PSYC506 - Data Analysis for Multi-variable Designs

Year

Credit points

Campus offering

Prerequisites

PSYC501 Foundations of Qualitative and Quantitative Data Analysis

Unit rationale, description and aim

Psychology is the discipline devoted to the scientific study of human behaviour. As such, when training as a psychologist students are, at the most fundamental level, training as a scientist. This unit is designed to develop foundational competencies developed in PSYC501. The unit will extend the students' knowledge and practical skills to the analysis of experimental and non-experimental data in complex research questions, where more than one independent/predictor variable is included. Students will learn how to (a) conduct and interpret analysis of variance (ANOVA) and factorial ANOVA for independent groups, repeated measures and mixed designs, and (b) conduct and interpret multiple regression analysis, including standard and hierarchical approaches to model building. Students will learn to use a statistical software package to conduct all analyses and present and discuss the findings consistent with APA guidelines. The aim of this unit is to extend competencies in quantitative analysis, interpretation and reporting to multi-variable designs.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Content

Topics will include:

• One-way analysis of variance (ANOVA) (between subjects and repeated measures designs)
• Assumptions of one-way ANOVA, planned and post-hoc follow up analysis
• Non-parametric equivalents of one-way ANOVA (i.e., Kruskal-Wallis and Friedman’s ANOVA)
• Factorial ANOVA (independent groups, repeated measures, mixed designs). The assumptions of factorial ANOVA and simple effects 
• Simple linear regression, standard multiple regression and hierarchical multiple linear regression
• Assumptions of linear regression and assessment of outliers/influential cases
• Categorical variables in regression
• Use of a statistical software package (e.g., SPSS, jamovi, JASP, R) to conduct statistical techniques covered in this unit
• Statistical power and effect size related to the analyses covered in the unit
• Interpretation and reporting of results for statistical techniques covered in this unit using APA guidelines 

Learning and teaching strategy and rationale

Teaching and learning strategies utilised in this unit will support students in meeting the aims and achieving the learning outcomes relevant to this unit as well as to the broader course learning outcomes. This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline, and opportunities to practice and apply their learning in situations similar to their future professions. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous discussions, interact with peers through these discussion forums, and engage with online modules and readings via the online learning platform. Activities encourage students to bring their own examples to demonstrate understanding and application, and to engage constructively with their peers. Collaboration with peers in the online environment will support students in considering, discussing and debating the content of the unit. These learning and teaching strategies assist students in developing their knowledge and application, analysis, synthesis and evaluation of that knowledge of statistics and data analysis techniques in psychology. Students will receive regular and timely feedback on their learning, which includes information on their progress.    

Assessment strategy and rationale

In order to pass this unit, students must complete and submit all assessment tasks. In addition to this, students must obtain an aggregate mark of at least 50% to pass the unit. 

The assessment tasks have been designed to allow students to demonstrate the achievement of the learning outcomes of the unit and develop the associated graduate attributes. There are three components involved in assessment of the unit. The two data analysis reports will include an opportunity to: (a) identify the statistical analysis that is appropriate to answer specific research questions, (b) conduct said analyses using the statistical software package, and (c) report and interpret the results. One of the data analysis reports will relate to factorial ANOVA while the other relates to Multiple regression. These Data Analysis Reports require students to demonstrate their data analysis skills and understanding of how to apply, interpret and report these statistical techniques. The final exam allows students to demonstrate their understanding, consolidation and application of the content covered in the unit to a range of novel problems.

Overview of assessments

Representative texts and references

American Psychological Association (2019). Publication manual of the American Psychological Association (7th ed.). American Psychological Association.

Field, A. (2017). Discovering statistics using IBM SPSS (5th ed.). Sage Publishers.

Gravetter, F., & Wallnau, L. (2017). Statistics for the behavioral sciences (10th ed.). Cengage Learning.

Navarro, D.J. and Foxcroft, D.R. (2019). Learning statistics with jamovi: A tutorial for psychology students and other beginners (Version 0.70). DOI: 10.24384/hgc3-7p15