000			04636nam a22002417a 4500
999			_c4189 _d4189
005			20221205161519.0
008			221205b ||||| |||| 00| 0 eng d
020			_a9781544344027
082			_a519.502855133 _bGIL
100			_aGillespie, Brian Joseph _99388
245			_aA guide to R for social and behavioral science statistics
260			_bSage Publications, Inc. _aLos Angeles _c2021
300			_axv284 p.
365			_aGBP _b45.00
504			_aChapter 1 • R and RStudio® Introduction Statistical Software Overview Downloading R and RStudio RStudio Finding R and RStudio Packages Opening Data Saving Data Files Conclusion Chapter 2 • Data, Variables, and Data Management About the Data and Variables Structure and Organization of Classic “Wide” Datasets The General Social Survey Variables and Measurement Recoding Variables Logic of Coding Recoding Missing Values Computing Variables Removing Outliers Conclusion Chapter 3 • Data Frequencies and Distributions Frequencies for Categorical Variables Cumulative Frequencies and Percentages Frequencies for Interval/Ratio Variables Histograms The Normal Distribution Non-Normal Distribution Characteristics Exporting Tables Conclusion Chapter 4 • Central Tendency and Variability Measures of Central Tendency Measures of Variability The z-Score Selecting Cases for Analysis Conclusion Chapter 5 • Creating and Interpreting Univariate and Bivariate Data Visualizations Introduction R’s Color Palette Univariate Data Visualization Bivariate Data Visualization Exporting Figures Conclusion Chapter 6 • Conceptual Overview of Hypothesis Testing and Effect Size Introduction Null and Alternative Hypotheses Statistical Significance Test Statistic Distributions Choosing a Test of Statistical Significance Hypothesis Testing Overview Effect Size Conclusion Chapter 7 • Relationships Between Categorical Variables Single Proportion Hypothesis Test Goodness of Fit Bivariate Frequencies The Chi-Square Test of Independence (?2) Conclusion Chapter 8 • Comparing One or Two Means Introduction One-Sample t-Test The Independent Samples t-Test Examples Additional Independent Samples t-Test Examples Effect Size for t-Test: Cohen’s d Paired t-Test Conclusion Chapter 9 • Comparing Means Across Three or More Groups (ANOVA) Analysis of Variance (ANOVA) ANOVA in R Two-Way Analysis of Variance Conclusion Chapter 10 • Correlation and Bivariate Regression Review of Scatterplots Correlations Pearson’s Correlation Coefficient Coefficient of Determination Correlation Tests for Ordinal Variables The Correlation Matrix Bivariate Linear Regression Logistic Regression Conclusion Chapter 11 • Multiple Regression The Multiple Regression Equation Interaction Effects and Interpretation Logistic Regression Interpretation and Presentation of Logistic Regression Results Conclusion Chapter 12 • Advanced Regression Topics Advanced Regression Topics Polynomials Logarithms Scaling Data Multicollinearity Multiple Imputation Further Exploration Conclusion Index
520			_aA Guide to R for Social and Behavioral Science Statistics is a short, accessible book for learning R. This handy guide contains basic information on statistics for undergraduates and graduate students, shown in the R statistical language using RStudio®. The book is geared toward social and behavioral science statistics students, especially those with no background in computer science. Written as a companion book to be used alongside a larger introductory statistics text, the text follows the most common progression of statistics for social scientists. The guide also serves as a companion for conducting data analysis in a research methods course or as a stand-alone R and statistics text. This guide can teach anyone how to use R to analyze data, and uses frequent reminders of basic statistical concepts to accompany instructions in R to help walk students through the basics of learning how to use R for statistics.
650			_aPsychology--Statistical methods _93829
650			_aR (Computer program language) _91512
650			_aSocial sciences--Statistical methods _91897
700			_aHibbert, Kathleen Charli _910544
700			_aWagner, William E. _910545
942			_2ddc _cBK