RB

PSY173 Week 9 Notes

Acknowledgment of Country

  • Murdoch University is situated on the lands of the Whadjuk and Binjareb Noongar people.
  • Acknowledgment of Noongar elders' enduring culture and contributions.
  • Emphasis on Murdoch University's role in a long tradition of learning.

Goals of the Session

  • Understand survey design.
  • Understand the correlational approach.
  • Reading assigned: Chapter 7 of "Discovering the Scientist Within".
  • Quiz 3 will relate to content from Chapter 7 and this session.

PART 1: Understanding Survey Design

Surveys

  • Definition: A quantitative research strategy for systematically collecting information from individuals, generalizing it to a larger population.
  • Key Benefits:
    • Collects large amounts of data quickly.
    • Correlation is often used in survey research.

Psychometric Tests

  • Definition: Psychometric implies a measure of the mind.
  • Uses: Quantification of psychological characteristics.
  • Types of information measured:
    • Personality traits.
    • Mental disorders.
    • Attitudes.
    • Intelligence.

Questionnaire Design Considerations

  • Important questions to consider:
    • How many items?
    • Which items?
    • What response format?
    • Item ordering?
    • Phrasing of items?
    • Minimizing response biases?
    • Instructions provided?

Item Design Considerations

  • Avoid:
    • Leading questions (e.g., “Wouldn’t you say that…”).
    • Jargon and colloquialisms.
    • Double-barreled questions (e.g., “Do you enjoy heavy metal and ballet?”).
    • Double negatives (e.g., “Is it important to never stop never stopping?”).

Process for Designing a Questionnaire

  • Important iterative steps:
    • Operationalize your construct.
    • Conduct interviews, focus groups, and observations.
    • Identify key themes.
    • Create items based on informed understanding.
    • Pilot the questionnaire.
    • Test reliability and validity, then refine items as necessary.

Response Bias

  • Common biases include:
    • Yeah-saying: A tendency to give similar responses across questions.
    • To minimize:
    • Vary item phrasing.
    • Alternate positive and negative phrased items.
    • Error of central tendency: Participants may avoid using extreme responses.
    • Address by using a wider response scale (5-9 points preferred).

Types of Response Formats

  1. Yes/No Responses: Clear binary format for questions.
  2. Likert Scales: Measure level of agreement with a statement.
  3. Semantic Differential Scales: Measure attitudes using opposites.
  4. Ranking Statements: Rank statements by importance.
  5. Open-ended Specific Questions: Allow for detailed responses.

Scoring a Questionnaire

  • Basic scoring procedures include:
    • Assign numerical values to responses (e.g., Yes=1, No=0).
    • Reverse scoring for certain items.
    • Combine scores for overall constructs.
    • Presentation of scores can include summed totals or averages for clarity.

Normative Values

  • Comparison of individual scores to group scores based on:
    • Sample demographics (e.g., age, gender).
    • Establish mean scores and range (high/low).

PART 2: Understanding Correlational Approach

Overview of Correlation

  • A research approach investigating relationships between variables.
  • It's crucial to remember correlation does not imply causation (e.g., ice cream sales vs shark attacks).

Selection of Statistical Tests

  • Determining the appropriate test based on the nature of variables (nominal, ordinal, interval/ratio).
  • Examples of tests:
    • Correlation Tests: Pearson's for interval/ratio, Spearman's for ordinal.
    • Chi Square for nominal data.

Correlation Examples

  • Examples of correlational studies include relationships between:
    • Happiness and income.
    • Urbanization and mental health concerns.
    • Trust levels and voting behavior.

Scatterplots and Direction of Association

  • Scatterplots visually represent relationships:
    • Positive correlation: Both variables increase together.
    • Negative correlation: One variable increases while the other decreases.

Pearson's Correlation Coefficient

  • Measures the strength and direction of a linear relationship between two interval/ratio variables, denoted as r.
  • Interpretation:
    • Range from -1 (perfect negative) to +1 (perfect positive).
    • 0 indicates no correlation.
    • The closer to -1 or +1, the stronger the relationship.

Assumptions of Correlation Coefficients

  • Must be on a continuous scale.
  • Ensure no outliers are present.
  • There should be a linear relationship between variables.

Reporting the Results

  • Example:
    • "The mean attendance score was 6.40 (SD = 2.61), with lab report marks averaging at 4.27 (SD = 2.99). There is a significant negative correlation (r(28) = -0.93, p < .001), indicating that higher attendance correlates with lower marks."