# 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. .. sppn.info sppn.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.

Biserial and point-biserial correlations Partial correlation 6.

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The theory behind part and partial correlation 6. Partial correlation using R 6. Comparing independent rs 6. Comparing dependent rs Calculating the effect size How to report correlation coefficents What have I discovered about statistics?

## Discovering Statistics Using SPSS

An introduction to regression 7. Some important information about straight lines 7.

The method of least squares 7. Assessing the goodness of fit: sums of squares, R and R2 7.

## Log in to Wiley Online Library

Assessing individual predictors Packages used in this chapter General procedure for regression in R 7. Doing simple regression using R Commander 7. Regression in R Interpreting a simple regression 7. Overall fit of the object model 7.

Model parameters 7. Using the model Multiple regression: the basics 7. An example of a multiple regression model 7. Sums of squares, R and R2 7. Parsimony-adjusted measures of fit 7. Methods of regression How accurate is my regression model? Assessing the regression model I: diagnostics 7. Some things to think about before the analysis 7. Multiple regression: running the basic model 7. Interpreting the basic multiple regression 7.

Comparing models 7. Diagnostic tests using R Commander 7. Outliers and influential cases 7. Assessing the assumption of independence 7. Assessing the assumption of no multicollinearity 7.

## Hm Are You a Human?

Checking assumptions about the residuals 7. 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. 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. 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?

## Discovering Statistics Using SPSS

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?

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 View larger image. Andy Field Sign Up Now! Synopsis Unrivalled in the way it makes the teaching of statistics compelling and accessible to even the most anxious of students, the only statistics textbook you and your students will ever need just got better!

The Fourth Edition of the award-winning book continues, with its unique blend of humour and collection of bizarre examples, to bring statistics - from first principles to advanced concepts - well and truly to life using IBM SPSS Statistics.

They get instant feedback and also links to the relevant chapter or section in the integral ebook to help them work out the correct solution. Students please note: SAGE MobileStudy - study where and when you like Scan any QR code within the book to access revision material on a smartphone or tablet such as Cramming Sam's Study tips, flashcard glossaries, interactive multiple choice questionnaires and more.

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.

Support materials for a wide range of disciplines Education and Sport Sciences lecturer support materials with enhanced ones for Psychology, Business and Management and the Health Sciences on the enhanced Companion Website make the book even more relevant to a wider range of subjects across the social sciences and where statistics is taught to a cross-disciplinary audience. 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.They get instant feedback and also links to the relevant chapter or section in the integral ebook to help them work out the correct solution.

The assumption of sphericity How is sphericity measured? Two new characters! Step 4: create adjusted values for each variable The t-test 9. Mean squares The independent t-test equation explained 9.

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.

Setting contrasts Two new characters!

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