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PSYC206 Research Design and Statistics II

Teaching organisation

3 contact hours per week over 12 weeks or equivalent.

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 one of three units in the APAC accredited sequence designed to develop foundational competencies in research methods and statistics, as well as on the appropriate values and ethical principles underlying research in psychology. The unit continues the students' training in research design and statistical analysis, which is part of the research toolbox of psychologists, both as researchers themselves, and as practitioners. Like PSYC206, the unit will teach statistical analysis techniques in the context of the research design in which they are used. 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) critically evaluate the internal validity of research studies, (b) conduct and interpret factorial analysis of variance for independent groups, repeated measures and mixed designs, and (c) conduct and interpret multiple regression analysis, including standard and hierarchical forms of model building. In achieving objectives (b) and (c), students will learn to use a statistical software package (e.g., SPSS, jamovi, JASP, R) to conduct all analyses.

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.

On successful completion of this unit, students should be able to:

LO1 - Differentiate experimental and non-experimental designs and compare the implications of using one versus the other. Recognise the need to consider ethical principles when conducting research, as well as to balance resource availability and external validity (GA1, GA3, GA4, GA5);

LO2 - Identify potential threats to internal validity and determine the implications for the design of experiments and the interpretation of data stemming from experimental research (GA4, GA5, GA8, GA10);

LO3 - Evaluate complex factorial models with more than one independent variable and judge the appropriateness of each design to answer specific research questions (GA4, GA5, GA8, GA10);

LO4 - Conduct and interpret factorial analyses of variance, using a statistical software package (e.g., SPSS, jamovi, JASP, R), for the case of between-subjects, repeated-measures and mixed designs with more than one independent variable. Identify when an analysis of simple main effects is required to determine the source of interaction effects, and conduct these analyses (GA4, GA5, GA8, GA10);

LO5 - Recognise research questions that require the use of multiple linear regression models and identify the appropriate technique (GA4, GA5, GA8);

LO6 - Conduct and interpret multiple linear regression analyses, using a statistical software package (e.g., SPSS, jamovi, JASP, R). Conduct preliminary data screening and assumption testing. (GA4, GA5, GA8, GA10).

Graduate attributes

GA1 - demonstrate respect for the dignity of each individual and for human diversity 

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.


Topics will include:  

  • Distinction between experimental, quasi-experimental and non-experimental designs for the case with more than one independent/predictor variable 
  • Judging the quality of research studies: internal and external validity 
  • Revision of one-way ANOVA, Factorial designs  
  • Factorial Analysis of Variance (IG, RM, mixed) 
  • Revision of correlation and simple linear regression 
  • Standard multiple regression and hierarchical multiple linear regression 
  • The 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 
  • Interpretation and reporting of results for statistical techniques covered in this unit

Learning and teaching strategy and rationale

This unit is primarily delivered face-to-face. Students have 3 contact hours per week which involve a 2 hour lecture and a 1 hour tutorial. Some lectures may be delivered online (or partly online) with the face-to-face time devoted to activities designed to consolidate problem solving skills. The lectures will introduce students to the content of the unit and are designed to facilitate understanding of the main concepts of the analyses under study. The tutorial program is designed to provide practical skills in the conduct and interpretation of the analysis taught in the lecture. In particular, the tutorials provide training in the use of statistical software package (e.g., SPSS, jamovi, JASP, R), the interpretation of the software’s statistical output, and the write up of results. In addition to the data analysis exercises completed during tutorial time, students are provided with practice weekly exercises to complete in their own time and for which the answers are provided a few days later to assist in self-assessment of performance.

Assessment strategy and rationale

In order to successfully complete this unit, students need to complete and submit all of the 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. First, a research critique assignment requires students to read, reflect on and write a critique of a research study. Second, a data analysis report 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. The final exam allows students to demonstrate their understanding, consolidation and application of the content covered in the unit.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Research critique:  

Students will evaluate a research study, thus demonstrating their understanding of research methodology.


LO1, LO2, LO3

GA1, GA3, GA4, GA5, GA8, GA10

Data analysis report:  

Students will be required to identify, conduct, interpret and report the results of a statistical analysis that is appropriate for a specified research question. This task enables students to demonstrate the ability to apply the knowledge acquired in this unit.


LO4, LO5, LO6

GA4, GA5, GA8, GA10

End of semester exam:  

Students will be required to demonstrate an understanding of the main constructs discussed throughout this unit.


LO1, LO3, LO4, LO5, LO6

GA1, GA3, GA4, GA5, GA8, GA10

Representative texts and references

Allen, P., Bennet, K., & Heritage, B. (2019). SPSS Statistics: A Practical Guide (4th ed). Cengage.

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

Cozby, P. C. & Bates, S. (2019). Methods in behavioral research (14th ed). McGraw-Hill.  

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

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage.

Navarro, D.J. Learning statistics with R: A tutorial for psychology students and other beginners (Version 0.60). Freely available:

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

Navarro, D.J., Foxcroft, D.R., & Faulkenberry, T.J. (2019). Learning statistics with JASP: A tutorial for psychology students and other beginners. Freely available:

Smith, R. A. (2013). The psychologist as detective: An introduction to conducting research in psychology (6th ed). Pearson.

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