{"product_id":"testing-statistical-assumptions-in-research-isbn-9781119528418","title":"Testing Statistical Assumptions in Research","description":"\u003cp\u003e\u003cb\u003eComprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eTesting Statistical Assumptions in Research\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study\u003c\/li\u003e \u003cli\u003eShows readers the adverse effect of violating the assumptions on findings by means of various illustrations\u003c\/li\u003e \u003cli\u003eDescribes different assumptions associated with different statistical tests commonly used by research scholars\u003c\/li\u003e \u003cli\u003eContains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions\u003c\/li\u003e \u003cli\u003eLooks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eTesting Statistical Assumptions in Research\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Importance of Assumptions in Using Statistical Techniques 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Data Types 2\u003c\/p\u003e \u003cp\u003e1.2.1 Nonmetric Data 2\u003c\/p\u003e \u003cp\u003e1.2.2 Metric Data 2\u003c\/p\u003e \u003cp\u003e1.3 Assumptions About Type of Data 3\u003c\/p\u003e \u003cp\u003e1.4 Statistical Decisions in Hypothesis Testing Experiments 4\u003c\/p\u003e \u003cp\u003e1.4.1 Type I and Type II Errors 5\u003c\/p\u003e \u003cp\u003e1.4.2 Understanding Power of Test 6\u003c\/p\u003e \u003cp\u003e1.4.3 Relationship Between Type I and Type II Errors 7\u003c\/p\u003e \u003cp\u003e1.4.4 One-Tailed and Two-Tailed Tests 8\u003c\/p\u003e \u003cp\u003e1.5 Sample Size in Research Studies 8\u003c\/p\u003e \u003cp\u003e1.6 Effect of Violating Assumptions 11\u003c\/p\u003e \u003cp\u003eExercises 12\u003c\/p\u003e \u003cp\u003eAnswers 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction of SPSS and Segregation of Data 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.2 Introduction to SPSS 17\u003c\/p\u003e \u003cp\u003e2.2.1 Data File Preparation 19\u003c\/p\u003e \u003cp\u003e2.2.2 Importing the Data Set from Excel 21\u003c\/p\u003e \u003cp\u003e2.3 Data Cleaning 23\u003c\/p\u003e \u003cp\u003e2.3.1 Interpreting Descriptive Statistics Output 26\u003c\/p\u003e \u003cp\u003e2.3.2 Interpreting Frequency Statistic Output 27\u003c\/p\u003e \u003cp\u003e2.4 Data Management 27\u003c\/p\u003e \u003cp\u003e2.4.1 Sorting Data 28\u003c\/p\u003e \u003cp\u003e2.4.1.1 Sort Cases 28\u003c\/p\u003e \u003cp\u003e2.4.1.2 Sort Variables 29\u003c\/p\u003e \u003cp\u003e2.4.2 Selecting Cases Using Condition 31\u003c\/p\u003e \u003cp\u003e2.4.2.1 Selecting Data of Males with Agree Response 32\u003c\/p\u003e \u003cp\u003e2.4.3 Drawing Random Sample of Cases 34\u003c\/p\u003e \u003cp\u003e2.4.4 Splitting File 36\u003c\/p\u003e \u003cp\u003e2.4.5 Computing Variable 36\u003c\/p\u003e \u003cp\u003eExercises 40\u003c\/p\u003e \u003cp\u003eAnswers 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Assumptions in Survey Studies 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 45\u003c\/p\u003e \u003cp\u003e3.2 Assumptions in Survey Research 46\u003c\/p\u003e \u003cp\u003e3.2.1 Data Cleaning 46\u003c\/p\u003e \u003cp\u003e3.2.2 About Instructions in Questionnaire 46\u003c\/p\u003e \u003cp\u003e3.2.3 Respondent’s Willingness to Answer 47\u003c\/p\u003e \u003cp\u003e3.2.4 Receiving Correct Information 47\u003c\/p\u003e \u003cp\u003e3.2.5 Seriousness of the Respondents 47\u003c\/p\u003e \u003cp\u003e3.2.6 Prior Knowledge of the Respondents 48\u003c\/p\u003e \u003cp\u003e3.2.7 Clarity About Items in the Questionnaire 48\u003c\/p\u003e \u003cp\u003e3.2.8 Ensuring Survey Feedback 48\u003c\/p\u003e \u003cp\u003e3.2.9 Nonresponse Error 48\u003c\/p\u003e \u003cp\u003e3.3 Questionnaire’s Reliability 49\u003c\/p\u003e \u003cp\u003e3.3.1 Temporal Stability 49\u003c\/p\u003e \u003cp\u003e3.3.1.1 Test–Retest Method 49\u003c\/p\u003e \u003cp\u003e3.3.2 Internal Consistency 50\u003c\/p\u003e \u003cp\u003e3.3.2.1 Split-Half Test 50\u003c\/p\u003e \u003cp\u003e3.3.2.2 Kuder–Richardson Test 52\u003c\/p\u003e \u003cp\u003e3.3.2.3 Cronbach’s Alpha 55\u003c\/p\u003e \u003cp\u003eExercise 60\u003c\/p\u003e \u003cp\u003eAnswers 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Assumptions in Parametric Tests 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 65\u003c\/p\u003e \u003cp\u003e4.2 Common Assumptions in Parametric Tests 66\u003c\/p\u003e \u003cp\u003e4.2.1 Normality 66\u003c\/p\u003e \u003cp\u003e4.2.1.1 Testing Normality with SPSS 67\u003c\/p\u003e \u003cp\u003e4.2.1.2 What if the Normality Assumption Is Violated? 71\u003c\/p\u003e \u003cp\u003e4.2.1.3 Using Transformations for Normality 72\u003c\/p\u003e \u003cp\u003e4.2.2 Randomness 74\u003c\/p\u003e \u003cp\u003e4.2.2.1 Runs Test for Testing Randomness 75\u003c\/p\u003e \u003cp\u003e4.2.3 Outliers 76\u003c\/p\u003e \u003cp\u003e4.2.3.1 Identifying Outliers with SPSS 77\u003c\/p\u003e \u003cp\u003e4.2.4 Homogeneity of Variances 79\u003c\/p\u003e \u003cp\u003e4.2.4.1 Testing Homogeneity with Levene’s Test 79\u003c\/p\u003e \u003cp\u003e4.2.5 Independence of Observations 82\u003c\/p\u003e \u003cp\u003e4.2.6 Linearity 82\u003c\/p\u003e \u003cp\u003e4.3 Assumptions in Hypothesis Testing Experiments 82\u003c\/p\u003e \u003cp\u003e4.3.1 Comparing Means with \u003ci\u003et\u003c\/i\u003e-Test 83\u003c\/p\u003e \u003cp\u003e4.3.2 One Sample \u003ci\u003et\u003c\/i\u003e-Test 83\u003c\/p\u003e \u003cp\u003e4.3.2.1 Testing Assumption of Randomness 84\u003c\/p\u003e \u003cp\u003e4.3.2.2 Testing Normality Assumption in t-Test 85\u003c\/p\u003e \u003cp\u003e4.3.2.3 What if the Normality Assumption Is Violated? 88\u003c\/p\u003e \u003cp\u003e4.3.3 Sign Test 88\u003c\/p\u003e \u003cp\u003e4.3.4 Paired t-Test 88\u003c\/p\u003e \u003cp\u003e4.3.4.1 Effect of Violating Normality Assumption in Paired t-Test 91\u003c\/p\u003e \u003cp\u003e4.3.5 Rank Test 91\u003c\/p\u003e \u003cp\u003e4.3.6 Independent Two-Sample t-Test 92\u003c\/p\u003e \u003cp\u003e4.3.6.1 Two-Sample t-Test with SPSS and Testing Assumptions 92\u003c\/p\u003e \u003cp\u003e4.3.6.2 Effect of Violating Assumption of Homogeneity 96\u003c\/p\u003e \u003cp\u003e4.4 \u003ci\u003eF\u003c\/i\u003e-test For Comparing Variability 97\u003c\/p\u003e \u003cp\u003e4.4.1 Analysis of Variance (ANOVA) 98\u003c\/p\u003e \u003cp\u003e4.4.2 ANOVA Assumptions 99\u003c\/p\u003e \u003cp\u003e4.4.2.1 Checking Assumptions Using SPSS 99\u003c\/p\u003e \u003cp\u003e4.4.3 One-Way ANOVA Using SPSS 105\u003c\/p\u003e \u003cp\u003e4.4.4 What to Do if Assumption Violates? 109\u003c\/p\u003e \u003cp\u003e4.4.5 What if the Assumptions in ANOVA Are Violated? 109\u003c\/p\u003e \u003cp\u003e4.5 Correlation Analysis 118\u003c\/p\u003e \u003cp\u003e4.5.1 Karl Pearson’s Coefficient of Correlation 118\u003c\/p\u003e \u003cp\u003e4.5.2 Testing Assumptions with SPSS 119\u003c\/p\u003e \u003cp\u003e4.5.2.1 Testing for Linearity 119\u003c\/p\u003e \u003cp\u003e4.5.3 Coefficient of Determination 122\u003c\/p\u003e \u003cp\u003e4.6 Regression Analysis 125\u003c\/p\u003e \u003cp\u003e4.6.1 Simple Linear Regression 126\u003c\/p\u003e \u003cp\u003e4.6.2 Assumptions in Linear Regression Analysis 128\u003c\/p\u003e \u003cp\u003e4.6.2.1 Testing Assumptions with SPSS 128\u003c\/p\u003e \u003cp\u003eExercises 136\u003c\/p\u003e \u003cp\u003eAnswers 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Assumptions in Nonparametric Tests 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 141\u003c\/p\u003e \u003cp\u003e5.2 Common Assumptions in Nonparametric Tests 141\u003c\/p\u003e \u003cp\u003e5.2.1 Randomness 142\u003c\/p\u003e \u003cp\u003e5.2.2 Independence 142\u003c\/p\u003e \u003cp\u003e5.2.2.1 Testing Assumptions Using SPSS 142\u003c\/p\u003e \u003cp\u003e5.2.2.2 Runs Test for Randomness Using SPSS 143\u003c\/p\u003e \u003cp\u003e5.3 Chi-square Tests 144\u003c\/p\u003e \u003cp\u003e5.3.1 Goodness-of-Fit Test 145\u003c\/p\u003e \u003cp\u003e5.3.1.1 Assumptions About Data 145\u003c\/p\u003e \u003cp\u003e5.3.1.2 Performing Chi-square Goodness-of-Fit Test Using SPSS 146\u003c\/p\u003e \u003cp\u003e5.3.2 Testing for Independence 148\u003c\/p\u003e \u003cp\u003e5.3.2.1 Assumptions About Data 148\u003c\/p\u003e \u003cp\u003e5.3.2.2 Performing Chi-square Test of Independence Using SPSS 148\u003c\/p\u003e \u003cp\u003e5.3.3 Testing for Homogeneity 152\u003c\/p\u003e \u003cp\u003e5.3.3.1 Assumptions About Data 153\u003c\/p\u003e \u003cp\u003e5.3.3.2 Performing Chi-square Test of Homogeneity Using SPSS 153\u003c\/p\u003e \u003cp\u003e5.3.4 What to Do if Assumption Violates? 155\u003c\/p\u003e \u003cp\u003e5.4 Mann-Whitney U Test 156\u003c\/p\u003e \u003cp\u003e5.4.1 Assumption About Data 157\u003c\/p\u003e \u003cp\u003e5.4.2 Mann-Whitney Test Using SPSS 157\u003c\/p\u003e \u003cp\u003e5.4.3 What to Do if Assumption Violates? 159\u003c\/p\u003e \u003cp\u003e5.5 Kruskal-Wallis Test 161\u003c\/p\u003e \u003cp\u003e5.5.1 Assumptions About Data 162\u003c\/p\u003e \u003cp\u003e5.5.2 Kruskal-Wallis H Test Using SPSS 162\u003c\/p\u003e \u003cp\u003e5.5.3 Dealing with Data When Assumption Is Violated 166\u003c\/p\u003e \u003cp\u003e5.6 Wilcoxon Signed-Rank Test 168\u003c\/p\u003e \u003cp\u003e5.6.1 Assumptions About Data 168\u003c\/p\u003e \u003cp\u003e5.6.2 Wilcoxon Signed-Rank Test Using SPSS 168\u003c\/p\u003e \u003cp\u003e5.6.3 Remedy if Assumption Violates 172\u003c\/p\u003e \u003cp\u003eExercises 172\u003c\/p\u003e \u003cp\u003eAnswers 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Assumptions in Nonparametric Correlations 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 175\u003c\/p\u003e \u003cp\u003e6.2 Spearman Rank-Order Correlation 175\u003c\/p\u003e \u003cp\u003e6.3 Biserial Correlation 178\u003c\/p\u003e \u003cp\u003e6.4 Tetrachoric Correlation 182\u003c\/p\u003e \u003cp\u003e6.4.1 Assumptions for Tetrachoric Correlation Coefficient 182\u003c\/p\u003e \u003cp\u003e6.4.1.1 Testing Significance 183\u003c\/p\u003e \u003cp\u003e6.5 Phi Coefficient (Φ) 184\u003c\/p\u003e \u003cp\u003e6.6 Assumptions About Data 188\u003c\/p\u003e \u003cp\u003e6.7 What if the Assumptions Are Violated? 188\u003c\/p\u003e \u003cp\u003eExercises 188\u003c\/p\u003e \u003cp\u003eAnswers 190\u003c\/p\u003e \u003cp\u003eAppendix Statistical Tables 193\u003c\/p\u003e \u003cp\u003eBibliography 203\u003c\/p\u003e \u003cp\u003eIndex 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJ. P. VERMA, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Professor of Statistics and Dean of Students Welfare at Lakshmibai National University of Physical Education, India. He is the author of \u003ci\u003eSports Research with Analytical Solution using SPSS, Repeated Measures Design for Empirical Researchers,\u003c\/i\u003e and\u003ci\u003e Statistics for Exercise Science and Health with Microsoft Office Excel.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eABDEL-SALAM G. ABDEL-SALAM, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e 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.\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eComprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eTesting Statistical Assumptions in Research\u003c\/i\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAn excellent reference for graduate students and research scholars of any    discipline in testing assumptions of statistical tests before using them in their    research study\u003c\/li\u003e \u003cli\u003eShows readers the adverse effect of violating the assumptions on findings by    means of various illustrations\u003c\/li\u003e \u003cli\u003eDescribes different assumptions associated with different statistical tests    commonly used by research scholars\u003c\/li\u003e \u003cli\u003eContains examples using SPSS, which helps facilitate readers to understand    the procedure involved in testing assumptions\u003c\/li\u003e \u003cli\u003eLooks at commonly used assumptions in statistical tests, such as z, t and F    tests, ANOVA, correlation, and regression analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eTesting Statistical Assumptions in Research\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990145810661,"sku":"NP9781119528418","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119528418.jpg?v=1761786678","url":"https:\/\/k12savings.com\/es\/products\/testing-statistical-assumptions-in-research-isbn-9781119528418","provider":"K12savings","version":"1.0","type":"link"}