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Book
xli, 596 pages ; 26 cm
  • Acknowledgments
  • Introduction and descriptive statistics
  • Introduction to statistics
  • Summarizing data : frequency distributions in tables and graphs
  • Summarizing data : central tendency
  • Summarizing data: variability
  • Probability and the foundations of inferential statistics
  • Probability, normal distributions, and z scores
  • Characteristics of the sample mean
  • Hypothesis testing : significance, effect size, and power
  • Making inferences about one or two means
  • Testing means : one-sample t test with confidence intervals
  • Testing means : two-independent-sample t test with confidence intervals
  • Testing means : related-samples t test with confidence intervals
  • Making inferences about the variability of two or more means
  • One-way analysis of variance : between-subjects and within-subjects (repeated-measures) designs
  • Two-way analysis of variance : between-subjects factorial design
  • Making inferences about patterns, prediction, and nonparametric tests
  • Correlation and linear regression
  • Chi-square tests : goodness-of-fit and the test for independence
  • Afterword
  • Appendix A: Basic math review and summation notation
  • Appendix B: SPSS general instruction guide
  • Appendix C: Statistical tables
  • Appendix D: Chapter solutions for even-numbered problems
  • Glossary
  • References
  • Index.
Revised edition of the author's Essential statistics for the behavioral sciences, 2016.
(source: Nielsen Book Data)9781506386300 20180409
Science Library (Li and Ma)
Book
xiii, 213 pages ; 23 cm
  • Acknowledgments Chapter 1: Brief Introduction to Research in the Social, Behavioral, and Health Sciences What Is the Purpose of Research? How Is Research Done? Scientific Method and Hypothesis Testing Inductive Research Deductive Research Research Designs Chapter 2: Variables and Measurement Variables and Data Levels of Variable Measurement Types of Relationships Research Design and Measurement Quality Chapter 3: How to Sample and Collect Data for Analysis Why Use a Sample? Probability Sampling Methods Nonprobability Sampling Methods Validating a Sample Split Ballot Designs How and Where Are Data Collected Today? Chapter 4: Data Frequencies and Distributions Univariate Frequencies and Relative Frequencies Cumulative Percentages and Percentiles Frequencies for Quantitative Data Univariate Distributions The Normal Distribution Non-Normal Distribution Characteristics Data Transformations for Dealing With Non-Normal Distributions Bivariate Frequencies Chapter 5: Using and Interpreting Univariate and Bivariate Visualizations Univariate Data Visualization Bivariate Data Visualization Chapter 6: Central Tendency and Variability Understanding How to Calculate and Interpret Measures of Central Tendency Understanding How Individuals in a Distribution Vary Around a Central Tendency Chapter 7: What Are z Scores, and Why Are They Important? What Is a z Score? How to Calculate a z Score The Standard Normal Table Working With the Standard Normal Distribution to Calculate z Scores, Raw Scores, and Percentiles Confidence Intervals Chapter 8: Hypothesis Testing and Statistical Significance Null and Alternative Hypotheses Statistical Significance Test Statistic Distributions Choosing a Test of Statistical Significance The Chi-Square Test of Independence The Independent Samples t Test One-Way Analysis of Variance Chapter 9: How to Measure the Relationship Between Nominal and Ordinal Variables Choosing the Correct Measure of Association Trying to Reduce Error (PRE Statistics) Calculating and Interpreting Lambda Calculating and Interpreting Gamma Calculating and Interpreting Somers' d Calculating and Interpreting Kendall's Tau-b Interpreting PRE Statistics Overview Chapter 10: Effect Size Effect Size Choosing an Effect Size Chapter 11: How to Interpret and Report Regression Results What Is a Regression? Correlation Bivariate Regression Coefficient of Determination (r2) Multiple Regression Logistic Regression Chapter 12: Indices, Typologies, and Scales Indices, Typologies, and Scales Defined and Explained Appendix A. The Standard Normal Table Appendix B. Critical Values for t Statistic Appendix C. Critical Values for Chi-Square Appendix D. Critical Values for F Statistic Appendix E. Glossary About the Authors.
  • (source: Nielsen Book Data)9781526402493 20180521
Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help those students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.
(source: Nielsen Book Data)9781526402493 20180521
Science Library (Li and Ma)
Book
1 online resource.
  • Preface ................................................................ ........................................................... 1Chapter 1 - Basics of regression models ................................................................ .. 21.1. Types and applications of regression models. ................................................................ .............. 21.2. Basic elements of a single-equation linear regression model. ..................................................... 4Chapter 2 - Relevance of outlying and influential observations for regression analysis ................................................................ ..................................... 72.1. Nature and dangers of univariate and multivariate outlying observations. ................................ 72.2. Tools for detection of outlying observations. ................................................................ ............. 192.3. Recommended procedure for detection of outlying and influential observations. .................... 322.4. Dealing with detected outlying and influential observations. .................................................... 33Chapter 3 - Basic procedure for multiple regression model building ............. 353.1. Introduction. ................................................................ ............................................................... 353.2. Preliminary specification of the model. ................................................................ ...................... 353.3. Detection of potential outliers in the dataset. ................................................................ ........... 403.4. Selection of explanatory variables (from the set of candidates). ............................................... 483.5. Interpretation of the obtained regression' structural parameters. ............................................ 57Chapter 4 - Verification of multiple regression model ...................................... 604.1. Introduction. ................................................................ ............................................................... 604.2. Testing general statistical significance of the whole model: F test. ........................................... 614.3. Testing the normality of regression residuals' distribution. ....................................................... 634.4. Testing the autocorrelation of regression residuals. ................................................................ .. 724.5. Testing the heteroscedasticity of regression residuals. .............................................................. 874.6. Testing the symmetry of regression residuals. ................................................................ ........... 974.7. Testing the randomness of regression residuals. ................................................................ ..... 1064.8. Testing the specification of the model: Ramsey's RESET test. ................................................. 1154.9. Testing the multicollinearity of explanatory variables. ............................................................ 1214.10. What to do if the model is not correct? ................................................................ .................. 1254.11. Summary of verification of our model ................................................................ .................... 130Chapter 5 - Common adjustments to multiple regressions .............................. 1325.1. Dealing with qualitative factors by means of dummy variables. ............................................. 1325.2. Modeling seasonality by means of dummy variables. ............................................................. 1365.3. Using dummy variables for outlying observations. ................................................................ .. 1482815.4. Dealing with structural changes in modeled relationships. ..................................................... 1555.5. Dealing with in-sample non-linearities. ................................................................ .................... 164Chapter 6 - Common pitfalls in regression analysis .......................................... 1716.1. Introduction. ................................................................ ............................................................. 1716.2. Distorting impact of multicollinearity on regression parameters. ........................................... 1716.3. Analyzing incomplete regressions. ................................................................ ........................... 1766.4. Spurious regressions and long-term trends. ................................................................ ............. 1806.5. Extrapolating in-sample relationships too far into out-of-sample ranges. .............................. 1866.6. Estimating regressions on too narrow ranges of data. ............................................................ 1936.7. Ignoring structural changes within modeled relationships and within individual variables. ... 197Chapter 7 - Regression analysis of discrete dependent variable .................... 2097.1. The nature and examples of discrete dependent variables. ..................................................... 2097.2. The discriminant analysis. ................................................................ ........................................ 2097.3. The logit function. ................................................................ ..................................................... 218Chapter 8 - Real-life case-study: The quarterly sales revenues of Nokia Corporation..................................................... .......................................................... 2238.1. Introduction. ................................................................ ............................................................. 2238.2. Preliminary specification of the model. ................................................................ .................... 2238.3. Detection of potential outliers in the dataset ................................................................ .......... 2258.4. Selection of explanatory variables (from the set of candidates). ............................................. 2318.5. Verification of the obtained model. ................................................................ .......................... 2348.6. Evaluation of the predictive power of the estimated model. ................................................... 246Chapter 9 - Real-life case-study: Identifying overvalued and undervalued airlines ................................................................ ........................................................ 2529.1. Introduction. ................................................................ ............................................................. 2529.2. Preliminary specification of the model. ................................................................ .................... 2529.3. Detection of potential outliers in the dataset ................................................................ .......... 2549.4. Selection of explanatory variables (from the set of candidates). ............................................. 2589.5. Verification of the obtained model. ................................................................ .......................... 2599.6. Evaluation of model usefulness in identifying overvalued and undervalued stocks. ............... 268Appendix - Statistical Tables ................................................................ ................... 271A1. Critical values for F-statistic for k = 0,05............................................................ ..................... 271A2. Critical values for t-statistic. ................................................................ ...................................... 273A3. Critical values for Chi-squared statistic. ................................................................ .................... 274282A4. Critical values for Hellwig test. ................................................................ .................................. 275A5. Critical values for symmetry test for k = 0,10. ................................................................ ........ 276A6. Critical values for maximum series length test for k = 0,05. ................................................... 276A7. Critical values for number of series test for k = 0,05. ............................................................. 277.
  • (source: Nielsen Book Data)9783319711553 20180226
This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies. Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.
(source: Nielsen Book Data)9783319711553 20180226
Book
xv, 642 pages : illustrations (some color) ; 24 cm
  • 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.
  • (source: Nielsen Book Data)9781446298480 20180530
'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.
(source: Nielsen Book Data)9781446298480 20180530
Green Library
Book
xviii, 699 pages : illustrations ; 24 cm.
Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries the world s largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development. The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions. In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors. Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA Explores new statistical modeling strategies and explores their economic and business implications Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is must-reading for researchers, academics, graduate students, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution.
(source: Nielsen Book Data)9781118979341 20180604
Green Library
Book
1 online resource.
Book
314 pages, 1 unnumbered page : illustrations, chart ; 21 cm.
  • Introduction : Construire un monde commun grâce aux sciences ? -- Cimenter le social -- L'ordre par le nombre -- L'espérance par la planification -- Construire des choses qui tiennent -- Quelle place pour la formalisation? -- Pour aller plus loin -- Comment les sciences naturelles construisent un savoir fiable -- D'où vient la fiabilité des sciences naturelles ? -- La fiabilité, en pratique -- Les abstractions comme outils -- Les abstractions comme résumés -- Pour aller plus loin -- Galilée a-t-il découvert la loi de la chute des corps ? -- La physique de la chute -- Le premier laboratoire -- Le feuillet inédit -- Que veut dire "découvrir" ? -- Les obstacles dus à la matière -- Transformer sans déformer -- Les mathématiques et le monde -- Pour aller plus loin -- Les atomes des physiciens sont-ils atomiques ? -- Les atomes métaphysiques -- Les qualités des atomes -- À quoi servent les atomes, en pratique? -- Les atomes relient, mais n'expliquent pas -- Pour aller plus loin -- Prédire le temps -- Grâce aux équations de la physique? -- Des résistances -- Une Terre virtuelle apprivoisée -- Prédire le climat -- Pourquoi les climatologues ont-ils réussi? -- Pour aller plus loin -- Simuler la société ? -- Des quartiers ségrégés impliquent-ils des habitants racistes ? -- Un modèle utile et dangereux -- Pour aller plus loin -- Physique des élections, piège à c. -- Des rideaux de fumée mathématiques -- Pour aller plus loin -- Compétition économique sur la plage -- Un principe fragile -- Pour aller plus loin -- Sommes-nous des fourmis compliquées ? -- Les nids de fourmis, en principe. -- Et en pratique -- Les piétons -- À quoi servent ces modèles? -- Pour aller plus loin -- Des actions sous influence -- Reproduire les inégalités scolaires dans un ordinateur -- Quand le magnétisme aide l'économie -- Pour aller plus loin -- Modéliser les épidémies -- La force d'une épidémie -- Par quels chemins se propage l'épidémie ? -- Intégrer les données digitales -- Quelle place pour la modélisation ? -- Pour aller plus loin -- Prédire l'économie? -- Le futur est comme le passé -- Les actions des atomes sociaux -- Pour aller plus loin -- Prédire grâce aux big data ? -- Prédire le succès des tweets -- Je suis qui je suis -- Le manque de transférabilité -- Qui est au chômage? -- Pour aller plus loin -- À quoi servent les modèles de la société ? -- Pour aller plus loin -- Analyser la société -- La naissance des statistiques -- Le Dieu du social joue-t-il aux dés ? -- Ni société, ni atomes sociaux -- Des différences de moyennes aux différences de faits -- Pour aller plus loin -- Des explications complexes ? -- Les explications générales -- Séparer les effets de différentes causes -- Des causes conjoncturelles, dont l'effet dépend du contexte -- Qui agit ? -- Descriptions, récits -- Oublions les explications scientifiques ? -- Pour aller plus loin -- Les big data, pour comprendre ou coordonner ? -- Peu, c'est mieux -- L'intermédiation numérique -- Pour aller plus loin.
"Croissance économique, classements des lycées, publicités sur le web : de plus en plus, nos actions sont mises en chiffres, en équations, pour aiguiller ou prédire nos comportements. Les big data, ces abondantes traces numériques que nous produisons constamment, nous permettront-elles de créer une nouvelle science de la société, aussi performante que les sciences de la nature ? Peut-on s'inspirer des techniques de modélisation mathématique et de simulation informatique élaborées dans les sciences naturelles pour comprendre enfin la société et l'améliorer ? Une analyse de cette perspective s'avère urgente à l'aube de la révolution numérique. Grâce à sa double compétence de chercheur en physique et en sciences sociales, l'auteur décortique de nombreux cas concrets de quantification de nos activités, en les comparant aux mathématisations réussies de la physique. Il peut alors replacer ces exemples dans une perspective théorique générale, en expliquant les réussites, les échecs et les conséquences politiques d'une mise en équations du monde."--Page 4 of cover.
Green Library
Book
xxix, 496 pages ; 24 cm
  • Preface About the Authors Prologue PART I * GETTING STARTED Chapter 1: The Big Picture Models The Classical Statistical Model Designing Experiments and Analyzing Data Summary Questions Raised by the Use of the Classical Statistical Model Conceptual Exercises Chapter 2: Examining Our Data: An Introduction to Some of the Techniques of Exploratory Data Analysis Descriptive Statistics Histograms Exploratory Data Analysis Quantile Plots Stem-and-Leaf Displays Letter-Value Displays Box Plots Did My Data Come From a Normal Distribution? Why Should We Care About Looking at Our Data? Summary Conceptual Exercises PART II * THE BEHAVIOR OF DATA Chapter 3: Properties of Distributions: The Building Blocks of Statistical Inference The Effects of Adding a Constant or Multiplying by a Constant The Standard Score Transformation The Effects of Adding or Subtracting Scores From Two Different Distributions The Distribution of Sample Means The Central Limit Theorem Averaging Means and Variances Expected Value Theorems on Expected Value Summary Conceptual Exercises PART III * THE BASICS OF STATISTICAL INFERENCE: DRAWING CONCLUSIONS FROM OUR DATA Chapter 4: Estimating Parameters of Populations From Sample Data Statistical Inference With the Classical Statistical Model Criteria for Selecting Estimators of Population Parameters Maximum Likelihood Estimation Confidence Intervals Beyond Normal Distributions and Estimating Population Means Summary Conceptual Exercises Chapter 5: Resistant Estimators of Parameters A Closer Look at Sampling From Non-Normal Populations The Sample Mean and Sample Median Are L-Estimators Measuring the Influence of Outliers on Estimates of Location and Spread ?-Trimmed Means as Resistant and Efficient Estimators of Location Winsorizing: Another Way to Create a Resistant Estimator of Location Applying These Resistant Estimators to Our Data Resistant Estimators of Spread Applying These Resistant Estimators to Our Data (Part 2) M-Estimators: Another Approach to Finding Resistant Estimators of Location Which Estimator of Location Should I Use? Resampling Methods for Constructing Confidence Intervals A Final Caveat Summary Conceptual Exercises Chapter 6: General Principles of Hypothesis Testing Experimental and Statistical Hypotheses Estimating Parameters The Criterion for Evaluating Our Statistical Hypotheses Creating Our Test Statistic Drawing Conclusions About Our Null Hypothesis But Suppose H0 Is False? Errors in Hypothesis Testing Power and Power Functions The Use of Power Functions p-Values, a, and Alpha (Type I) Errors: What They Do and Do Not Mean A Word of Caution About Attempting to Estimate the Power of a Hypothesis Test After the Data Have Been Collected Is It Ever Appropriate to Use a One-Tailed Hypothesis Test? What Should We Mean When We Say Our Results Are Statistically Significant? A Final Word Summary Conceptual Exercises PART IV * SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS Chapter 7: The Independent Groups t-Tests for Testing for Differences Between Population Means Student's t-test Distribution of the Independent Groups t-Statistic when H0 Is True Distribution of the Independent Groups t-Statistic When H0 Is False Factors That Affect the Power of the Independent Groups t-Test The Assumption Behind the Homogeneity of Variance Assumption Graphical Methods for Comparing Two Groups Suppose the Population Variances Are Not Equal? Standardized Group Differences as Estimators of Effect Size Robust Hypothesis Testing Resistant Estimates of Effect Size Summary Conceptual Exercises Chapter 8: Testing Hypotheses When the Dependent Variable Consists of Frequencies of Scores in Various Categories Classifying Data Testing Hypotheses When the Dependent Variable Consists of Only Two Possibilities The Binomial Distribution Testing Hypotheses About the Parameter p in a Binomial Experiment The Normal Distribution Approximation to the Binomial Distribution Testing Hypotheses About the Difference Between Two Binomial Parameters (p1 - p2) Testing Hypotheses in Which the Dependent Variable Consists of Two or More Categories Summary Conceptual Exercises Chapter 9: The Randomization/Permutation Model: An Alternative to the Classical Statistical Model for Testing Hypotheses About Treatment Effects The Assumptions Underlying the Classical Statistical Model The Assumptions Underlying the Randomization Model Hypotheses for Both Models The Exact Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior The Approximate Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior Using the Randomization Model to Investigate Possible Effects of Treatments Single-Participant Experimental Designs Summary Conceptual Exercises Additional Resources Chapter 10: Exploring the Relationship Between Two Variables: Correlation Measuring the Degree of Relationship Between Two Interval-Scale Variables Randomization (Permutation) Model for Testing Hypotheses About the Relationship Between Two Variables The Bivariate Normal Distribution Model for Testing Hypotheses About Population Correlations Creating a Confidence Interval for the Population Correlation Using the Bivariate Normal Distribution Model Bootstrap Confidence Intervals for the Population Correlation Unbiased Estimators of the Population Correlation Robust Estimators of Correlation Assessing the Relationship Between Two Nominal Variables The Fisher Exact Probability Test for 2 x 2 Contingency Tables With Small Sample Sizes Correlation Coefficients for Nominal Data in Contingency Tables Summary Conceptual Exercises Chapter 11: Exploring the Relationship Between Two Variables: The Linear Regression Model Assumptions for the Linear Regression Model Estimating Parameters With the Linear Regression Model Regression and Prediction Variance and Correlation Testing Hypotheses With the Linear Regression Model Summary Conceptual Exercises Chapter 12: A Closer Look at Linear Regression The Importance of Looking at Our Data Using Residuals to Check Assumptions Testing Whether the Relationship Between Two Variables Is Linear The Correlation Ratio: An Alternate Way to Measure the Degree of Relationship and Test for a Linear Relationship Where Do We Go From Here? When the Relationship Is Not Linear The Effects of Outliers on Regression Robust Alternatives to the Method of Least Squares A Quick Peek at Multiple Regression Summary Conceptual Exercises Chapter 13: Another Way to Scale the Size of Treatment Effects The Point Biserial Correlation Coefficient and the t-Test Advantages and Disadvantages of Estimating Effect Sizes With Correlation Coefficients or Standardized Group Difference Measures Confidence Intervals for Effect Size Estimates Final Comments on the Use of Effect Size Estimators Summary Conceptual Exercises Chapter 14: Analysis of Variance for Testing for Differences Between Population Means What Are the Sources of Variation in Our Experiments? Experimental and Statistical Hypotheses Estimating Variances When There Are More Than Two Conditions in Your Experiment Assumptions for Analysis of Variance Testing Hypotheses About Differences Among Population Means With Analysis of Variance Factors That Affect the Power of the F-Test in Analysis of Variance Relational Effect Size Measures for Analysis of Variance Randomization Tests for Testing for Differential Effects of Three or More Treatments Using ANOVA to Study the Effects of More Than One Factor on Behavior Partitioning Variance for a Two-Factor Analysis of Variance Testing Hypotheses With Two-Factor Analysis of Variance Testing Hypotheses About Differences Among Population Means With Analysis of Variance Dealing With Unequal Sample Sizes in Factorial Designs Summary Conceptual Exercises Chapter 15: Multiple Regression and Beyond Overview of the General Linear Model Approach Regression Simple Versus Multiple Regression Multiple Regression Types of Multiple Regression Interactions in Multiple Regression Continuous x Continuous Interactions Categorical x Continuous Interactions Categorical x Categorical Interactions: ANOVA Versus Regression Summary Conceptual Exercises Epilogue Appendices A. Some Useful Rules of Algebra B. Rules of Summation C. Logarithms D. The Inverse of the Cumulative Normal Distribution E. The Unit Normal Distribution F. The t-Distribution G. The Fisher r to zr Transformation H. Critical Values for F With Alpha = .05 I. The Chi Square Distribution References Index.
  • (source: Nielsen Book Data)9781483358598 20170508
This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying, stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the underlying theory behind statistical methods. By presenting examples and principles of statistics within the context of these timely issues, authors Jerome Frieman, Donald A. Saucier, and Stuart S. Miller show how the results of analyses can be used to answer research questions. New techniques for data analysis and a wide range of topics are covered, including how to deal with "messy data" and the importance of engaging in exploratory data analysis.
(source: Nielsen Book Data)9781483358598 20170508
Science Library (Li and Ma)
Book
xvi, 456 pages : illustrations ; 26 cm
  • 1. Foundations of Statistical Modeling Demonstrated with Simple Regression 2. Multiple Regression with Continuous Predictors 3. Regression with Categorical Predictors 4. Interactions in Multiple Regression: Models for Moderation 5. Using Multiple Regression to Model Mediation and Other Indirect Effects 6. Introduction to Multilevel Modeling 7. Basic Matrix Algebra for Statistical Modeling 8. Exploratory Factor Analysis 9. Structural Equation Modeling I: Path Analysis 10. Structural Equation Modeling II: Latent Variable Models 11. Growth Curve Modeling.
  • (source: Nielsen Book Data)9781446269831 20180423
Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
(source: Nielsen Book Data)9781446269831 20180423
Science Library (Li and Ma)
Book
xliii, 650 pages, 114 variously numbered pages ; 26 cm
  • About the author
  • Acknowledgments
  • Preface to the instructor
  • To the student : how to use spss with this book
  • Introduction and descriptive statistics
  • Introduction to statistics
  • Summarizing data: frequency distributions in tables and graphs
  • Summarizing data: central tendency
  • Summarizing data: variability
  • Probability and the foundations of inferential statistics
  • Probability
  • Probability, normal distributions, and Z scores
  • Probability and sampling distributions
  • Making inferences about one or two means
  • Hypothesis testing: significance, effect size, and power
  • Testing means : one-sample and two-independent-sample t tests
  • Testing means : the related-samples T test
  • Estimation and confidence intervals
  • Making inferences about the variability of two or more means
  • Analysis of variance: one-way between-subjects design
  • Analysis of variance : one-way within-subjects (repeated-measures) design
  • Analysis of variance: two-way between-subjects factorial design
  • Making inferences about patterns, frequencies, and ordinal data
  • Correlation
  • Linear regression and multiple regression
  • Nonparametric tests: chi-square tests
  • Nonparametric tests: tests for ordinal data
  • Afterword
  • Appendix A. Basic math review and summation notation
  • Appendix B. SPSSs general instructions guide
  • Appendix C. Statistical tables
  • Appendix D. Chapter solutions for even-numbered problems
  • Glossary
  • References.
Science Library (Li and Ma)
Book
xiv, 151 pages ; 22 cm.
  • 1. Introduction 2. Getting Started 3. Temporal Analysis with Cross-Sectional Data 4. Analysis of Data on Durations 5. Analysis of Repeated Contacts Data 6. Conclusion Bibliography Index.
  • (source: Nielsen Book Data)9781472515407 20180508
Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution. The book is designed for postgraduates and researchers across the social sciences considering the use of quantitative longitudinal data resources in their research. It will also be an excellent text for undergraduate and postgraduate courses on advanced quantitative methods.
(source: Nielsen Book Data)9781472515407 20180508
Science Library (Li and Ma)
Book
274 pages : map, facsimiles ; 21 cm.
SAL3 (off-campus storage)
Book
8, 455 pages ; 31 cm + 1 CD-ROM (4 3/4 in.)
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East Asia Library
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SAL1&2 (on-campus shelving)
Book
8, 519 pages ; 31 cm
East Asia Library
Book
8, 509 pages ; 31 cm + 1 CD-ROM (4 3/4 in.)
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SAL1&2 (on-campus shelving)
Book
8, 638 pages ; 31 cm + 1 CD-ROM (4 3/4 in.)
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East Asia Library
Book
8, 514 pages ; 31 cm + 1 CD-ROM (4 3/4 in.)
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East Asia Library
Book
8, 468 pages ; 31 cm + 1 CD-ROM (4 3/4 in.)
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East Asia Library