PSY WEEK 1 LECTURE

ACU Overview

Institution:

Australian Catholic University (ACU)

Course:

PSYC311: Research Design and Statistics III

Week 1 Lecture

Teaching Staff

• Lecturer: Dr. Tayla Kapelles

  • Email: tayla.kapelles@acu.edu.au

  • Office: Daniel Mannix Building, Room 5.28

  • Consultation: By appointment, ensuring personalized guidance and feedback for students.

• Tutors:

  • Tayla Kapelles

  • Alena Bondarchuk-McLaughlin

    • Email: alena.bondarchuk-mclaughlin@acu.edu.au

Course Outline

Weekly Topics

1. Introduction to PSYC311: Overview of course objectives and expectations.

2. Judging Research Quality: Validity: Exploring different types of validity (internal, external, construct).

3. Review of Correlation and Simple Regression: Fundamental statistical concepts to analyze relationships between variables.

4. Multiple Regression I - IV: In-depth understanding of multiple regression analysis, focusing on different complexities and applications.

5. Review of One-way ANOVA: Fundamental ANOVA concepts, when to use one-way ANOVA.

6. Factorial ANOVA 2: Post-hoc Tests, Simple Effects: Understanding post-hoc tests and their application in determining specific group differences.

7. Factorial ANOVA 3: Repeated Measures ANOVA, Intro to Mixed: Advanced analysis techniques demonstrating how to handle repeated measures.

8. Factorial ANOVA 4: Mixed: Understanding mixed designs that incorporate both between and within subjects.

9. Review and Revision for Exam: Comprehensive review sessions to prepare students for assessments.

Lecture and Tutorial Format

Lectures Include:

• Theory of analyses: In-depth discussion of statistical theories and methodologies.

• Overview of analyses in jamovi: Practical demonstrations of using jamsment Overview

• Assignments: Two assignments and a final exam that evaluate students' understanding and application of course concepts.

  • Research Critique (25%): Review and critique a published study, identifying limitations and areas for improvement. (Length: 1000 words)

  • Data Analysis Report (35%): Analyze data for two research

The Scientific Method

• Key Elements:

  • Data Analysis: Involves identifying, operationalizing variables, and applying descriptive and inferential statistics to derive conclusions.

  • Characteristics of Scientific Theory: Explains that a scientific theory must be falsifiable, supported by independent evidence, comprehensive, accommodating new data, and ultimately parsimonious.

    • Helps to translate theory into testable hypotheses, connecting theoretical frameworks with practical research questions.

Quality of Research Design

• Highlights the essential role of statistics as a fundamental tool in research, while underscoring that methodology determines whether the data adequately answers research questions.

Research Methodology Concepts

• Discusses variable manipulation with specific examples, such as exploring how the quality of breakfast affects concentration levels.

• Aims for clear definitions of dependent and independent variables, emphasizing the necessity for robust methods to ensure valid conclusions.

Types of Designs

• Non-Experimental: Lacks manipulation or control, primarily correlational studies that assess pre-existing relationships.

• Quasi-Experimental: Involves manipulation of one independent variable with some control but does not include random assignment, impacting causal inference.

• Experimental: Incorporates manipulation of one independent variable with comprehensive control, allowing for causal inferences to be drawn.

Design Methodology

• Between vs Within Subjects Design:

  • Between Subjects: Different participants are assigned to each condition to compare results.

  • Within Subjects: The same participants experience all conditions, enhancing the study's power.

  • Mixed Designs: Combines both approaches to leverage the strengths of each design type.

Increasing Complexity in Designs

• Discusses the introduction of multiple independent variables (IVs), allowing for more nuanced understanding of relationships while increasing analytical complexity, necessitating careful calculation of main effects and interaction effects.

Conclusion

Understanding research design and methodology is critical for effective psychological research, enabling researchers to construct and analyze studies that contribute valuable insights to the field.