Testing Statistical Assumptions in Research: An interview with author J.P. Verma

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  • Author: Statistics Views
  • Date: 24 Jul 2019

Last month Wiley was proud to publish Testing Statistical Assumptions in Research by J.P. Verma and Abdel-Salam G. Abdel-Salam, which comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so

This book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met.

Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient.

•An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study
•Shows readers the adverse effect of violating the assumptions on findings by means of various illustrations
•Describes different assumptions associated with different statistical tests commonly used by research scholars
•Contains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions
•Looks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis

Testing Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts.

Alison Oliver talks to author J.P. Verma about the book.

thumbnail image: Testing Statistical Assumptions in Research: An interview with author J.P. Verma

1. Congratulations on the publication of Testing Statistical Assumptions in Research. The book itself is said to be a reference for graduate students and research scholars for testing assumptions of statistical tests before using them in their research study. What led to the idea to write a book focusing on statistical assumptions in research?

Being the professor of statistics, I keep conducting research methodology workshops/courses in different universities across the word. During the workshop I have noticed that the research scholars and faculty do not give much importance to the assumptions associated with the statistical test they use for analysing data. Since no formal course work is offered to let the researchers know about the possible impact of not testing assumptions related to data and tests, I thought of writing a formal text on testing statistical assumptions in research.

2. What were your main objectives during the writing process? And which topic, that you discuss in your book, would you say was the most interesting for you?

I wanted to let the readers know about different type of assumptions associated with parametric and non-parametric statistical tests and test them before applying statistical tests for addressing their research questions. The most interesting topic in the book was to show as to how the results of the study can be totally reversed if assumptions associated with the test is severely violated.

3. This book particularly focuses on graduate students and research scholars to improve the accuracy of their conclusions. When writing this book, why did you feel it was important to teach these testing methods and explain their uses?

While teaching advanced statistics to the PhD scholars in physical education, sports sciences, management and other areas I have shown as to how the violation of assumptions and not selecting optimum sample size in the study affects the research findings. While conducting sessions on testing assumptions in various workshops and conferences the issue was well received hence, I decided to help larger community of researchers by bringing out exclusive text on testing statistical assumptions in research.

4. If there is one piece of information or advice that you would want your audience to take away from your book, what would that be?

Although measures have been discussed as to what the researcher should do if some of the assumptions associated with the test is violated, but if the results are required to be shown when one or two assumptions are not severely violated then one must write that the results should be viewed along with the backdrop of violating specific assumption(s).

J.P. Verma

5. The book is said to describe different assumptions associated with different statistical tests commonly used by research scholars. Would you be able to provide a brief overview of the assumptions discussed?

The book Testing Statistical Assumptions in Research discusses various assumptions associated with parametric and non-parametric tests. For parametric tests assumptions like normality, Randomness, outliers, Homogeneity of Variances, Independence of Observations and Linearity have been discussed by means of illustrations. For regression analysis assumptions like Autocorrelation, Homoscedasticity with Durbin-Watson Test and multicollinearity with VIF test have been discussed by means of outputs generated using the SPSS software. Assumptions for different non-parametric tests have been explained by means of different illustrations. In case of violation of assumptions, remedies have also been suggested in using different statistical tests.

6. Why, do you think, this understanding of testing statistical assumptions in research may be of importance now?

Testing statistical assumptions were always important in discussing the research findings. Due to easy access of the statistical software and non-availability of user-friendly texts on the subject matter researchers are tempted to discuss the findings without testing assumptions.

7. Alongside your own text, what other books would you recommend to students looking to learn more about testing statistical assumptions in their research?

For motivating the researchers in different areas I have produced international editions of text books on sports research, repeated measures design, statistics for exercise science and data analysis in management.

8. What other work are you currently working on or has recently been published?

Another aspect which is must for the researcher is to decide the sample size for specific power in hypothesis testing experiment, and for desired accuracy of estimate in survey studies. The readers can use the text on determining Sample Size with GPower Software for finding sample size in their study for specific power by using the freeware software. At present I am engaged in giving final shape to my next text book titled Statistics and Research Methods in Psychology with Excel which is due for publication in August 2019.

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