different time points).
Analyzing Data from Non-Experimental Designs - Moderating and Mediating Variables
- Mediator variables explaining how and why relationships exist.
- Moderator variables specifying conditions under which relationships occur (e.g., alcohol use and risky sex with impulse control as mediator).
Summary
- Surveys gather information on attitudes, beliefs, opinions, and behaviors.
- Researchers should develop clear questions and use appropriate survey methods.
- Correlation and regression analyze non-experimental data, like survey data.
- Consider directionality and potential third variables in interpreting non-experimental research.
- Non-experimental research can be exploratory prior to experiments.
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Description
This quiz covers key concepts in survey methods within psychology, focusing on sampling issues, survey design principles, and the challenges of interpreting survey data. It also explores various data collection methods and the types of probability sampling appropriate for different contexts, alongside correlation and regression analysis techniques.
