ANDY FIELD DISCOVERING STATISTICS PDF
Andy Field First edition published Second edition published Apart from any fair dealing for the purposes of research or private study, or. Andy Field First edition .. 21 Epilogue: life after discovering statistics. . book (or downloaded an illegal PDF of it from someone who has way too. Reprinted , , (twice), What the hell am I doing here? I don't belong.. Effect B00B1TJO10 EBOK Discovering Statistics Using SPSS.
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Module Convenor: Professor Andy Field C Discovering Statistics. Professor .. Discovering Statistics two components: (1) statistical analysis and ( 2) empirical research. .. pixia-club.info pixia-club.info Machine Learning and Deep Learning Resources. Contribute to Avkash/mldl development by creating an account on GitHub. Lecturers/instructors - request a free digital inspection copy here With a little help from his weird band of characters, Andy Field has transformed the way students.
What if I violate an assumption? Robust regression: bootstrapping 7. How to report multiple regression 7. Categorical predictors and multiple regression 7.
Dummy coding 7. Regression with dummy variables What have I discovered about statistics? Background to logistic regression What are the principles behind logistic regression?
Assessing the model: the log-likelihood statistic 8. Assessing the model: the deviance statistic 8. Assessing the model: R and R2 8.
Download Discovering Statistics using IBM SPSS Statistics / Edition 4 ebook PDF
Assessing the model: information criteria 8. Assessing the contribution of predictors: the z-statistic 8. The odds ratio 8. Methods of logistic regression Assumptions and things that can go wrong 8. Assumptions 8. Preparing the data 8. The main logistic regression analysis 8.
Basic logistic regression analysis using R 8. Interpreting a basic logistic regression 8.
Discovering Statistics using IBM SPSS Statistics (4th edition) (PDF)
Model 1: Intervention only 8. Model 2: Intervention and Duration as predictors 8. Casewise diagnostics in logistic regression 8. Calculating the effect size How to report logistic regression Testing assumptions: another example 8. Testing for multicollinearity 8.
Testing for linearity of the logit Predicting several categories: multinomial logistic regression 8. Running multinomial logistic regression in R 8. Interpreting the multinomial logistic regression output 8.
Reporting the results What have I discovered about statistics? Packages used in this chapter Looking at differences - 13 - 9. A problem with error bar graphs of repeated-measures designs Step 1: calculate the mean for each participant Step 2: calculate the grand mean Step 3: calculate the adjustment factor 9. Step 4: create adjusted values for each variable The t-test 9.
Rationale for the t-test 9. The t-test as a general linear model 9.
Assumptions of the t-test The independent t-test 9. The independent t-test equation explained 9. Doing the independent t-test The dependent t-test 9. Sampling distributions and the standard error 9. The dependent t-test equation explained 9.
Dependent t-tests using R Between groups or repeated measures? What have I discovered about statistics? Interpreting F ANOVA as regression Total sum of squares SS T Model sum of squares SSM Residual sum of squares SS R Mean squares Homogeneity of variance Planned contrasts Choosing which contrasts to do Defining contrasts using weights Non-orthogonal comparisons Standard contrasts Polynomial contrasts: trend analysis Post hoc procedures Post hoc procedures and violations of test assumptions Entering data Exploring the data The main analysis Planned contrasts using R Independence of the covariate and treatment effect Homogeneity of regression slopes Are the predictor variable and covariate independent?
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Interpreting the covariate Some final remarks Testing for homogeneity of regression slopes Calculating the effect size Reporting results - 16 - What have I discovered about statistics? Factorial designs An example with two independent variables Extending the regression model Total sums of squares SST The model sum of squares SSM The residual sum of squares SSR The F-ratios Entering the data Choosing contrasts Interpreting contrasts - 17 - Simple effects analysis Post hoc analysis Overall conclusions Interpreting interaction graphs Calculating effect sizes Introduction to repeated-measures designs The assumption of sphericity How is sphericity measured?
Assessing the severity of departures from sphericity What is the effect of violating the assumption of sphericity? What do you do if you violate sphericity?
The total sum of squares SST The within-participant sum of squares SS W - 18 - The mean squares The F-ratio The between-participant sum of squares One-way repeated-measures designs using R Packages for repeated measures designs in R General procedure for repeated-measures designs Analysing repeated measures: two ways to skin a. Robust one-way repeated-measures ANOVA Effect sizes for repeated-measures designs Reporting one-way repeated-measures designs Factorial repeated-measures designs Setting contrasts Factorial repeated-measures designs as a GLM Robust factorial repeated-measures ANOVA Effect sizes for factorial repeated-measures designs Reporting the results from factorial repeated-measures designs What have I discovered about statistics?
Discovering Statistics Using R by Andy Field, Jeremy Miles, Zoë Field
Mixed designs What do men and women look for in a partner? Entering and exploring your data Packages for mixed designs in R General procedure for mixed designs Mixed designs as a GLM Building the model The main effect of gender The main effect of looks The main effect of personality The interaction between gender and looks The interaction between gender and personality The interaction between looks and personality The interaction between looks, personality and gender Conclusions - 20 - Robust analysis for mixed designs What have I discovered about statistics?
When to use non-parametric tests Packages used in this chapter Comparing two independent conditions: the Wilcoxon rank-sum test Theory of the Wilcoxon rank-sum test Inputting data and provisional analysis Running the analysis using R Commander Running the analysis using R Output from the Wilcoxon rank-sum test Calculating an effect size Writing the results Comparing two related conditions: the Wilcoxon signed-rank test - 21 - Theory of the Wilcoxon signed-rank test Running the analysis with R Commander Running the analysis using R Wilcoxon signed-rank test output Calculating an effect size Writing the results Differences between several independent groups: the Kruskal—Wallis Theory of the Kruskal—Wallis test Inputting data and provisional Other major new updates include: Two new characters!
The enhanced Companion Website offers plenty of lecturer and student material to use in conjunction with the textbook. These include PowerPoints and subject-specific testbanks for lecturers as well as answers to the Smart Alex tasks at the end of the each chapter; datafiles for testing problems in SPSS; flashcards of key concepts; self-assessment multiple-choice questions; and online videos of key statistical and SPSS procedures discussed in the textbook for students.
Teaching stats Gibson or Fender Ask Andy Anything: The one part of the book Andy hated writing Copyright: No Language: English Has Image Descriptions: No Categories: Nonfiction , Business and Finance Submitted By: Bookshare Staff Usage Restrictions: This is a copyrighted book.Creating coding variables with R Commander Using other software to enter and edit data 3. Using the model Multiple regression: the basics 7.
A detour into the murky world of covariance 6. Click here to take a look if you're accessing the site from a desktop you'll be taken to the Companion Website instead; look out for the MobileStudy icon to show you which pages are also available on the MobileStudy site.
Packages used in this chapter Looking at differences - 13 - 9. Testing for homogeneity of regression slopes Calculating new variables from exisiting ones 3. The enhanced Companion Website offers plenty of lecturer and student material to use in conjunction with the textbook. How to report multiple regression 7. The total sum of squares SST