Quantitative Research Methods

Description

Overview: Aims: The key aim of the Quantitative Methods module is to provide a solid foundation in the principal statistical techniques used within contemporary psychological research. Students will acquire an understanding of what data analysis solutions are available under a wide range of different circumstances, how to apply these in an appropriate manner, and how to interpret empirical research reports that make use of them in an informed and critical fashion. These aims are achieved via: • revision and extension of knowledge of those statistical methods to which students were introduced at undergraduate level • provision of an introduction to a range of more advanced statistical techniques, their underlying principles, and the research designs where they are likely to be employed • provision of guidance on how to use SPSS and other statistical packages to carry out analyses using these techniques, and how to interpret the output from these analyses. Syllabus: The class contains lectures with in-built practical exercises and tutorials to support additional understanding to cover the theory and practice of the advanced application of the following analytical techniques within psychology: • Hypothesis Testing, Effect Sizes, and Power Analysis • Analysis of Variance (ANOVA) • Analysis of Covariance (ANCOVA) • Multivariate Analysis of Variance (MANOVA) • Systematic Reviews and Meta-Analysis • Correlation and Simple Linear Regression • Multiple Linear Regression • Logistic Regression • Analysis of Contingency Tables • Structural Equation Modelling • Factor Analysis • Longitudinal Data Analysis Since the class covers the advanced application of statistical techniques, students must have prior learning of the following, which are commonly taught on Undergraduate Psychology programmes. These will provide the basis for learning the more advanced methods taught in this class: • The principles of hypothesis testing (e.g., falsifiability, the null hypothesis, one- and two-tailed hypotheses, probability and statistical significance). • Variables and levels of measurement (e.g., what is an independent variable? What is a dependent variable? Ratio, interval, ordinal and nominal levels of measurement). • Reliability and validity. • Descriptive statistics (e.g., computing a mean, median, mode, standard deviation, standard error, z-scores, normality distributions, percentiles and quartiles). • The Chi-Square test • T-Tests (between-groups and paired samples) • Correlations (Pearson’s, Spearman’s) • The Wilcoxon signed ranks test • The Kruskall-Wallis test • The Mann Whitney U test • The Friedman test • The Cronbach’s Alpha (reliability) test Prior learning of the following is also desirable: • Basic ANOVA designs (incl. one-way ANOVA, repeated measures ANOVA, two-way ANOVAs) • Basic Linear Regression techniques (testing R square and independent predictors of a dependent variable) Prior to this class, students also need to be familiar with the use of standard software packages for running statistical analyses (SPSS, Excel). Students wishing to refresh their understanding of the above pre-requisites are advised to consult the following text: Wilson, S., & MacLean, R. (2011). Research methods and data analysis for psychology. McGraw-Hill: Berkshire. The recommended text for this class (specified at the end of this class statement) also provides an excellent overview of the use of these pre-requisite analytical techniques in SPSS. Learning Outcomes: Knowledge and Understanding Students are expected to develop: • An advanced theoretical and practical understanding of core statistical competencies in Psychology. • Advanced skills in the implementation of the appropriate statistical tests within SPSS and AMOS. • An advanced understanding of the output from computerised and ‘by-hand’ analyses and an ability to communicate these in an appropriate format. Practical Skills Students are expected to develop their skills in: • Numerical and statistical competency. • Computer software interface and procedures with SPSS and AMOS. • Statistical interpretation. Generic/Transferable Skills Students are expected to develop their skills in: • Logical thought • Problem solving • Numerical and statistical competency • Computer software literacy • Independent learning Assessment: Assessment will take the form of two (2) class tests. These are individual assessments (i.e., completed on your own). The total mark available for each test will be 100%. The mean of the two class test marks will constitute the final class mark. Feedback on performance in each test will be provided. Ordinarily, students are asked to complete both class tests within a two (2) hour session at computer terminals, on-campus, independently, and under standard test conditions. The class tests normally take place at the beginning of the second teaching semester (in January). Students failing the class (i.e., attaining a mean mark for the two test papers lower than 50%) will be given an opportunity to retake the tests in the University’s formal resit exam diet (July/August).