Applied Statistical Procedures

Fee structure

Students
1,025 NZD

Everyone else
2,050 NZD

Course outline

This course covers many commonly used statistical procedures from Chi-Square to Factor Analysis. It defines research methodologies that so participants can match their research designs with their research needs; that is, when and how causal relationships are to be determined and, in social research, how attitudes and behaviours are to be evaluated and reported upon.

The course is taught from an applied perspective with many examples, and questions are encouraged. Participants are assisted in practising the procedures during the course using their choice of SPSS or Jamovi (a freely downloadable, menu-driven stats package). No prior knowledge of either package is required.

Participants will be exposed to a variety of research scenarios and to the logic of statistical procedure selection and application. Qualitative researchers wanting to gain quantitative skills would find that this course suits their needs well, as would quantitative researchers wanting to broaden their understanding across procedures.

This course will enable participants to read and understand literature where the covered procedures are reported, to select appropriate statistical procedures for research, run them in the software, and report results from them, with an informed base of understanding.

Day one: Introduction to quantitative research
  • The context of quantitative research in relation to qualitative research
  • The language of quantitative research, and the required fundamentals of SPSS/Jamovi
  • Data preparation and manipulation
  • Computing and recoding variables
  • Measures of reliability including Cronbach’s Alpha and Kuder-Richardson Formulas.
Day two: Frequency-based statistics
  • Pearson’s r, Spearman’s Rho, point-biserial correlation, Phi and Cramér’s V
  • Chi-Square tests of goodness of fit and independence/association
  • Independent and paired t-tests
  • Controlling for confounding variables
  • Assumptions and reliability
  • Interplays between effect size and statistical significance.
Day three: Statistical tests of differences
  • One-way Analysis of Variance (ANOVA), post-hoc comparisons using Tukey’s Honest Significant Difference and Scheffé’s Test, and Analysis of Covariance (ANCOVA), controlling for confounding variables
  • Multivariate Analysis of Variance (MANOVA) (testing for multiple dependent variables), Pillai’s Trace, and Multivariate Analysis of Covariance (MANCOVA), controlling for confounding variables
  • Two-way ANOVA, specifically Factorial ANOVA, testing for interactions between independent variables
  • Repeated Measures ANOVA (equivalent of paired t-tests for more than two measures) and Mauchly’s Test of Sphericity
  • Non-parametric equivalents of many of these tests, including the Mann-Whitney U test, Wilcoxon Signed Ranks test, Friedman’s Analysis of Variance, and the Kruskal Wallis test.
Day four: Prediction, variance explanation, and data reduction
  • Simple Linear Regression
  • Multiple Linear Regression and variable selection strategies
  • Linear Discriminant Analysis, for categorical dependent variables
  • Factor Analysis, for identifying new constructs from sets of observed variables.
Day five: Statistical power and techniques for increasing it
  • Skewness and kurtosis
  • Testing for normality and transforming variables to improve their normality
  • Levene’s Test for equality of variances
  • Measuring reliability and validity in survey research
  • Moving data between Excel and SPSS/Jamovi
  • Creating and editing graphs in SPSS and in Excel
  • Tips for data management, and integrating analytical results into reports.

If you want any further advice on whether this course is right for you, please email nzssncourses@auckland.ac.nz and we will put you in touch with the instructor.

Instructor

Gordon Emmerson
Gordon Emmerson is a specialist in quantitative research. He has taught undergraduate and postgraduate statistics programmes in the Psychology Department of Victoria University (Melbourne), and coordinated a Major in Social Research Methods there.

Gordon has been employed as a statistical methods advisor to Kansas State University staff, and taught in the PhD programme there. He is an experienced group facilitator and regularly conducts workshops in a range of topic areas.

Gordon is an experienced user of data management and statistical software packages, including SPSS, Excel, and Jamovi. He has also undertaken many quantitative research consultancies within the health and education sectors.