PSYC424 - Research Methods

Year

Credit points

Campus offering

Prerequisites

Unit rationale, description and aim

This unit continues the training in the research skills and competencies underpinning not only the discipline of psychology but also evidence based practice. The unit is designed to extend the knowledge and skills in research methods developed throughout the three-year undergraduate degree. It provides you with research and analytical skills to support your own research projects, as well as your later careers in psychology and/or other fields. This unit covers issues of research design in the context of the statistical tools used to analyse quantitative research data. In addition to this, a series of univariate and multivariate data analysis techniques are introduced, and you will learn to conduct these analyses using SPSS, to interpret the output of said analyses, and to write up reports of the results, including interpretation of their meaning in the context of the research question they address. Emphasis will be placed on the importance of reporting effect size estimates and the confidence intervals around them and of not focusing exclusively on significance testing. As such, the aim of this unit is to provide you with advanced knowledge of statistical analysis and skills in conducting, interpreting and reporting those 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 - demonstrate an understanding of the strengths and limitations of the use of null hypothesis significance testing, and its implications for the “scientific crisis” in psychology (GA4, GA5); 

LO2 - identify the most appropriate statistical data analysis technique for data stemming from various research designs, including univariate and multivariate designs (GA4, GA5, GA8); 

LO3 - use SPSS to conduct data screening, assumption testing, and relevant statistical analyses. (GA4, GA5, GA8, GA10); 

LO4 - interpret and report the results from all types of analyses taught in the unit, adhering to standard practice and APA guidelines for reporting. (GA4, GA5, GA8); 

LO5 - demonstrate critical and analytical thought in relation to the interpretation of the results of statistical analysis (GA4, GA5, GA8). 

Graduate attributes

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.

Content

Topics will include: 

1. Principles of research design including experimental, quasi-experimental, and non-experimental approaches. The implications of each research approach for data analysis and interpretation of results will be explored. 
2. Review of hypothesis testing and the related concepts of effect size and power, as well as its implications for the critical analysis of journal articles and for the so called “scientific crisis” in psychology. 
3. Data screening procedures, including missing value analysis and assessing properties of univariate distributions and bivariate relationships. 
4. Statistical techniques including a review of previously studied statistical procedures. Students will be introduced to more advanced univariate and multivariate techniques (e.g. logistic regression analysis, factor analysis, multivariate analysis of variance, discriminant function analysis, etc). 
5. Use of SPSS for the conduct of all the analysis covered in the unit, including assumption testing. 

Learning and teaching strategy and rationale

The unit is primarily delivered face-to-face, with 3 contact hours per week. These three contact hours are scheduled in a single block. These sessions are a mixture of lecture and tutorial in style. That is, delivery of content during this time involves both the lecturer explaining basic concepts, analytic procedures and interpretation of results, and the students conducting data analyses for the topic in question. Results are then discussed at group level.   

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 assessments of this unit are designed to place you in the role of researchers who are ready to critically analyse data using their knowledge of statistics and research design. Indeed, the assessments require you to make decisions about appropriate interpretation of data, and justify those decisions using a variety of statistical and logical arguments. The unit includes two assignments. The first is a data screening assignment which will allow you to demonstrate your knowledge and understanding of key issues in data screening and analysis. The second assignment will provide you with an opportunity to demonstrate your ability to choose and conduct appropriate data analysis techniques using SPSS, In addition to this, you will interpret the results of said analyses and report them adhering to discipline standards (APA). Emphasis is placed on critical analysis and decision making. 

Overview of assessments

Representative texts and references

American Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.  

Field, A. (2013). Discovering statistics using SPSS. (4th ed.). London: Sage. 

Hair, J.F., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice-Hall.  

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press, Chapter 1 

Tabachnick, B.G., & Fidell, L.S. (2012). Using multivariate statistics (6th ed.). Boston: Pearson.