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Test Bank for Statistics for the Behavioral Sciences, 5th Edition

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Test Bank for Statistics for the Behavioral Sciences, 5th Edition

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Nolan and Heinzen offer an introduction to the basics of statistics that is uniquely suited for behavioral science students due to its coverage that is anchored in real-world stories, its highly visual approach to presenting data, helpful mathematical and formula support, and its unique immersive learning activities (Which Test is Best and the new Interpreting Statistical Results) right in LaunchPad. The latest edition includes information in every chapter about data ethics, including the movement towards open science

 

Table of Content:

  1. Chapter 1 An Introduction to Statistics and Research Design
  2. The Two Branches of Statistics
  3. Descriptive Statistics
  4. Inferential Statistics
  5. Distinguishing Between a Sample and a Population
  6. How to Transform Observations into Variables
  7. Discrete Observations
  8. Continuous Observations
  9. Variables and Research
  10. Independent, Dependent, and Confounding Variables
  11. Reliability and Validity
  12. Introduction to Hypothesis Testing
  13. Correlational Studies and the Danger of Confounding Variables
  14. Conducting Experiments to Control for Confounding Variables
  15. Between-Groups Design Versus Within-Groups Design
  16. Introduction to Data Ethics
  17. Data Ethics: Preregistration
  18. Review of Concepts
  19. SPSS
  20. Exercises
  21. Terms
  22. Chapter 2 Frequency Distributions
  23. Frequency Distributions
  24. Frequency Tables
  25. Grouped Frequency Tables
  26. Histograms
  27. Shapes of Distributions
  28. Normal Distributions
  29. Skewed Distributions
  30. Data Ethics: Dot Plots and the Importance of Seeing Individual Data Points
  31. Review of Concepts
  32. SPSS
  33. How it Works
  34. Exercises
  35. Terms
  36. Chapter 3 Visual Displays of Data
  37. The Power of Graphs
  38. “The Most Misleading Graph Ever Published”
  39. Data Ethics: How to Lie with Graphs
  40. Common Types of Graphs
  41. Scatterplots
  42. Line Graphs
  43. Bar Graphs
  44. Pictorial Graphs
  45. Pie Charts
  46. How to Build a Graph
  47. Choosing the Appropriate Type of Graph
  48. How to Read a Graph
  49. Guidelines for Creating a Graph
  50. The Future of Graphs
  51. Review of Concepts
  52. SPSS
  53. How it Works
  54. Exercises
  55. Terms
  56. Chapter 4 Central Tendency and Variability
  57. Central Tendency
  58. Mean: The Arithmetic Average
  59. Median: The Middle Score
  60. Mode: The Most Common Score
  61. How Outliers Affect Measures of Central Tendency
  62. Data Ethics: Being Fair with Central Tendency
  63. Measures of Variability
  64. Range
  65. The Interquartile Range
  66. Variance
  67. Standard Deviation
  68. Review of Concepts
  69. SPSS
  70. How it Works
  71. Exercises
  72. Terms
  73. Chapter 5 Sampling and Probability
  74. Samples and Their Populations
  75. Random Sampling
  76. Convenience Sampling
  77. The Problem with a Biased Sample
  78. Random Assignment
  79. Probability
  80. Coincidence and Probability
  81. Expected Relative-Frequency Probability
  82. Independence and Probability
  83. Inferential Statistics
  84. Developing Hypotheses
  85. Making a Decision About a Hypothesis
  86. Type I and Type II Errors
  87. Type I Errors
  88. Type II Errors
  89. Data Ethics: The Shocking Prevalence of Type I Errors
  90. Review of Concepts
  91. SPSS
  92. How it Works
  93. Exercises
  94. Terms
  95. Chapter 6 The Normal Curve, Standardization, and z Scores
  96. The Normal Curve
  97. Standardization, z Scores, and the Normal Curve
  98. The Need for Standardization
  99. Transforming Raw Scores into z Scores
  100. Transforming z Scores into Raw Scores
  101. Using z Scores to Make Comparisons
  102. Transforming z Scores into Percentiles
  103. The Central Limit Theorem
  104. Creating a Distribution of Means
  105. Characteristics of the Distribution of Means
  106. Using the Central Limit Theorem to Make Comparisons with z Scores
  107. Data Ethics: Using z Scores to Keep Researchers Honest
  108. Review of Concepts
  109. SPSS
  110. How it Works
  111. Exercises
  112. Terms
  113. Chapter 7 Hypothesis Testing with z Tests
  114. The z Table
  115. Raw Scores, z Scores, and Percentages
  116. The z Table and Distributions of Means
  117. The Assumptions and Steps of Hypothesis Testing
  118. The Three Assumptions for Conducting Analyses
  119. The Six Steps of Hypothesis Testing
  120. An Example of the z Test
  121. Data Ethics: HARKing and p-Hacking
  122. Review of Concepts
  123. SPSS
  124. How it Works
  125. Exercises
  126. Terms
  127. Chapter 8 Confidence Intervals, Effect Size, and Statistical Power
  128. The New Statistics
  129. Confidence Intervals
  130. Interval Estimates
  131. Calculating Confidence Intervals with z Distributions
  132. Effect Size
  133. The Effect of Sample Size on Statistical Significance
  134. What Effect Size Is
  135. Cohen’s d
  136. Meta-Analysis
  137. Statistical Power
  138. Making Correct Decisions
  139. Five Factors That Affect Statistical Power
  140. Data Ethics: Sample Size Planning
  141. Review of Concepts
  142. How it Works
  143. Exercises
  144. Terms
  145. Chapter 9 The Single-Sample t Test
  146. The t Distributions
  147. Estimating Population Standard Deviation from a Sample
  148. Calculating Standard Error for the t Statistic
  149. Using Standard Error to Calculate the t Statistic
  150. The Single-Sample t Test
  151. The t Table and Degrees of Freedom
  152. The Six Steps of the Single-Sample t Test
  153. Calculating a Confidence Interval for a Single-Sample t Test
  154. Calculating Effect Size for a Single-Sample t Test
  155. Data Ethics: Replication and Reproducibility
  156. Review of Concepts
  157. SPSS
  158. How it Works
  159. Exercises
  160. Terms
  161. Chapter 10 The Paired-Samples t Test
  162. The Paired-Samples t Test
  163. Distributions of Mean Differences
  164. The Six Steps of the Paired-Samples t Test
  165. Beyond Hypothesis Testing
  166. Calculating a Confidence Interval for a Paired-Samples t Test
  167. Calculating Effect Size for a Paired-Samples t Test
  168. Data Ethics: Avoiding Order Effects Through Counterbalancing
  169. Review of Concepts
  170. SPSS
  171. How it Works
  172. Exercises
  173. Terms
  174. Chapter 11 The Independent-Samples t Test
  175. Conducting an Independent-Samples t Test
  176. A Distribution of Differences Between Means
  177. The Six Steps of the Independent-Samples t Test
  178. Reporting the Statistics
  179. Beyond Hypothesis Testing
  180. Calculating a Confidence Interval for an Independent-Samples t Test
  181. Calculating Effect Size for an Independent-Samples t Test
  182. Data Ethics: The Bayesian Approach to Data Analysis
  183. Review of Concepts
  184. SPSS
  185. How it Works
  186. Exercises
  187. Terms
  188. Chapter 12 One-Way Between-Groups ANOVA
  189. Using the F Distributions with Three or More Samples
  190. Type I Errors When Making Three or More Comparisons
  191. The F Statistic as an Expansion of the z and t Statistics
  192. The F Distributions for Analyzing Variability to Compare Means
  193. The F Table
  194. The Language and Assumptions for ANOVA
  195. One-Way Between-Groups ANOVA
  196. Everything About ANOVA But the Calculations
  197. The Logic and Calculations of the F Statistic
  198. Making a Decision
  199. Beyond Hypothesis Testing for the One-Way Between-Groups ANOVA
  200. R2 and Omega Squared, Effect Sizes for ANOVA
  201. Post Hoc Tests
  202. The Tukey HSD Test
  203. Data Ethics: The Bonferroni Test
  204. Review of Concepts
  205. SPSS
  206. How it Works
  207. Exercises
  208. Terms
  209. Chapter 13 One-Way Within-Groups ANOVA
  210. One-Way Within-Groups ANOVA
  211. The Benefits of Within-Groups ANOVA
  212. The Six Steps of Hypothesis Testing
  213. Beyond Hypothesis Testing for the One-Way Within-Groups ANOVA
  214. R2, the Effect Size for ANOVA
  215. The Tukey HSD Test
  216. Data Ethics: “WEIRD” Samples and Good Reporting Practices
  217. Review of Concepts
  218. SPSS
  219. How it Works
  220. Exercises
  221. Terms
  222. Chapter 14 Two-Way Between-Groups ANOVA
  223. Two-Way ANOVA
  224. Why We Use Two-Way ANOVA
  225. The More Specific Vocabulary of Two-Way ANOVA
  226. Two Main Effects and an Interaction
  227. Understanding Interactions in ANOVA
  228. Interactions and Public Policy
  229. Interpreting Interactions
  230. Conducting a Two-Way Between-Groups ANOVA
  231. The Six Steps of Two-Way ANOVA
  232. Identifying Four Sources of Variability in a Two-Way ANOVA
  233. Effect Size for Two-Way ANOVA
  234. Variations on ANOVA
  235. Data Ethics: How Data Detectives Fact-Check Statistics
  236. Review of Concepts
  237. SPSS
  238. How it Works
  239. Exercises
  240. Terms
  241. Chapter 15 Correlation
  242. The Meaning of Correlation
  243. The Characteristics of Correlation
  244. Correlation Is Not Causation
  245. The Pearson Correlation Coefficient
  246. Calculating the Pearson Correlation Coefficient
  247. Hypothesis Testing with the Pearson Correlation Coefficient
  248. Partial Correlation
  249. Data Ethics: Correlation, Causation, and Big Data
  250. Applying Correlation in Psychometrics
  251. Reliability
  252. Validity
  253. Review of Concepts
  254. SPSS
  255. How it Works
  256. Exercises
  257. Terms
  258. Chapter 16 Regression
  259. Simple Linear Regression
  260. Prediction Versus Relation
  261. Regression with z Scores
  262. Determining the Regression Equation
  263. The Standardized Regression Coefficient and Hypothesis Testing with Regression
  264. Interpretation and Prediction
  265. Regression and Error
  266. Applying the Lessons of Correlation to Regression
  267. Regression to the Mean
  268. Multiple Regression
  269. Understanding the Equation
  270. Multiple Regression in Everyday Life
  271. Data Ethics: Ethical Landmines in Predicting Individual Behavior
  272. Review of Concepts
  273. SPSS
  274. How it Works
  275. Exercises
  276. Terms
  277. Chapter 17 Chi-Square Tests
  278. Nonparametric Statistics
  279. An Example of a Nonparametric Test
  280. When to Use Nonparametric Tests
  281. Chi-Square Tests
  282. Chi-Square Test for Goodness of Fit
  283. Chi-Square Test for Independence
  284. Adjusted Standardized Residuals
  285. Beyond Hypothesis Testing
  286. Cramér’s V, the Effect Size for Chi Square
  287. Graphing Chi-Square Percentages
  288. Data Ethics: Relative Risk
  289. Review of Concepts
  290. SPSS
  291. How it Works
  292. Exercises
  293. Terms
  294. Chapter 18 Choosing and Reporting Statistics
  295. Choosing the Right Statistical Test
  296. Category 1: Two Scale Variables
  297. Category 2: Nominal Independent Variable(s) and a Scale Dependent Variable
  298. Category 3: Only Nominal Variables
  299. Reporting Statistics
  300. Overview of Reporting Statistics
  301. Justifying the Study
  302. Reporting the Traditional and the New Statistics
  303. Data Ethics: Open Data Practices
  304. Review of Concepts
  305. How it Works
  306. Exercises
  307. Appendix A: Reference for Basic Mathematics
  308. Appendix B: Statistical Tables
  309. Appendix C: Solutions to Odd-Numbered End-of-Chapter Problems
  310. Appendix D: Check Your Learning Solutions
  311. Glossary
  312. References
  313. Index
  314. Formulas
  315. Back Cover

 

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