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Analysis of small samples in quantitative research using non-parametric statistics HEDC

About This Course

Small sample size datasets are widely used in quantitative research, especially within the Health, Social and Behavioural Sciences. There are two important issues to consider before conducting experiments with small samples. Firstly, it is important to understand what sample size is desirable to give optimal results. Secondly, when data, especially in circumstances, do not adequately meet parametric conditions, it is further imperative to decide the type of appropriate statistical tests.

Goals

This course introduces participants to effectively work with small samples and shows how they can go about deciding on using a suitable statistical test. The importance of statistical power analysis, sample size, effect size, and p-value will be discussed. Using practical examples, participants will learn how to predict sample size using a free tool— G*Power.

Demonstrating with IBM SPSS, participants will also be introduced to how to detect the normality and measure the kurtosis of distribution. The threshold of using either exact test or asymptotic (or approximant) test in SPSS will be discussed.

Finally, participants will be introduced to working with non-parametric tests specifically, Kruskal-Wallis H test and Mann-Whitney U test instead of ANOVA test and t test.

Requirements

Postgraduate students and staff should have already known the basic knowledge of parametric statistics (e.g. t-Test and ANOVA).

Acknowledgment

This course was based on the PhD thesis by Dr Minjie Hu, Teaching Novices Programming: A programming process using goals and plans with a visual programming environment, Department of Information Science, University of Otago. The supervision and contribution to the original ideas of these contents by Professor Michael Winikoff and Professor Stephen Cranefield are much appreciated.

Course Staff

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  1. Course Number

    HEDC102
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