Lecture 1 - Generating good hypotheses, psychological measurement, & writing good survey items

Page 1: Class Overview

  • Course: PSYC 71

  • Session: Week 1 Lecture

  • Date: Wednesday, January 8th Winter 2025

Page 2: Lecture Outline

  • Topics covered:

    • About this course

    • The Scientific Method

    • Questions, Hypotheses, & Predictions

    • Psychological Measurement

    • Writing Good Survey Questions

Page 3: About This Course

  • Introduction to the basics of research in psychology.

Page 4: Meet the Teaching Team

  • Instructor: Angela Nelson Lowe, PhD

    • Hometown: San Diego, CA

    • Education: UC Irvine (BA Cognitive Science, BA Economics), Indiana University (Joint PhD in Psychology and Cognitive Science)

    • Courses Taught: Psyc 60, 71, 105, 136, 193L

    • Research Interests: Mathematical models of human memory systems, interaction of knowledge and event memory, false memory, familiar face recognition.

    • Office Hours: Wednesdays 2:00-3:00 PM in McGill 2113 (by appointment).

Page 5: The Lowe Household

  • Personal insights about the instructor's family.

Page 6: Teaching Team

  • Grad TA (Wednesday Lab): Pria Daniel (Email: pldaniel@ucsd.edu, Office Hours: Tues 10-11 AM)

  • UGIA (Wednesday Lab): Aileen Ocampo (Email: aiocampo@ucsd.edu, Office Hours: TBA)

  • Grad TA (Thursday Lab): Shirley Liu (Email: xil086@ucsd.edu, Office Hours: Thurs 2-3 PM)

  • UGIA (Thursday Lab): John Rodriguez (Email: jcr043@ucsd.edu, Office Hours: TBA)

Page 7: Course Goals

  1. Review and connect key concepts in research design and data analysis.

  2. Develop a conceptual replication/extension of a published finding in psychology.

  3. Collect and analyze data to test a proposed hypothesis.

  4. Write a complete research report in APA format and present findings.

Page 8: Course Materials

  • Software for Data Analysis:

    • Jamovi Statistical Software (link in syllabus and on Canvas)

    • Microsoft Excel (link in syllabus and on Canvas)

  • Reading Requirements:

    • No required readings

    • Some recommended FREE textbooks on statistics and research methods (links in syllabus and on Canvas).

Page 9: Weekly Expectations

  • Lectures (Wednesdays):

    • Review key concepts from PSYC 60 and PSYC 70.

    • Engage with Peer Instruction questions.

  • Lab Sessions (Wednesdays/Thursdays):

    • Focus on research skills: summarizing articles, writing literature reviews, experiment design, data analysis, etc.

  • Homework: Due every Sunday related to research.

Page 10: Homework Schedule

  1. January 12: Library Tutorial #1: How to Read an Article and Class Survey

  2. January 19: Article summary

  3. January 26: Library Tutorial #2: How to Search Databases

  4. February 2: Library Tutorial #3: How to Cite Sources

  5. February 9: Project Proposal

  6. February 16: Statistics Homework #1: t-Tests and One-Way ANOVA

  7. February 23: Statistics Homework #2: Correlation & Chi-Square

  8. March 2: Statistics Homework #3: Two-Way ANOVA & Linear Mixed Models

  9. March 9: Project Poster

  10. March 16: Final Paper

Page 11: Research Project Overview

  • Teams of 4-5 will conduct a conceptual replication or extension of a published finding in psychology.

  • Components:

    • Library tutorials (3, 3% completion, weeks 1, 3, 4)

    • Individual article summary (5%, week 2)

    • Group project proposal (10%, week 5)

    • Group poster (5%, week 9)

    • Group presentation (2%, week 10)

    • Individual final paper (15%, week 10)

Page 12: Grading Scheme

  • Participation: SONA participation (3%), Peer Instruction (5%), Lab Attendance & Participation (5%)

  • Statistics Homework: (12%, 3 assignments worth 4% each)

  • Research Project: (40%, comprising various components)

  • Exams: (35%, 2 midterms, cumulative final)

Page 13: Syllabus Review

  • Syllabus available on Canvas for detailed review.

Page 14: Getting Started in Research

  • Introduction to foundational research concepts in psychology.

Page 15: Psychological Science

  • Defined as the systematic, iterative process of hypothesizing, predicting, and observing psychological phenomena to generate new knowledge.

  • Process: Data ➔ Theory/hypothesis ➔ Interpretation ➔ Measurable situation.

Page 16: The Scientific Method

  1. Pose a question.

  2. Formulate a hypothesis.

  3. Generate a prediction.

  4. Make systematic observations.

  5. Interpret observations to support or revise hypothesis.

  6. Return to step 1.

Page 17: Characteristics of a Good Hypothesis

  1. Logical: Follows from premises.

  2. Empirically Testable: All variables can be observed/measured.

  3. Refutable: Can be proven false.

  4. Positive: Proposes existence of something.

  5. Specific: Generates testable predictions for specific situations.

Page 18: Generating Testable Predictions

  • Research Question: Asks about the relationship between variables (broad/narrow).

    • Using two or more variables and examining the relationship between them

  • Hypothesis: Specifies what we think that relationship is

    • good study identifies at least two plausible alternative hypotheses

  • Prediction: Describes expected observations if the hypothesis is accurate (if-then statements).

Page 19: Example: Weather, Mood, & Location

  • Research Question: Does the relationship between weather and mood depend on location?

  • Specific Inquiry: Emotional responses to rain in Southern vs. Northern California.

  • Hypotheses & Predictions propose expected outcomes based on mood fluctuations during extended rain.

Page 20: Designing Testable Predictions

  • Collaborative activity to create research question, hypothesis, and prediction for a sample of UCSD students.

    • Peer Instruction Activities: Develop broad and specific questions, plausible hypotheses, and predictions in if-then format.

Page 21: Review Peer Instruction Results

  • Analysis discussions focused on formulated Research Questions, Hypotheses, and Predictions.

Page 22: Psychological Measurement

  • Measurement Definition: Systematic procedure for assigning scores/values representing individual characteristics.

Page 23: Measurement in Psychology

  • Constructs are indirect representations of interests, operational definitions vary.

    • A lot of what we know about psychology is not directly observable

    • variables - things we can measure that tell us indirectly about constructs

Page 24: Constructs & Variables

  • Framework of hypothesis and prediction measured in the empirical plane.

Page 25: Importance of Operational Definitions

  • Clarification on how constructs are measured via operational definitions.

Page 26: Types of Measures

  1. Self-report: Participants report own thoughts/behaviors.

  2. Behavioral: Researcher observes participant behaviors.

  3. Physiological: Records physiological variables.

Page 27: Stevens’ Theory of Measurement Scales

  • Types of Scales:

    • Nominal: Category labels. (colors)

    • Ordinal: Ordered scores. (1st and 2nd place)

    • Interval: Equal units. (temperature)

      • Kelvin is not an interval scale because 0 kelvins is important as it represents absolute zero, the point at which molecular motion ceases, making it a ratio scale instead.

    • Ratio: Meaningful zero point.

Page 28: Scales of Measurement Summary

  • Overview of measurement scales regarding observations and interpretations.

Page 29: Scales of Measurement Continued

  • Comparison table of scales for different measurement aspects.

Page 30: Peer Instruction Example

  • Scenario analyzing measurement types:

    • Weight in the context of university students’ health habits, determining the scale of measurement.

Page 31: Types and Scales of Measurement

  • Self-report, Behavioral, Physiological measures assessed based on quality.

Page 32: Peer Instruction on Measurement Assessment

  • Outgoing personality measurement options and analysis of better measurement.

Page 33: Writing Good Survey Items

  • Focus on the design of effective survey questions and measurement quality.

Page 34: Survey Responding as a Cognitive Process

  • Example of crafting questions regarding alcohol consumption to gather behavior patterns.

Page 35: Criterion for Good Survey Design

  • BRUSO Criteria: Brief, Relevant, Unambiguous, Specific, Objective.

Page 36: BRUSO Recap

  • Illustrative examples demonstrating effective vs. ineffective survey questions.

Page 37: Closed vs. Open-ended Questions

  • Open ended questions - allow respondents to to answer ho they choose

    • good for gathering rich and detailed information

    • can be tedious to code and anlayze data

  • closed-ended questions - limit possible answers participants can give by asking them to check boxes, use a rating scale, or provide a numerical answer

    • better when the construct or variable is well defined and oyu have a clear idea about the types of responses people are likely to give

Page 38: Peer Instruction on Survey Questions

  • Group activity to create survey questions in varying scales and assess against BRUSO.

    • How many hours on average do you sleep per day?

    • On average what do you receive on exams?

Page 39: Example of Research Questions and Predictions

  • Detailed outline of testing sleep and academic performance with examples of survey questions.

Page 40: Upcoming Tasks

  • Canvas quiz due Sunday for project topic selections with pertinent Seed Articles posted for reference.

Page 41: Next Week's Focus

  • Weekly lab emphasizing Excel and Jamovi; lecture addressing distributions and sampling methods.