A guide to doing statistics in second language research using SPSS and R / Jenifer Larson-Hall

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Pte. 1ª: Getting started with the software and using the computer for experimental details. -- Getting started with SPSS. -- Opening a data file. -- Entering your own data. -- Application activity for getting started with SPSS. -- Importing into SPSS. -- Saving your work in SPSS. -- Application activities for importing and saving files. -- Getting started with R. -- Downloading ans installing R. -- Customizing R in Windows. -- Loading packages and R commander. -- A list of all the R packages used in this book. -- Working with data in R and R commander. -- Entering your own data. -- Importing files into R through R commander. -- Viewing entered data. -- Saving data and reading it back in. -- Saving graphics files. -- Closing R and R commander. -- Application activities practicing entering data into R. -- Understanding the R environment. -- Using R as a calculator. -- Using R as a calculator practice activities. -- Objects in R. -- Creating objects in R practice activities. -- Types of data in R. -- Types of data practice activities. -- Funtions in R. -- Fuctions in R practice activities. -- The R workspace. -- Specifying variables within a data set, and attaching and detaching data sets. -- Missing data. -- Missing data and multiple imputacion in SPSS. -- Missing data application activity in SPSS. -- Missing data and multiple imputation in R. -- Getting help. -- Getting help with SPSS. -- Getting help with R. -- Some preliminaries to understanding statistics. -- Variables. -- Levels of measurement of variables. -- Application activities: practice of identifying levels of measurement. -- Dependent and independent variables. -- Application activities: practice in identifying variables. -- Summary variables. -- Fixed versus random effects (advanced topic). -- Understanding hidden assumption about how statistical testing works. -- Hypothesis testing. -- Application activities: creating null hypotheses. -- Who ges tested? Populations versus samples and inferential statistics. -- What does a P-Value mean?. -- Effects sizes. -- Understanding statistical reporting. -- Application activities: understanding statistical reporting. -- The inner workings of statistical testing. -- Application activity: the inner workings of statistical testing. -- Summary of hidden assumptions. -- Parametric and non-parametric statistics. -- Why robust statistics?. -- Describing data numerically and graphically and assessing assumptions for parametric test. -- Numerical summaries of data. -- The mean, median and mode. -- Standard deviation, variance and standard error. -- Confidence intervals. -- The number of observations and other numerical summaries you might want to report. -- Reporting numerical summaries. -- Data for this chapter. -- Using SPSS to get numerical summaries. -- Obtaining numerical summaries with SPSS and splitting groups. -- Application activities for numerical summaries in SPSS. -- Using R to get numerical summaries. -- Basic descriptive statistics in R. -- Application activities for numerical summaries in R. -- Satisfaying assumptions for parametric test. -- Graphic summaries of data: examining the shape of distribution for normality. -- Histograms. -- Skewness and kurtosis. -- Stem and leaf plots. -- Quantile-quantile plots. -- Obtaining exploratory visual summaries in SPSS. -- Application activities: looking at normality assumptions. -- Obtaining exploratory visual summaries in R. -- Creating histograms with R. -- Creating stem and leaf plots with R. -- Creating Q-Q plots with R. -- Testing for normality with R. -- Application activities: looking at normality assumptions with R. -- Checking homo geneity of variance (with SPSS or R). -- Dealing with departures from expectations. -- Outliers. -- Transforming data. -- Changing the way we do statistics: the new statistics. -- Introduction to confidence intervals. -- Application activity for ESCI and confidence intervals. -- Interpreting confidence intervals. -- Applicatino activities with confidence intervals and precision. -- Introduction to effect sizes. -- Understanding effect size measures. -- Interpreting effect sizes. -- Calculating effect sizes summary. -- Effect size confidence intervals. -- Some explanations fo the "Old" statistics. -- Null hypotheses significance tests. -- One-tailed versus two-tailed test of hypotheses. -- Outcomes of null hypotheses significance testing. -- Power analysis. -- Claculating effect sizes for power analysis. -- Examples of power analyses. -- Application activities with power calculation. -- Precision instead of power. -- Application activities with precision calculation. -- Summary. -- Power through replication and belief in the "Law of small numbers". -- Pte. 2ª: Statistical test. -- Choosing statistical test. -- Statistical test that are covered in this book. -- A brief overview of correlation. -- Correlation: a test of relationship. -- A brief overview of multiple regression. -- Multiple regression: multiple regression: a test of relationships. -- Brief overview of the Chi-square test of independence. -- Chi-square: a test of relationships. -- A brief overview of T-test. -- T-test: a test of group differences. -- A brief overview of the independence samples T-test. -- A brief overview of the paired samples T-test. -- A brief overview of one-way analysis of variance: a test of group differences. -- A brief overview of factorial analysis of variance. -- Factorial analysis of variance: a test of group differences. -- A brief overview of analysis of covariance. -- Analysis of covariance: a test of group differences. -- A brief overview of repeated-measures analysis of variance. -- Repeated-measures analysis of variance: a test of group differences. -- Summary. -- Application activities for choosing a statistical test. -- Finding relationships using correlation. age of learning. -- Visual inspection: scatterplots. -- The topic of chapter 6. -- Creating scatterplots in SPSS. -- Adding a regression or Loess line. -- Viewing simple scatterplot data by categories. -- Creating scatterplots in R. -- Modifying a scatterplot in R console. -- Viewing simple scatterplot data by categories. -- Appplication activities with scatterplots. -- Creating multiple scatterplots with SPSS. -- Creating multiple scatterplots with. -- Interpreting multiple scatterplots. -- Assumptions of parametric statistics for correlation. -- Effect size for correlation. -- Cofindence interval for correlations. -- Claculating correlation coefficients and confidence intervals. -- Calculating correlation coefficients and confidence intervals in SPSS. -- Calculating correlation coefficients and confidence intervals in R. -- Robust correlations. -- Application activities for correlation. -- Reporting a correlation. -- Looking for groups of explanatory variables through multiple regression: predicting important factors in firs grade reading. -- Understanding regression design. -- Standard multiple regression. -- Sequential (Hierarchical) regression. -- Data used in this chapter. -- Visualizinf multiple relationship. -- Graphs in R for understanding complex relationship: conditioning plots. -- Graphs in R for understanding complex relationship: 3-D graphs. -- Graphs in R for understanding complex relationships: tree models. -- Application activities in R with graphs for understanding complex relationships. -- Assumptions of multiple regression. -- Assumptions of multiple regression. -- Assumptions of multiple about sample size. -- Performing a multiple regression. -- Starting the multiple regression in SPSS. -- Regression output in SPSS. -- Examining regression assumptions using SPSS. -- Robust regression with SPSS. -- Linear regression in R: doing the same type of regression as in SPSS. -- Examining regression assumptions analysis in R. -- Robust linear regression in R. --Reporting the results of a regression analysis. -- Application activities: multiple regression. -- Looking for differences between two means with T-test: think-aloud methodology and teaching sarcasm. -- Types of T-test. -- Application activity: choosing a T-test. -- Data summaries and numerical inspection. -- Visual inspection: box plots. -- Box plots for one variable separated by groups in SPSS. -- Box plots for one variable separated by groups in R. -- Box plots for more than one variable plotted on the same graph in SPSS. -- Box plots for more than one variable plotted on the same graph in R. -- Box plots for more tha one variable separated by groups in SPSS and R. -- Application activities with box plots. -- Assumptions of T-tests. -- Adjustments for multiple T-test (Bonferroni adjusment, false discovery rate). -- Data formatting for tests of groups differences (the "Wilde form" and "Long form"). -- The independent samples T-test. -- Performing and independent samples T-test in SPSS. -- Performing an independent samples T-test in R. -- Performing a bootstrapped independent samples T-test in R. -- Performing a bootstrapped, 20% trimmed means, independent samples T-test in R. -- Effect sizes for independent samples T-tests. -- Reporting results of an independet samples T-test. -- Application activities for an independet samples T-test. --The paired sampels T-test. -- Performing a paired samples T-test in SPSS. -- One-sided versus two-sided confidence intervals. -- Performing a paired samples T-test in R. -- Performing a robust paired samples T-test in SPSS. -- One-sided versus two-sided confidence intervals. -- Performing a pair samples T-test in R. -- Performing a robust paired samples T-test in R. -- Effect sizes for paired samples T-test. -- Application activities with paired samples T-test. -- Reporting the results of a paired samples T-test. -- Summary T-test. -- Looking for a group differences with one-way analyses of variance: effects of planning time. -- Understanding the analysis of variance design. -- The topic of chapter 9. -- Numerical and visual inspection of the data of this chapter. -- Assumptions for an analysis of variance. -- One-way analysis of variance. -- Omnibus test with post-hoc test or planned comparisons. -- Testing for group equivalence before an experimental procedure. -- Performing an omnibus one-way analysis of variance test in SPSS with subsequent post-hoc test. -- performing an omnibus one-way analysis of variance in R with subsequent post-hoc test. -- Performing bootsrapped one-way analysis of variance in R. -- Conducting one-way analysis of variance using planned comparisons. -- Conducting planned comparisons in SPSS. -- Conducting planned comparisons in R. -- Effect sizes in one-way analysis of variance. -- Application activities with one-way analysis one-way analysis of variance. -- Reporting the results of a one-way analyses of variance. -- Looking for group differences with factorial analysis of variance when there is more than one independent variable: learning with music. -- Analysis of variance design: interaction. -- Application activity in understanding interaction. -- Analysis of variance design of the Obarow study. -- Analysis of variance design: variable or level?. -- Application activity: identifying independent variables and levels. -- Numerical and visual inspection. -- Creating a combination box plot and means plot in R. -- Assumptions of a factorial analysis of variance. -- Getting ready to perform a factorial analysis of variance. -- Making sure your data is in the correct format for a factorial analysis of variance. -- Rearranging data for a factorial analysis of vriance using SPSS. -- Rearranging data for a factorial analysis of variance using R. -- Excursus on type II vs. type III sums of squares (Advanced topic). -- Factorial analysis of variance: extending analyses to more than one independent variable. -- Performing a three-way factorial analysis of variance with SPSS. -- Performing a three-way factorial analysis of variance using R. -- A confidence interval approach to a factorial ANOVA (Advanced topic). -- Planned comparisons in a factorial analysis of variance. -- Performing planned comparisons in a factorial analysis of variance for SPSS and R. -- Effect sizes for factorial analysis of variance. -- Application activities with factorial analysis of variance. -- Reporting the results of a factorial analysis of variance. -- Summary. -- Looking for group differences when the same people are tested mora tha once: repeated-measures analysis of varaince with wug tests and instruction on french gender. -- Understanding repeated-measures analysis of variance designs. -- Repeated-measures analysis of variance designs of the Murphy (2004) study. -- Repeated-measures analysis of variance design of the Lyster (2004) study. -- Application activity: identifying between-groups and within-groups variables to decide between repeated-measures and factorial analysis of variance design. -- Arranging the data for a repeated-measures analysis of variance. -- Arranging data for repeated-measures analysis of variance in SPSS. -- Changing for wide form to long form in SPSS. -- Arranging the data for a repeated-measures analysis of variance in R. -- Application activities for canging data from the wide to the long form (Necessary for use with the R program only). -- Visualizing repeated-measures data. -- Exploring the Murphy (2004) and Lyster (2004) data with the combination interaction plot and box plot. -- Parallel coordinate plots. -- Creating a parallel coordinate plot in SPSS. -- Creating a parallel coordinate plot in R. -- Application activities with parallel coordinate plots. -- Repeated-measures analysis of variance assumptions. -- Exploring model assumptions. -- Performing a repeated-measures analysis of variance with the Least-squares approach. -- Least-squares repeated-measures analysis of variance in SPSS. -- Repeated-measures analysis of variance output. -- Least-squares repeated-measures analysis of variance in R. -- Application activities with least-squares, repeated-measures analysis of variance. -- Furthering a repeated-measures analysis by exploring simple interaction effects and simple main effects. -- Exploring simple interaction effects and and simple and simple main affects in the Murphy (2004) data (SPSS and R). -- Reporting the results of a repeated-measures analysis of variance. -- Application activities with further exloration of repeated-measures analysis of variance using simple interaction effects ans simple main affects. -- Appendix A: Doing things in R.

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