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More About This Title Testing Statistical Assumptions in Research
English
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.
English
J. P. VERMA, PHD, is Professor of Statistics and Dean of Students Welfare at Lakshmibai National University of Physical Education, India. He is the author of Sports Research with Analytical Solution using SPSS, Repeated Measures Design for Empirical Researchers, and Statistics for Exercise Science and Health with Microsoft Office Excel.
ABDEL-SALAM G. ABDEL-SALAM, PHD, is Assistant Professor of Statistics and Head of Student Data Management Section and Coordinator for the Statistical Consulting Unit at Qatar University, Qatar. He is a PStat® by the American Statistical Association and CStat by the Royal Statistical Society. He taught at different international universities such as; Virginia Polytechnic Institute and State University (Virginia Tech), Oklahoma State University and Cairo University.
English
Preface
Acknowledgements
CHAPTER 1: IMPORTANCE OF ASSUMPTIONS IN USING STATISTICAL TECHNIQUES
INTRODUCTION
DATA TYPES
Non-Metric Data
Metric Data
ASSUMPTIONS ABOUT TYPE OF DATA
STATISTICAL DECISIONS IN HYPOTHESIS TESTING EXPERIMENTS
Type I and Type II Errors
Understanding Power of Test
Relationship between Type I and Type II Errors
One Tailed and Two Tailed Tests
SAMPLE SIZE IN RESEARCH STUDIES
EFFECT OF VIOLATING ASSUMPTIONS EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
CHAPTER 2: INTRODUCTION OF SPSS AND SEGREGATION OF DATA
INTRODUCTION
INTRODUCTION TO SPSS
Data File Preparation
DATA CLEANING
Interpreting Descriptive Statistics Output
Interpreting Frequency Statistic Output
DATA MANAGEMENT
Sorting Data
Sort Cases
Sort Variables
Selecting Cases Using Condition
Selecting Data of Males with Agree Response
Drawing Random Sample of Cases
Splitting File
Computing Variable
EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
CHAPTER 3: ASSUMPTIONS IN SURVEY STUDIES
INTRODUCTION
ASSUMPTIONS IN SURVEY RESEARCH
Data Cleaning
About Instructions in Questionnaire
Respondent’s Willingnessto answer
Receiving Correct information
Seriousness of the Respondents
Prior Knowledge of the Respondents
Clarity about Items in the Questionnaire
Ensuring Survey Feedback
Non Response Error
QUESTIONNAIRE’S RELIABILITY
Temporal Stability
Test-retest Methods
Internal Consistency
Split-half Test
Kudar–Richardson Test
Cronbach’s Alpha
EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
CHAPTER 4: ASSUMPTIONS IN PARAMETRIC TESTS
INTRODUCTION
COMMON ASSUMPTIONS IN PARAMETRIC TESTS
Normality
Testing Normality with SPSS
What if the Normality Assumption is violated?
Using Transformations for Normality
Randomness
Runs Test for Testing Randomness
Outliers
Identifying Outliers with SPSS
Homogeneity of Variances
Testing Homogeneity with Levene’s Test
Independence of Observations
Linearity
ASSUMPTIONS IN HYPOTHESIS TESTING EXPERIMENTS
Comparing Means with ‘t’ Test
One Sample t-test
Testing Assumption of Randomness
Testing Normality Assumption in t test
What if the Normality assumption is violated?
Sign Test
Paired t-test
Effect of Violating Normality Assumption in Paired‘t’ Test
Rank Test
Independent Two-sample T-test
Two-sample t-test with SPSS and Testing Assumptions
Effect of Violating Assumption of Homogeneity
F-TEST FOR COMPARING VARIABILITY
Analysis of Variance (ANOVA)
ANOVA Assumptions
Checking Assumptions using SPSS
One-Way ANOVA using SPSS
What to do if Assumption Violates?
What if the Assumptions in ANOVA are violated?
CORRELATION ANALYSIS
Karl Pearson’s Coefficient of Correlation
Testing Assumptions with SPSS
Testing for Linearity
Coefficient of Determination
REGRESSION ANALYSIS
Simple Linear Regression
Assumptions in Regression Analysis
Testing Assumptions with SPSS
EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
CHAPTER 5: ASSUMPTIONS IN NON-PARAMETRIC TESTS
INTRODUCTION
COMMON ASSUMPTIONS IN NON-PARAMETRIC TESTS
Randomness
Independence
Testing Assumptions and Example using SPSS
Runs Test for Randomness using SPSS
CHI –SQUARE TEST
Goodness of Fit Test
Assumptions about data
Performing Chi-square Goodness of Fit Test using SPSS
Testing for Independence
Assumptions about data
Performing Chi-square Test of Independence using SPSS
Testing for Homogeneity
Assumptions about data
Performing Chi-square Test of Homogeneity using SPSS
What to do if assumption violates?
MANN-WHITNEY TEST
Assumption about data
Mann-Whitney Test using SPSS
What to do if assumption violates?
KRUSKAL WALLIS TEST
Assumptions about data
What to do if assumption violates?
WILCOXON SIGNED-RANKED TEST
Assumptions about data
Kruskal Wallis H Test using SPSS
Dealing with data when assumption is violated
WILCOXON SIGN TEST
Assumptions about data
Wilcoxon Sign Test using SPSS
Remedy if assumption violates
EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
Chapter 6:Assumptions in Non-Parametric Correlations
INTRODUCTION
SPEARMAN RANK-ORDER CORRELATION
BISERIAL CORRELATION
TETRACHORIC CORRELATION
PHI COEFFICIENT
ASSUMPTIONS ABOUT DATA
WHAT IF THE ASSUMPTIONS ARE VIOLATED
EXERCISE
Multiple Choice Questions
Short Answer Questions
ANSWER
Multiple Choice Questions
Appendix
Bibliography
Index
