Program Overview

Unit Overview

The unit PSYC226 - Research Design and Data Analysis 2 is part of the Australian Catholic University's (ACU) psychology program. This unit continues students' training in research design and statistical analysis, building on the foundational knowledge developed in PSYC110 Research Design and Data Analysis I.

Unit Rationale, Description, and Aim

Psychology is the scientific study of human behavior and mental processes. This unit expands students' knowledge and understanding of basic principles of research design and statistical analysis using statistical software packages like SPSS, jamovi, JASP, or R. It focuses on the simplest case of research involving one dependent variable (or outcome) and one independent (or predictor) variable, covering single factor designs and the associated statistical technique of one-way analysis of variance (ANOVA), as well as correlational designs using simple linear regression. The unit also introduces qualitative methods, including data collection and assessing the credibility, rigor, and trustworthiness of qualitative data analysis.

Campus Offerings

The unit is offered at two campuses:

• Melbourne
• Strathfield

Prerequisites

To enroll in this unit, students must have completed either PSYC104 Research Design and Statistics I or PSYC110 Research Design and Data Analysis 1.

Incompatible Units

PSYC206 Research Design and Statistics II is incompatible with this unit.

Learning Outcomes

Upon successfully completing this unit, students will be able to:

1. Distinguish between different research methods and research designs employed in psychological research, identifying the strengths and shortfalls of qualitative and quantitative designs.
2. Demonstrate an understanding of the key theoretical principles of different analyses, including ANOVA, regression, non-parametric tests, and qualitative data analysis.
3. Conduct, interpret, and report an ANOVA using a statistical software package, including distinguishing between planned and post-hoc follow-up tests and identifying appropriate methods for controlling the Type 1 error rate.
4. Identify situations when the assumptions of parametric tests have been violated and recognize when specific non-parametric tests are required.
5. Conduct simple linear regression analyses using a statistical software package, interpret the results, and make predictions from these analyses.

Content

The unit covers topics including:

• Interpretation and reporting of results for statistical techniques
• Experimental, quasi-experimental, and non-experimental approaches to research
• Research design using an Indigenous Research Framework
• Non-parametric equivalents of independent groups and repeated measures t-tests and Pearson's correlation
• One-way ANOVA (between subjects and repeated measures designs)
• Assumptions of one-way ANOVA
• Non-parametric equivalents of one-way ANOVA
• Chi-square
• Simple linear regression
• Qualitative methods (design, data collection, and credibility)
• Use of a statistical software package to conduct statistical techniques

Assessment Strategy and Rationale

To successfully complete this unit, students must complete and submit all assessment tasks, obtain an aggregate mark of at least 50%, and demonstrate achievement of each learning outcome. The assessment tasks include:

1. A mid-semester examination to provide timely feedback on students' progress.
2. Data analysis reports to develop skills in using a statistical software package for analysis and reporting results in a research report format.
3. An end-of-semester examination to assess students' mastery of the unit content.

Learning and Teaching Strategy and Rationale

The unit has three contact hours per week over twelve weeks, involving lectures and tutorials. Lectures introduce students to the content, while tutorials provide practical skills in conducting and interpreting analyses. The use of a statistical software package is a key component of the tutorials.

Representative Texts and References

Key texts include:

• American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.).
• Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.).
• Gravetter, F., & Wallnau, L. (2017). Statistics for the behavioral sciences (10th ed.).
• Howitt. (2019). Introduction to qualitative research methods in psychology: Putting theory into practice (4th ed.).
• Navarro, D.J. & Foxcroft, D.R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners.
• Rigney. (1999). Internationalization of an Indigenous anticolonial cultural critique of research methodologies: A guide to Indigenist research methodology and its principles.

Locations and Credit Points

The unit is available at the Melbourne and Strathfield campuses and is worth 10 credit points.