Data Analysis for the Social Sciences: Integrating Theory and Practice

Data Analysis for the Social Sciences: Integrating Theory and Practice

By: Douglas Bors (author)Hardback

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Description

'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' -Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' -Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.

About Author

Dr. Douglas Alexander Bors is currently an Associate Professor Emeritus at the University of Toronto, the institution from which he received his Ph.D. For over three decades he has taught courses in statistics at the undergraduate and graduate levels ranging from the introductory level to multivariate statistics and structural equation modelling. He is also the Dean's Designate for Matter of Academic Integrity. He has served as a reviewer for several scholarly journals and has been a regular reviewer for Personality and Individual Differences. During his career his empirical research focused on problem solving and abstract reasoning. The short-form of the Advanced Raven's Progressive Matrices that he developed with Tonya Stokes is a widely used research instrument. Over the years he has acted as a consultant for major pharmaceutical firms as well as survey and marketing companies. Currently he is retained as a design and analysis consultant for a major marketing firm.

Contents

Part I: The Foundations Chapter 1: Overview The general framework Recognizing randomness Lies, damn lies, and statistics Testing for randomness Research design and key concepts Paradoxes Chapter 2: Descriptive Statistics Numerical Scales Histograms Measures of Central Tendency: Measurement Data Measures of Spread: Measurement Data What creates Variance? Measures of Central Tendency: Categorical Data Measures of Spread: Categorical Data Unbiased Estimators Practical SPSS Summary Chapter 3: Probability Approaches to probability Frequency histograms and probability The asymptotic trend The terminology of probability The laws of probability Bayes' Rule Continuous variables and probability The standard normal distribution The standard normal distribution and probability Using the z-tables Part II: Basic Research Designs Chapter 4: Categorical data and hypothesis testing The binomial distribution Hypothesis testing with the binomial distribution Conducting the binomial test with SPSS Null hypothesis testing The x2 goodness-of-fit test The x2 goodness-of-fit test with more than two-categories Conducting the x2 goodness-of-fit test with SPSS Power and the x2 goodness-of-fit test G -test Can a failure to reject indicate support for a model? Chapter 5: Testing for a Difference: Two Conditions Building on the z-score Testing a single sample Independent-samples t-test t-test assumptions Pair-samples t-test Confidence limits and intervals Randomization test and bootstrapping Nonparametric tests Chapter 6: Observational studies: Two categorical variables x2 goodness-of-fit test reviewed x2 test of independence The phi coefficient Necessary assumptions x2 test of independence SPSS example Power, sample size, and the x2 test of independence The third-variable problem Multi-category nominal variables Tests of independence with ordinal variables Chapter 7: Observational studies: Two measurement variables Tests of association for categorical data reviewed The scatterplot Covariance The Pearson-Product Moment Correlation Coefficient Simple regression analysis The Ordinary Least Squares Regression Line (OLS) The assumptions necessary for valid correlation and regression coefficients Chapter 8: Testing for a difference: Multiple between-subject conditions (ANOVA) Reviewing the t-test and the x2 test of independence The logic of ANOVA: Two unbiased estimates of o2 ANOVA and the F-test Standardized effect sizes and the F-test Using SPPS to run an ANOVA F-test: Between-subjects design The third-variable problem: Analysis of covariance (ANCOVA) Non-parametric alternatives Chapter 9: Testing for a difference: Multiple related-samples Reviewing the between-subject ANOVA and the t-test The logic of the randomized block design Running a randomized block design with SPSS The logic of the repeated-measures design Running a repeated-measures design with SPSS Non-parametric alternatives Chapter 10: Testing for specific differences: Planned and unplanned tests A priori versus post hoc tests Per-comparison versus family-wise error rates Planned comparisons: A priori test Testing for polynomial trends Unplanned comparisons: Post hoc tests Non-parametric follow-up comparisons Part III: Analyzing Complex Designs Chapter 11: Testing for Differences: ANOVA and Factorial Designs Reviewing the independent-samples ANOVA The logic of factorial designs: Two between-subject independent variables Main and simple effects Two Between-Subject Factorial ANOVA with SPSS Fixed versus random factors Analyzing a mixed-design ANOVA with SPSS Non-parametric alternatives Chapter 12: Multiple Regression Regression revisited Introducing a second predictor A detailed example Issues concerning normality Missing data Testing for linearity and homoscedasticity A multiple regression: The first pass Addressing multicollinearity Interactions What can go wrong? Chapter 13: Factor analysis What is factor analysis? Correlation coefficients revisited The correlation matrix and PCA The component matrix The rotated component matrix A detailed example Choosing a method of rotation Sample size requirements Hierarchical multiple factor analysis The effects of variable selection

Product Details

  • ISBN13: 9781446298473
  • Format: Hardback
  • Number Of Pages: 664
  • ID: 9781446298473
  • weight: 1320
  • ISBN10: 1446298477

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