- 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
(source: Nielsen Book Data)9781483358598 20170508
- 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
(source: Nielsen Book Data)9781483358598 20170508
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
HA29 .F76812 2018 | Unknown |
2. Statistics for the behavioral sciences [2018]
- 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.
- 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)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
HA29 .P755 2018 | Unknown |
- Book
- 274 pages : map, facsimiles ; 21 cm.
SAL3 (off-campus storage)
SAL3 (off-campus storage) | Status |
---|---|
In process | Request |
HA4556.5 .A4 A19 2017 | Unavailable |
4. America's diverse population : a comparison of race, ethnicity, and social class in graphic detail [2017]
- Book
- xvi, 390 pages : color charts, maps ; 28 cm
The composition of the American population is rapidly changing from a white, male dominated society to one that is so diverse it will soon be without any single, dominant race, ethnicity or gender. The dramatic demographic shifts in American society have provoked many false claims and distortions of facts that have fueled demagoguery, as occurred during the 2016 presidential campaign. Access to unvarnished facts about people different than you-but who are becoming your neighbors-is more critical now than ever. This book was created to provide a single source of easily accessible facts-obtained primarily from U.S. government agencies-comparing characteristics of race, ethnicity and gender in graphic format to enhance comprehension, as only visual presentations can achieve. Virtually all major socio-economic topics are covered, including geographic distribution of populations, birth rates, health, wealth, poverty, income, employment, crime, incarcerations, social behaviors, education and political preferences. Included are past and future trends for many characteristics, as are comparisons between foreign-born, natural citizens, legal and undocumented immigrants. Special Features: *Socio-economic characteristics between races, ethnicities, and genders in America *Comparisons include: health, education, wealth, poverty, income, employment, crime, incarcerations, social behaviors, geographic distributions, and political preferences *Includes foreign-born and natural citizens, lawful and undocumented immigrants *All data are graphically displayed for easy visualization and comprehension *Attributed sources for all data include web addresses to enable additional research *Only factual data are presented without editorial comments or opinions Interesting facts found in America's Diverse Population include: *More than one-in-eight persons residing in the U.S. in 2015 were born elsewhere. *Approximately one-in-four persons with "Green Cards" resided in California in 2013. *Over three million temporary workers were admitted into the U.S. in 2014. *In 2009 over two-thirds of convictions of undocumented immigrants were for violations of immigration law, and two percent for crimes against persons. *Approximately one-of-three Black or African American children live in households with both married parents, compared with ninety-five percent of Asian households. *In 2013 one-of-eight high school age Hispanic or Latino females reported they were forced to have sex. *In 2015 ninety-seven percent of kindergarten teachers were women. *Approximately one-half of all maids and housekeeping cleaners in 2015 were Hispanic or Latino women. *In 2015, almost one-in-four Asian females held a Master's degree, the highest rate of any race or ethnicity. *In 2013, the number of Non-Hispanic White children in the U.S., grades K-8, fell below fifty percent of students for the first time.
(source: Nielsen Book Data)9781598889147 20171121
(source: Nielsen Book Data)9781598889147 20171121
The composition of the American population is rapidly changing from a white, male dominated society to one that is so diverse it will soon be without any single, dominant race, ethnicity or gender. The dramatic demographic shifts in American society have provoked many false claims and distortions of facts that have fueled demagoguery, as occurred during the 2016 presidential campaign. Access to unvarnished facts about people different than you-but who are becoming your neighbors-is more critical now than ever. This book was created to provide a single source of easily accessible facts-obtained primarily from U.S. government agencies-comparing characteristics of race, ethnicity and gender in graphic format to enhance comprehension, as only visual presentations can achieve. Virtually all major socio-economic topics are covered, including geographic distribution of populations, birth rates, health, wealth, poverty, income, employment, crime, incarcerations, social behaviors, education and political preferences. Included are past and future trends for many characteristics, as are comparisons between foreign-born, natural citizens, legal and undocumented immigrants. Special Features: *Socio-economic characteristics between races, ethnicities, and genders in America *Comparisons include: health, education, wealth, poverty, income, employment, crime, incarcerations, social behaviors, geographic distributions, and political preferences *Includes foreign-born and natural citizens, lawful and undocumented immigrants *All data are graphically displayed for easy visualization and comprehension *Attributed sources for all data include web addresses to enable additional research *Only factual data are presented without editorial comments or opinions Interesting facts found in America's Diverse Population include: *More than one-in-eight persons residing in the U.S. in 2015 were born elsewhere. *Approximately one-in-four persons with "Green Cards" resided in California in 2013. *Over three million temporary workers were admitted into the U.S. in 2014. *In 2009 over two-thirds of convictions of undocumented immigrants were for violations of immigration law, and two percent for crimes against persons. *Approximately one-of-three Black or African American children live in households with both married parents, compared with ninety-five percent of Asian households. *In 2013 one-of-eight high school age Hispanic or Latino females reported they were forced to have sex. *In 2015 ninety-seven percent of kindergarten teachers were women. *Approximately one-half of all maids and housekeeping cleaners in 2015 were Hispanic or Latino women. *In 2015, almost one-in-four Asian females held a Master's degree, the highest rate of any race or ethnicity. *In 2013, the number of Non-Hispanic White children in the U.S., grades K-8, fell below fifty percent of students for the first time.
(source: Nielsen Book Data)9781598889147 20171121
(source: Nielsen Book Data)9781598889147 20171121
Green Library
Green Library | Status |
---|---|
Find it Jonsson Social Sciences Reading Room: New books | |
HA214 .D85 2017 | Unknown |
5. Applied spatial modelling and planning [2017]
- Book
- xxiv, 362 pages, 8 unnumbered pages of plates : illustrations (some color), color maps ; 24 cm.
- Chapter 1: Introduction John Lombard, Eli Stern and Graham Clarke Chapter 2: Applied spatial modelling: 'big science' and 'best practice' challenges Sir Alan Wilson Part I Dynamics of economic space Chapter 3: An exploratory analysis of new firm formation in New England Jitendra Parajuli and Kingsley E. Haynes Chapter 4: The impacts of policy changes on overseas human captial in Australia: the implementation of the 485 graduate visa Jonathan Corcoran, Francisco Rowe, Alessandra Faggian, and Robert Stimson Chapter 5: On the three 'laws' of spatial interaction and a string theory finale: perspectives from social physics with examples in the digital and retail economy Robert G.V. Baker Part II Housing and settlements Chapter 6: Spatial typology of the private rental housing market at neighbourhood scale: the case of South East Queensland, Australia Yan Liu, David Wadley and Jonathan Corcoran Chapter 7: Optimal kernel and bandwidth specifications for geographically weighted regression: an evaluation using automated valuation models (AVMS) for mass real estate appraisal Paul E. Bidanset and John R. Lombard Chapter 8: Spatial search: new settlements for Israel's ultra-orthodox population Eliahu Stern Chapter 9: A tale of two earthquakes: dynamic agent-based simulation of urban resilience A. Yair Grinberger and Daniel Felsenstein Part III Population dynamics and population ageing Chapter 10: The United Kingdom's multi-ethnic future: how fast is it arriving? Philip Rees, Pia Wohland and Paul Norman Chapter 11: Decomposition of life expectancy at older ages and prospects for ageing populations Leslie Mayhew and David Smith Chapter 12: Using agent-based modelling to understand crime phenomena Nick Addis Part IV Health care planning and analysis Chapter 13: Modelling the impact of new community hospitals on access to health care Holly Shulman, Graham Clarke and Mark Birkin Chapter 14: SimSALUD - towards a health decision support system for regional planning Melanie N. Tomintz and Victor M. Garcia-Barrios Chapter 15: Small-scale agent-based modelling of infectious disease transmission: an example in a primary school Mike Bithell Chapter 16: Exploring small-area geographies of obesity in the UK: evidence from the UK Women's Cohort Study Michelle A. Morris, Graham Clarke, Kimberley L. Edwards, Claire Hulme and Janet E. Cade Chapter 17: Examining the socio-spatial determinants of depression in the UK Karyn Morrissey, Peter Kinderman, Eleanor Pontin, Sara Tai and Matthias Schwannauer Part V Environmental modelling Chapter 18: A case study of flooding in the Limpopo River Basin, Xai-Xai, Mozambique Robert Fligg and Joana Barros Chapter 19: A computational framework for mitigating land degradation: a synergy of knowledge from physical geography and geoinformatics Tal Svoray.
- (source: Nielsen Book Data)9781138925700 20170321
(source: Nielsen Book Data)9781138925700 20170321
- Chapter 1: Introduction John Lombard, Eli Stern and Graham Clarke Chapter 2: Applied spatial modelling: 'big science' and 'best practice' challenges Sir Alan Wilson Part I Dynamics of economic space Chapter 3: An exploratory analysis of new firm formation in New England Jitendra Parajuli and Kingsley E. Haynes Chapter 4: The impacts of policy changes on overseas human captial in Australia: the implementation of the 485 graduate visa Jonathan Corcoran, Francisco Rowe, Alessandra Faggian, and Robert Stimson Chapter 5: On the three 'laws' of spatial interaction and a string theory finale: perspectives from social physics with examples in the digital and retail economy Robert G.V. Baker Part II Housing and settlements Chapter 6: Spatial typology of the private rental housing market at neighbourhood scale: the case of South East Queensland, Australia Yan Liu, David Wadley and Jonathan Corcoran Chapter 7: Optimal kernel and bandwidth specifications for geographically weighted regression: an evaluation using automated valuation models (AVMS) for mass real estate appraisal Paul E. Bidanset and John R. Lombard Chapter 8: Spatial search: new settlements for Israel's ultra-orthodox population Eliahu Stern Chapter 9: A tale of two earthquakes: dynamic agent-based simulation of urban resilience A. Yair Grinberger and Daniel Felsenstein Part III Population dynamics and population ageing Chapter 10: The United Kingdom's multi-ethnic future: how fast is it arriving? Philip Rees, Pia Wohland and Paul Norman Chapter 11: Decomposition of life expectancy at older ages and prospects for ageing populations Leslie Mayhew and David Smith Chapter 12: Using agent-based modelling to understand crime phenomena Nick Addis Part IV Health care planning and analysis Chapter 13: Modelling the impact of new community hospitals on access to health care Holly Shulman, Graham Clarke and Mark Birkin Chapter 14: SimSALUD - towards a health decision support system for regional planning Melanie N. Tomintz and Victor M. Garcia-Barrios Chapter 15: Small-scale agent-based modelling of infectious disease transmission: an example in a primary school Mike Bithell Chapter 16: Exploring small-area geographies of obesity in the UK: evidence from the UK Women's Cohort Study Michelle A. Morris, Graham Clarke, Kimberley L. Edwards, Claire Hulme and Janet E. Cade Chapter 17: Examining the socio-spatial determinants of depression in the UK Karyn Morrissey, Peter Kinderman, Eleanor Pontin, Sara Tai and Matthias Schwannauer Part V Environmental modelling Chapter 18: A case study of flooding in the Limpopo River Basin, Xai-Xai, Mozambique Robert Fligg and Joana Barros Chapter 19: A computational framework for mitigating land degradation: a synergy of knowledge from physical geography and geoinformatics Tal Svoray.
- (source: Nielsen Book Data)9781138925700 20170321
(source: Nielsen Book Data)9781138925700 20170321
- Book
- xvi, 356 pages : illustrations ; 25 cm
- Research and statistics
- Introduction to Stata
- Simple (bivariate) regression
- Multiple regression
- Dummy-variable regression
- Interaction/moderation effects using regression
- Linear regression assumptions and diagnostics
- Logistic regression
- Multilevel analysis
- Panel data analysis
- Exploratory factor analysis
- Structural equation modelling and confirmatory factor analysis
- Critical issues.
(source: Nielsen Book Data)9781473913233 20170424
- Research and statistics
- Introduction to Stata
- Simple (bivariate) regression
- Multiple regression
- Dummy-variable regression
- Interaction/moderation effects using regression
- Linear regression assumptions and diagnostics
- Logistic regression
- Multilevel analysis
- Panel data analysis
- Exploratory factor analysis
- Structural equation modelling and confirmatory factor analysis
- Critical issues.
(source: Nielsen Book Data)9781473913233 20170424
7. The butcher, the baker, the candlestick maker : the story of Britain through its census, since 1801 [2017]
- Book
- xi, 340 pages ; 25 cm
- A perfect man
- Censuses, taxation and war
- A hazy snapshot from the air
- The first modern census
- Seamstresses, prostitutes, billiard-markers and footballers
- Lathes and rapes
- Two parrots, one canary and innumerable mice
- The British babel
- A nation of emigrants
- Aliens
- Empire
- Great War
- The fractured kingdom
- Depression
- Welcome home.
(source: Nielsen Book Data)9781408707012 20170321
- A perfect man
- Censuses, taxation and war
- A hazy snapshot from the air
- The first modern census
- Seamstresses, prostitutes, billiard-markers and footballers
- Lathes and rapes
- Two parrots, one canary and innumerable mice
- The British babel
- A nation of emigrants
- Aliens
- Empire
- Great War
- The fractured kingdom
- Depression
- Welcome home.
(source: Nielsen Book Data)9781408707012 20170321
8. The butcher, the baker, the candlestick-maker : the story of Britain through its census, since 1801 [2017]
- Book
- xi, 340 pages ; 24 cm
At the beginning of each decade for 200 years the national census has presented a self-portrait of the British Isles. The census has surveyed Britain from the Napoleonic wars to the age of the internet, through the agricultural and industrial revolutions, possession of the biggest empire on earth and the devastation of the 20th century's two world wars. In The Butcher, the Baker, the Candlestick Maker, Roger Hutchinson looks at every census between the first in 1801 and the latest in 2011. He uses this much-loved resource of family historians to paint a vivid picture of a society experiencing unprecedented changes. Hutchinson explores the controversial creation of the British census. He follows its development from a head-count of the population conducted by clerks with quill pens, to a computerised survey which is designed to discover 'the address, place of birth, religion, marital status, ability to speak English and self-perceived national identity of every twenty-seven-year-old Welsh-speaking Sikh metalworker living in Swansea'. All human life is here, from prime ministers to peasants and paupers, from Irish rebels to English patriots, from the last native speakers of Cornish to the first professional footballers, from communities of prostitutes to individuals called 'abecedarians' who made a living from teaching the alphabet. The Butcher, the Baker, the Candlestick Maker is as original and unique as those people and their islands on the cutting edge of Europe.
(source: Nielsen Book Data)9781408707012 20170321
(source: Nielsen Book Data)9781408707012 20170321
At the beginning of each decade for 200 years the national census has presented a self-portrait of the British Isles. The census has surveyed Britain from the Napoleonic wars to the age of the internet, through the agricultural and industrial revolutions, possession of the biggest empire on earth and the devastation of the 20th century's two world wars. In The Butcher, the Baker, the Candlestick Maker, Roger Hutchinson looks at every census between the first in 1801 and the latest in 2011. He uses this much-loved resource of family historians to paint a vivid picture of a society experiencing unprecedented changes. Hutchinson explores the controversial creation of the British census. He follows its development from a head-count of the population conducted by clerks with quill pens, to a computerised survey which is designed to discover 'the address, place of birth, religion, marital status, ability to speak English and self-perceived national identity of every twenty-seven-year-old Welsh-speaking Sikh metalworker living in Swansea'. All human life is here, from prime ministers to peasants and paupers, from Irish rebels to English patriots, from the last native speakers of Cornish to the first professional footballers, from communities of prostitutes to individuals called 'abecedarians' who made a living from teaching the alphabet. The Butcher, the Baker, the Candlestick Maker is as original and unique as those people and their islands on the cutting edge of Europe.
(source: Nielsen Book Data)9781408707012 20170321
(source: Nielsen Book Data)9781408707012 20170321
SAL3 (off-campus storage)
SAL3 (off-campus storage) | Status |
---|---|
Stacks | Request |
HA37 .G72 H88 2017 | Available |
9. China ethnic statistical yearbook 2016 [2017]
- Book
- xxv, 391 pages : 2 color maps ; 22 cm
- Chapter 1: Population Growth and Structural Change.- Chapter 2: Macroeconomic Growth and Structural Change.- Chapter 3: Employment and Income Distribution.- Chapter 4: Living Conditions and the Means of Livelihood.- Chapter 5: Agricultural Production and Other Rural Activity.- Chapter 6: Education, Science and Technological Progress.- Chapter 7: Health Care and Social Security.- Chapter 8: Entertainment and Other Cultural Activity.
- (source: Nielsen Book Data)9783319491981 20171211
(source: Nielsen Book Data)9783319491981 20171211
- Chapter 1: Population Growth and Structural Change.- Chapter 2: Macroeconomic Growth and Structural Change.- Chapter 3: Employment and Income Distribution.- Chapter 4: Living Conditions and the Means of Livelihood.- Chapter 5: Agricultural Production and Other Rural Activity.- Chapter 6: Education, Science and Technological Progress.- Chapter 7: Health Care and Social Security.- Chapter 8: Entertainment and Other Cultural Activity.
- (source: Nielsen Book Data)9783319491981 20171211
(source: Nielsen Book Data)9783319491981 20171211
- Book
- xvii, 356 pages : illustrations ; 25 cm
How could the same person be classified by the US census as black in 1900, mulatto in 1910, and white in 1920? The history of categories used by the US census reflects a country whose identity and self-understanding-particularly its social construction of race-is closely tied to the continuous polling on the composition of its population. By tracing the evolution of the categories the United States used to count and classify its population from 1790 to 1940, Paul Schor shows that, far from being simply a reflection of society or a mere instrument of power, censuses are actually complex negotiations between the state, experts, and the population itself. The census is not an administrative or scientific act, but a political one. Counting Americans is a social history exploring the political stakes that pitted various interests and groups of people against each other as population categories were constantly redefined. Utilizing new archival material from the Census Bureau, this study pays needed attention to the long arc of contested changes in race and census-making. It traces changes in how race mattered in the United States during the era of legal slavery, through its fraught end, and then during (and past) the period of Jim Crow laws, which set different ethnic groups in conflict. And it shows how those developing policies also provided a template for classifying Asian groups and white ethnic immigrants from southern and eastern Europe-and how they continue to influence the newly complicated racial imaginings informing censuses in the second half of the twentieth century and beyond. Focusing in detail on slaves and their descendants, on racialized groups and on immigrants, and on the troubled imposition of U.S. racial categories upon the populations of newly acquired territories, Counting Americans demonstrates that census-taking in the United States has been at its core a political undertaking shaped by racial ideologies that reflect its violent history of colonization, enslavement, segregation and discrimination.
(source: Nielsen Book Data)9780199917853 20170731
(source: Nielsen Book Data)9780199917853 20170731
How could the same person be classified by the US census as black in 1900, mulatto in 1910, and white in 1920? The history of categories used by the US census reflects a country whose identity and self-understanding-particularly its social construction of race-is closely tied to the continuous polling on the composition of its population. By tracing the evolution of the categories the United States used to count and classify its population from 1790 to 1940, Paul Schor shows that, far from being simply a reflection of society or a mere instrument of power, censuses are actually complex negotiations between the state, experts, and the population itself. The census is not an administrative or scientific act, but a political one. Counting Americans is a social history exploring the political stakes that pitted various interests and groups of people against each other as population categories were constantly redefined. Utilizing new archival material from the Census Bureau, this study pays needed attention to the long arc of contested changes in race and census-making. It traces changes in how race mattered in the United States during the era of legal slavery, through its fraught end, and then during (and past) the period of Jim Crow laws, which set different ethnic groups in conflict. And it shows how those developing policies also provided a template for classifying Asian groups and white ethnic immigrants from southern and eastern Europe-and how they continue to influence the newly complicated racial imaginings informing censuses in the second half of the twentieth century and beyond. Focusing in detail on slaves and their descendants, on racialized groups and on immigrants, and on the troubled imposition of U.S. racial categories upon the populations of newly acquired territories, Counting Americans demonstrates that census-taking in the United States has been at its core a political undertaking shaped by racial ideologies that reflect its violent history of colonization, enslavement, segregation and discrimination.
(source: Nielsen Book Data)9780199917853 20170731
(source: Nielsen Book Data)9780199917853 20170731
Green Library
Green Library | Status |
---|---|
Find it Jonsson Social Sciences Reading Room: New books | |
HA181 .S3413 2017 | Unknown |
- Book
- 1 online resource (364 pages) : illustrations.
- Classifying the usage of multiple objective decision making techniques in data envelopment analysis / A. Ghazi [and 3 others]
- A survey on models and methods for preference voting and aggregation / Ali Ebrahimnejad, Farhad Hosseinzadeh Lotfi
- Application of Malmquist productivity index in integrated units of power plant / Elahe Shariatmadari Serkani, Seyed Esmaeil Najafi, Arash Nejadi
- Evaluation of faculties by DEA-ANP hybrid algorithm of chapter: educational-research performance / Elahe Shariatmadari Serkani
- Evaluation of supplier performance and efficiency: a critical analysis / Chandra Sekhar Patro
- Productivity assessment in data envelopment analysis / M. Vaez-Ghasmi, Z. Moghaddas
- Ranking models in data envelopment analysis technique / Z. Moghaddas, M. Vaez-Ghasemi
- Performance evaluation of suppliers with undesirable outputs using DEA / Alireza Shayan Arani, Hamed Nozari, Meisam Jafari-Eskandari.
(source: Nielsen Book Data)9781522505969 20161213
- Classifying the usage of multiple objective decision making techniques in data envelopment analysis / A. Ghazi [and 3 others]
- A survey on models and methods for preference voting and aggregation / Ali Ebrahimnejad, Farhad Hosseinzadeh Lotfi
- Application of Malmquist productivity index in integrated units of power plant / Elahe Shariatmadari Serkani, Seyed Esmaeil Najafi, Arash Nejadi
- Evaluation of faculties by DEA-ANP hybrid algorithm of chapter: educational-research performance / Elahe Shariatmadari Serkani
- Evaluation of supplier performance and efficiency: a critical analysis / Chandra Sekhar Patro
- Productivity assessment in data envelopment analysis / M. Vaez-Ghasmi, Z. Moghaddas
- Ranking models in data envelopment analysis technique / Z. Moghaddas, M. Vaez-Ghasemi
- Performance evaluation of suppliers with undesirable outputs using DEA / Alireza Shayan Arani, Hamed Nozari, Meisam Jafari-Eskandari.
(source: Nielsen Book Data)9781522505969 20161213
12. Dernekpazarı nüfus defteri 1834-1846 [2017]
- Book
- 318 pages : facsimiles ; 24 cm
SAL3 (off-campus storage)
SAL3 (off-campus storage) | Status |
---|---|
Stacks | Request |
HA4556.5 .Z9 D476 2017 | Available |
- Book
- 1 online resource ( xv, 202 pages.) :.
EBSCOhost Access limited to 1 user
- EBSCOhost Access limited to 1 user
- Google Books (Full view)
- Book
- xvii, 216 pages ; 23 cm
- Chapter 1: Introductory Ideas Regression Modeling Control Modeling Modeling Interactions Modeling Linearity With Splines Testing Research Hypotheses Classical Approach to Regression Disadvantages of Classical Approach Discrete Approach to Regression Summary Key Concepts Notes Chapter 2: Basic Statistical Procedures Individual Units and Groups Measurement Level of Measurement Examples for Level of Measurement Count, Sum, and Transformations Mean Proportion and Percentage Odds and Log odds Examples of Means and Log Odds Differences Summary Key Concepts Chapter Exercises Notes Chapter 3: Regression Modeling Basics Difference between Means: The t-test Linear Regression With a Two-Category Independent Variable Logistic Regression With a Two-Category Independent Variable Linear Regression With a Four-Category Independent Variable Logistic Regression With a Four-Category Independent Variable Modeling Linear Effect With Dummy Variables Linear Coefficient in Linear Regression Linear Coefficient in Logistic Regression Using Dummy Variables for a Continuous Variable Summary Key Concepts Chapter Exercises Notes Chapter 4: Key Regression Modeling Concepts Unit Vector: Estimating the Intercept Nestedness Higher-Order Differences Constraints Summary Key Concepts Chapter Exercises Notes Chapter 5: Control Modeling Elementary Control Modeling Elaboration for Controlling Demographic Standardization for Controlling Small and Big Models Allocating Influence With Multiple Control Variables One-at-a-Time Without Controls Step Approach One-at-a-Time With Controls Hybrid Approach Nestedness and Constraints Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 6: Modeling Interactions Interactions as Conditional Differences Interactions Between Dummy Variables Interactions Between Dummy Variables and an Interval Variable Three-Way Interactions Estimating Separate Models Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 7: Modeling Linearity With Splines Dummy Variables Nested in an Interval Variable Introduction to Knotted Spline Variables Spline Variables Nested in an Interval Variable Regression Modeling Using Spline Variables Working With a Continuous Independent Variable Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 8: Conclusion: Testing Research Hypotheses Bivariate Hypothesis/No Controls Bivariate Hypothesis/Unanalyzed Controls Bivariate Hypothesis/Analyzed Controls Hypothesis Involving Interactions Hypothesis Involving Nonlinearity Final Comments Key Concepts Summary Chapter exercises Notes.
- (source: Nielsen Book Data)9781506303475 20161024
(source: Nielsen Book Data)9781506303475 20161024
- Chapter 1: Introductory Ideas Regression Modeling Control Modeling Modeling Interactions Modeling Linearity With Splines Testing Research Hypotheses Classical Approach to Regression Disadvantages of Classical Approach Discrete Approach to Regression Summary Key Concepts Notes Chapter 2: Basic Statistical Procedures Individual Units and Groups Measurement Level of Measurement Examples for Level of Measurement Count, Sum, and Transformations Mean Proportion and Percentage Odds and Log odds Examples of Means and Log Odds Differences Summary Key Concepts Chapter Exercises Notes Chapter 3: Regression Modeling Basics Difference between Means: The t-test Linear Regression With a Two-Category Independent Variable Logistic Regression With a Two-Category Independent Variable Linear Regression With a Four-Category Independent Variable Logistic Regression With a Four-Category Independent Variable Modeling Linear Effect With Dummy Variables Linear Coefficient in Linear Regression Linear Coefficient in Logistic Regression Using Dummy Variables for a Continuous Variable Summary Key Concepts Chapter Exercises Notes Chapter 4: Key Regression Modeling Concepts Unit Vector: Estimating the Intercept Nestedness Higher-Order Differences Constraints Summary Key Concepts Chapter Exercises Notes Chapter 5: Control Modeling Elementary Control Modeling Elaboration for Controlling Demographic Standardization for Controlling Small and Big Models Allocating Influence With Multiple Control Variables One-at-a-Time Without Controls Step Approach One-at-a-Time With Controls Hybrid Approach Nestedness and Constraints Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 6: Modeling Interactions Interactions as Conditional Differences Interactions Between Dummy Variables Interactions Between Dummy Variables and an Interval Variable Three-Way Interactions Estimating Separate Models Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 7: Modeling Linearity With Splines Dummy Variables Nested in an Interval Variable Introduction to Knotted Spline Variables Spline Variables Nested in an Interval Variable Regression Modeling Using Spline Variables Working With a Continuous Independent Variable Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 8: Conclusion: Testing Research Hypotheses Bivariate Hypothesis/No Controls Bivariate Hypothesis/Unanalyzed Controls Bivariate Hypothesis/Analyzed Controls Hypothesis Involving Interactions Hypothesis Involving Nonlinearity Final Comments Key Concepts Summary Chapter exercises Notes.
- (source: Nielsen Book Data)9781506303475 20161024
(source: Nielsen Book Data)9781506303475 20161024
- Book
- 1 online resource (401 pages) : illustrations.
- Measuring cross-border regional integration with composite indicators: the Oresund integration index / Teemu Makkonen
- Developing a sustainable urban mobility index: methodological steps / Yongjun Shen, Elke Hermans
- Composite indices in technology management: a critical approach / Milica Jovanovic [and 4 others]
- Composite indicators of a knowledge society: triangulation of experts interviews and factor analyses / Norsiah Abdul Hamid [and 3 others]
- Composite indicators construction by data envelopment analysis: methodological background / Gordana Savic, Milan Martic
- Methodological challenges in building composite indexes: linking theory to practice / Fabio Gaetano Santeramo
- Composite indicators of development: some recent contributions / Sandrina B. Moreira, Nuno Crespo
- Constructing a multidimensional socioeconomic index and the validation of it with early child developmental outcomes / Vijaya Krishnan
- The rule of law index: is it really impartial? A twofold multivariate i-distance approach / Milica Maricic, Milica Bulajic, Milica Vasilijevic
- Comparison of input and output indicators in measuring human capital: an analysis at provincial level for Turkey / Sibel Bali
- The Delphi method application in the analysis of postgraduate students' attitudes on the environmental performance index / Natasa Petrovic [and 4 others]
- How age-friendly are cities?: measuring age-friendliness with a composite index / Lucie Vidovicová
- Using data envelopment analysis to construct human development index / Paulo Nocera Alves Junior, Enzo Barberio Mariano, Daisy Aparecida do Nascimento Rebelatto
- Statistical approach for ranking OECD countries based on composite GICSES index and I-distance method / Maja Mitrovic, Maja Markovic, Stefan Zdravkovic.
(source: Nielsen Book Data)9781522507147 20161213
- Measuring cross-border regional integration with composite indicators: the Oresund integration index / Teemu Makkonen
- Developing a sustainable urban mobility index: methodological steps / Yongjun Shen, Elke Hermans
- Composite indices in technology management: a critical approach / Milica Jovanovic [and 4 others]
- Composite indicators of a knowledge society: triangulation of experts interviews and factor analyses / Norsiah Abdul Hamid [and 3 others]
- Composite indicators construction by data envelopment analysis: methodological background / Gordana Savic, Milan Martic
- Methodological challenges in building composite indexes: linking theory to practice / Fabio Gaetano Santeramo
- Composite indicators of development: some recent contributions / Sandrina B. Moreira, Nuno Crespo
- Constructing a multidimensional socioeconomic index and the validation of it with early child developmental outcomes / Vijaya Krishnan
- The rule of law index: is it really impartial? A twofold multivariate i-distance approach / Milica Maricic, Milica Bulajic, Milica Vasilijevic
- Comparison of input and output indicators in measuring human capital: an analysis at provincial level for Turkey / Sibel Bali
- The Delphi method application in the analysis of postgraduate students' attitudes on the environmental performance index / Natasa Petrovic [and 4 others]
- How age-friendly are cities?: measuring age-friendliness with a composite index / Lucie Vidovicová
- Using data envelopment analysis to construct human development index / Paulo Nocera Alves Junior, Enzo Barberio Mariano, Daisy Aparecida do Nascimento Rebelatto
- Statistical approach for ranking OECD countries based on composite GICSES index and I-distance method / Maja Mitrovic, Maja Markovic, Stefan Zdravkovic.
(source: Nielsen Book Data)9781522507147 20161213
16. European statistics for European policies : a wealth of data to underpin the Commission's priorities [2017]
- Book
- 29 pages : color illustrations ; 21 cm
SAL3 (off-campus storage)
SAL3 (off-campus storage) | Status |
---|---|
Stacks | Request |
HA1107.5 .E89 2017 | Available |
- Book
- xiii, 125 pages : illustrations ; 23 cm
- * How to Use This Book * Frequently Asked Questions about Reporting Statistics * Descriptive Information * Reliabilities * Correlation * Nonparametric Statistics * Parametric Statistics * Presenting Results Visually * Conclusion Appendices: * Summary chart of Statistics * What to Report * Abbreviations * Suggested Syntax.
- (source: Nielsen Book Data)9781138638082 20161010
(source: Nielsen Book Data)9781138638082 20161010
- * How to Use This Book * Frequently Asked Questions about Reporting Statistics * Descriptive Information * Reliabilities * Correlation * Nonparametric Statistics * Parametric Statistics * Presenting Results Visually * Conclusion Appendices: * Summary chart of Statistics * What to Report * Abbreviations * Suggested Syntax.
- (source: Nielsen Book Data)9781138638082 20161010
(source: Nielsen Book Data)9781138638082 20161010
- Book
- xvii, 535 pages : illustrations ; 26 cm.
- I. Introduction and Organization 1. Overview, Goals of Longitudinal Research, and Historical Developments Overview Five Rationales for Longitudinal Research Historical Development of Growth Models Modeling Frameworks and Programs 2. Practical Preliminaries: Things to Do before Fitting Growth Models Data Structures Longitudinal Plots Data Screening Longitudinal Measurement Time Metrics Change Hypotheses Incomplete Data Moving Forward II. The Linear Growth Model and Its Extensions 3. Linear Growth Models Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 4. Continuous Time Metrics Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 5. Linear Growth Models with Time-Invariant Covariates Multilevel Model Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 6. Multiple-Group Growth Modeling Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 7. Growth Mixture Modeling Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Model Fit, Model Comparison, and Class Enumeration Important Considerations Moving Forward 8. Multivariate Growth Models and Dynamic Predictors Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward III. Nonlinearity in Growth Modeling 9. Introduction to Nonlinearity Organization for Nonlinear Change Models Moving Forward 10. Growth Models with Nonlinearity in Time Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 11. Growth Models with Nonlinearity in Parameters Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 12. Growth Models with Nonlinearity in Random Coefficients Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward IV. Modeling Change with Latent Entities 13. Modeling Change with Ordinal Outcomes Dichotomous Outcomes Polytomous Outcomes Illustration Multilevel Modeling Implementation Structural Equation Modeling Implementation Important Considerations Moving Forward 14. Modeling Change with Latent Variables Measured by Continuous Indicators Common-Factor Model Factorial Invariance over Time Second-Order Growth Model Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 15. Modeling Change with Latent Variables Measured by Ordinal Indicators Item Response Modeling Second-Order Growth Model Illustration Important Considerations Moving Forward V. Latent Change Scores as a Framework for Studying Change 16. Introduction to Latent Change Score Modeling General Model Specification Models of Change Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 17. Multivariate Latent Change Score Models Autoregressive Cross-Lag Model Multivariate Growth Model Multivariate Latent Change Score Model Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 18. Rate-of-Change Estimates in Nonlinear Growth Models Growth Rate Models Latent Change Score Models Illustration Multilevel Modeling Implementation Structural Equation Modeling Implementation Important Considerations Appendix A. A Brief Introduction to Multilevel Modeling Illustrative Example Multilevel Modeling and Longitudinal Data Appendix B. A Brief Introduction to Structural Equation Modeling Illustrative Example Structural Equation Modeling and Longitudinal Data References Author Index Subject Index About the Authors.
- (source: Nielsen Book Data)9781462526062 20161205
(source: Nielsen Book Data)9781462526062 20161205
- I. Introduction and Organization 1. Overview, Goals of Longitudinal Research, and Historical Developments Overview Five Rationales for Longitudinal Research Historical Development of Growth Models Modeling Frameworks and Programs 2. Practical Preliminaries: Things to Do before Fitting Growth Models Data Structures Longitudinal Plots Data Screening Longitudinal Measurement Time Metrics Change Hypotheses Incomplete Data Moving Forward II. The Linear Growth Model and Its Extensions 3. Linear Growth Models Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 4. Continuous Time Metrics Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 5. Linear Growth Models with Time-Invariant Covariates Multilevel Model Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 6. Multiple-Group Growth Modeling Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 7. Growth Mixture Modeling Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Model Fit, Model Comparison, and Class Enumeration Important Considerations Moving Forward 8. Multivariate Growth Models and Dynamic Predictors Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward III. Nonlinearity in Growth Modeling 9. Introduction to Nonlinearity Organization for Nonlinear Change Models Moving Forward 10. Growth Models with Nonlinearity in Time Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 11. Growth Models with Nonlinearity in Parameters Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward 12. Growth Models with Nonlinearity in Random Coefficients Multilevel Modeling Framework Multilevel Modeling Implementation Structural Equation Modeling Framework Structural Equation Modeling Implementation Important Considerations Moving Forward IV. Modeling Change with Latent Entities 13. Modeling Change with Ordinal Outcomes Dichotomous Outcomes Polytomous Outcomes Illustration Multilevel Modeling Implementation Structural Equation Modeling Implementation Important Considerations Moving Forward 14. Modeling Change with Latent Variables Measured by Continuous Indicators Common-Factor Model Factorial Invariance over Time Second-Order Growth Model Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 15. Modeling Change with Latent Variables Measured by Ordinal Indicators Item Response Modeling Second-Order Growth Model Illustration Important Considerations Moving Forward V. Latent Change Scores as a Framework for Studying Change 16. Introduction to Latent Change Score Modeling General Model Specification Models of Change Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 17. Multivariate Latent Change Score Models Autoregressive Cross-Lag Model Multivariate Growth Model Multivariate Latent Change Score Model Illustration Structural Equation Modeling Implementation Important Considerations Moving Forward 18. Rate-of-Change Estimates in Nonlinear Growth Models Growth Rate Models Latent Change Score Models Illustration Multilevel Modeling Implementation Structural Equation Modeling Implementation Important Considerations Appendix A. A Brief Introduction to Multilevel Modeling Illustrative Example Multilevel Modeling and Longitudinal Data Appendix B. A Brief Introduction to Structural Equation Modeling Illustrative Example Structural Equation Modeling and Longitudinal Data References Author Index Subject Index About the Authors.
- (source: Nielsen Book Data)9781462526062 20161205
(source: Nielsen Book Data)9781462526062 20161205
SAL3 (off-campus storage)
SAL3 (off-campus storage) | Status |
---|---|
Stacks | Request |
HA29 .G7734 2017 | Available |
- Book
- viii, 168 pages : illustrations ; 28 cm
- Getting started
- Entering and modifying data
- Descriptive statistics
- Graphing data
- Prediction and association
- Parametric inferential statistics
- Nonparametric inferential statistics
- Test construction.
- Getting started
- Entering and modifying data
- Descriptive statistics
- Graphing data
- Prediction and association
- Parametric inferential statistics
- Nonparametric inferential statistics
- Test construction.
- Book
- xii, 136 pages ; 23 cm
- Executive summary
- Introduction
- Current challenges and opportunities in federal statistics
- Using government administrative and other data for federal statistics
- Using private-sector data for federal statistics
- Protecting privacy and confidentiality while providing access to data for research use
- Advancing the paradigm of combining data sources.
(source: Nielsen Book Data)9780309454285 20170717
- Executive summary
- Introduction
- Current challenges and opportunities in federal statistics
- Using government administrative and other data for federal statistics
- Using private-sector data for federal statistics
- Protecting privacy and confidentiality while providing access to data for research use
- Advancing the paradigm of combining data sources.
(source: Nielsen Book Data)9780309454285 20170717