Applied Statistical Procedures
A course giving a good overview of a number of statistical procedures all in one place, from Chi-Square to Multivariate Analysis of Variance to Factor Analysis.
Wellington (IN PERSON):
Monday 28 November – Friday 2 December (5 DAYS)
If you are paying via invoice, please email email@example.com in order to start the process. For credit card and bank transfer payments, you can enrol online directly:
While this is an intermediate course, research language and concepts will be taught to encompass both qualitative researchers with little knowledge of quantitative research and quantitative researchers who wish to review or broaden their understanding of the range of techniques and how to use them.
The course covers many commonly used statistical procedures from Chi-Square to Factor Analysis. It further defines research methodologies that so participants can match their research design with their research needs, i.e. when and how to determine causal relationships, and how to evaluate and report attitudes and behaviours. The course is taught from an applied perspective with many examples, and questions are encouraged. SPSS will be used to practise procedures, but no prior knowledge is required.
You will be exposed to a variety of research scenarios and to the logic of statistical procedure selection and application. The target audience ranges from qualitative researchers wanting to gain quantitative skills, to quantitative researchers wanting to broaden their understanding across procedures, or to become more comfortable with covariance prior to taking on the likes of Structural Equation Modelling.
After completing this course you should be able to read and understand literature where these procedures are reported, select appropriate statistical procedures for research, run procedures, and report results from an informed base of understanding.
The context of quantitative research in relation to qualitative research. The language of quantitative research, and the required fundamentals of SPSS.
Reliability, correlation, controlling for confounding variables, chi-square, t-tests.
ANOVA, ANCOVA, Factorial ANOVA, MANOVA, non-parametric tests.
Simple linear regression, multiple regression, discriminate analysis, factor analysis.
Testing Normality, data transformations, validity, reporting, ethics.
Topics covered include:
- Frequency-based statistics of chi-square goodness of fit and the test of association
- Parametric test of difference statistics: t-tests, ANOVA, ANCOVA, MANOVA, MANCOVA. Factorial analysis with multiple independent variables will also be covered along with repeated measures ANOVA
- Non-parametric test of difference statistics of Mann-Whitney, Wilcoxon, Friedman's analysis of variance, and Kruskal-Wallis
- Statistics to predict values and explain variance: simple regression, multiple regression, discriminant analysis, multiple discriminant analysis
- Data reduction techniques of factor analysis
- Power, data, and statistics that are most powerful, and techniques for increasing statistical power
- How to determine the best procedure for the demands of the research
- Data transformation to increase power and allow parametric procedures to be employed when data can be appropriately adjusted
- Important interplays between effect size and significance
- Integration of statistical results into reports.
Aron A, Coups E, Aron E (2019). Statistics for the Behavioral and Social Sciences: A Brief Course, 6th Edition. Pearson Higher Education.
Hair J, Babin B, Anderson R, Black C (2018). Multivariate Data Analysis,
8th Edition. Harlow: Pearson Education Limited.