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 Aanderud, Tricia, author.
 Cary, NC : SAS Institute, [2017]
 Description
 Book — 1 online resource : illustrations
 Summary

 Accessing content
 Building your first report
 Building your first dashboard
 Using the data builder
 Visualizing your data
 The where of data
 Approachable analytics
 Loading data
 LASR administration
 Performance considerations
 Introducing the SAS Viya platform
 Wrangling your data
 Visualizing and exploring your data.
 Aanderud, Tricia, author.
 Cary, NC : SAS Institute, [2017]
 Description
 Book — 1 online resource : illustrations
 Summary

 Accessing content
 Building your first report
 Building your first dashboard
 Using the data builder
 Visualizing your data
 The where of data
 Approachable analytics
 Loading data
 LASR administration
 Performance considerations
 Introducing the SAS Viya platform
 Wrangling your data
 Visualizing and exploring your data.
 Abbott, Martin, 1949
 Hoboken, N.J. : John Wiley & Sons, Inc., c2013.
 Description
 Book — xix, 425 p. : ill. ; 24 cm.
 Summary

 Preface xvii Acknowledgments xix PART I WHEEL OF SCIENCE: PREMISES OF RESEARCH
 1
 1 "DUH" SCIENCE VERSUS "HUH" SCIENCE
 3
 2 THEORIES AND HYPOTHESES
 21
 3 OBSERVATION AND EMPIRICAL GENERALIZATION
 35
 4 ETHICS
 52 PART II WHEEL OF SCIENCE: PROCEDURES OF RESEARCH
 63
 5 MEASUREMENT
 65
 6 USING SPSS IN RESEARCH
 83
 7 CHISQUARE AND CONTINGENCY TABLE ANALYSIS
 90
 8 LEARNING FROM POPULATIONS: CENSUSES AND SAMPLES
 102
 9 CORRELATION
 127
 10 REGRESSION
 146
 11 CAUSATION
 162 PART III WHEEL OF SCIENCE: DESIGNS OF RESEARCH
 203
 12 SURVEY RESEARCH
 205
 13 AGGREGATE RESEARCH
 234
 14 EXPERIMENTS
 251
 15 STATISTICAL METHODS OF DIFFERENCE: T TEST
 270
 16 ANALYSIS OF VARIANCE
 280
 17 FIELD RESEARCH
 301
 18 CONTENT ANALYSIS
 316 PART IV STATISTICS AND DATA MANAGEMENT
 327 STATISTICAL PROCEDURES UNIT A: WRITING THE STATISTICAL RESEARCH SUMMARY
 329 STATISTICAL PROCEDURES UNIT B: THE NATURE OF INFERENTIAL STATISTICS
 333 DATA MANAGEMENT UNIT A: USE AND FUNCTIONS OF SPSS
 343 DATA MANAGEMENT UNIT B: USING SPSS TO RECODE FOR T TEST
 357 DATA MANAGEMENT UNIT C: DESCRIPTIVE STATISTICS
 364 STATISTICAL PROCEDURES UNIT C: Z SCORES
 389 Glossary
 397 Bibliography
 411 Index 416.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Abbott, Martin, 1949
 Hoboken, N.J. : Wiley, c2013.
 Description
 Book — 1 online resource : ill.
 Summary

 Preface xvii Acknowledgments xix PART I WHEEL OF SCIENCE: PREMISES OF RESEARCH
 1
 1 "DUH" SCIENCE VERSUS "HUH" SCIENCE
 3
 2 THEORIES AND HYPOTHESES
 21
 3 OBSERVATION AND EMPIRICAL GENERALIZATION
 35
 4 ETHICS
 52 PART II WHEEL OF SCIENCE: PROCEDURES OF RESEARCH
 63
 5 MEASUREMENT
 65
 6 USING SPSS IN RESEARCH
 83
 7 CHISQUARE AND CONTINGENCY TABLE ANALYSIS
 90
 8 LEARNING FROM POPULATIONS: CENSUSES AND SAMPLES
 102
 9 CORRELATION
 127
 10 REGRESSION
 146
 11 CAUSATION
 162 PART III WHEEL OF SCIENCE: DESIGNS OF RESEARCH
 203
 12 SURVEY RESEARCH
 205
 13 AGGREGATE RESEARCH
 234
 14 EXPERIMENTS
 251
 15 STATISTICAL METHODS OF DIFFERENCE: T TEST
 270
 16 ANALYSIS OF VARIANCE
 280
 17 FIELD RESEARCH
 301
 18 CONTENT ANALYSIS
 316 PART IV STATISTICS AND DATA MANAGEMENT
 327 STATISTICAL PROCEDURES UNIT A: WRITING THE STATISTICAL RESEARCH SUMMARY
 329 STATISTICAL PROCEDURES UNIT B: THE NATURE OF INFERENTIAL STATISTICS
 333 DATA MANAGEMENT UNIT A: USE AND FUNCTIONS OF SPSS
 343 DATA MANAGEMENT UNIT B: USING SPSS TO RECODE FOR T TEST
 357 DATA MANAGEMENT UNIT C: DESCRIPTIVE STATISTICS
 364 STATISTICAL PROCEDURES UNIT C: Z SCORES
 389 Glossary
 397 Bibliography
 411 Index 416.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online

 dx.doi.org Wiley Online Library
 Google Books (Full view)
 Abbott, Martin, 1949
 Hoboken, New Jersey : John Wiley & Sons, Inc., [2017]
 Description
 Book — 1 online resource.
 Summary

 Descriptive Statistics: Central Tendency
 Descriptive Statistics: Variability
 The Normal Distribution
 Probability and the Z Distribution
 Research Design and Inferential Statistics
 The T Test for Single Samples
 Independent Sample T Test
 Analysis of Variance
 Factorial Anova
 Correlation
 Bivariate Regression
 Introduction to Multiple Linear Regression
 Chisquare and Contingency Table Analysis
 Repeated Measures Procedures: Tdep and ANOVAWS
 SPSS Basics
 Excel Basics
 Statistical Tables.
(source: Nielsen Book Data)
 Abeje Fekadu, Gedefaw, author.
 Rockville, Maryland, USA : ICF, 2018.
 Description
 Book — 1 online resource (ix, 28 pages).
 Abel, James F. (James Frederick), 1878
 Washington, D.C. : United States Department of the Interior, Office of Education, 1930.
 Description
 Book — 14 pages : illustrations ; 23 cm.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
I 16.43:12  Unknown 
 Aberson, Christopher L.
 New York : Routledge, c2010.
 Description
 Book — xiv, 257 p. : ill. ; 23 cm.
 Summary

 1. What is Power? Why is Power Important?
 2. ChiSquare and Tests for Proportions.
 3. Independent Samples and Paired ttests.
 4. Correlations and Differences between Correlations.
 5. Between Subjects ANOVA (One Factor, Two or more Factors).
 6. Within Subjects Designs.
 7. Mixed Model ANOVA and Multivariate ANOVA.
 8. Multiple Regression.
 9. Covariate Analyses and Regression Interactions.
 10. Precision Analysis for Confidence Intervals.
 11. Additional Issues and Resources.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
 Aberson, Christopher L., author.
 2nd edition.  New York, NY : Routledge, 2019.
 Description
 Book — 1 online resource.
 Summary

 1. What is Power? Why is Power Important?
 2. ChiSquare and Tests for Proportions
 3. Independent Samples and Paired ttests
 4. Correlations and Differences Between Correlations
 5. Between Subjects ANOVA (One and Two Factors)
 6. WithinSubjects Designs with ANOVA and Linear Mixed Models
 7. Mixed Model ANOVA and Multivariate ANOVA
 8. Multiple Regression
 9. Analysis of Covariance, Moderated Regression, Logistic Regression, and Mediation
 10. Precision Analysis for Confidence Intervals
 11. Additional Issues and Resources.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Abraham, Bovas.
 Boston, MA : Birkhäuser Boston, 1998.
 Description
 Book — 1 online resource (388 pages 30 illustrations).
11. Making sense of data : a selfinstruction manual on the interpretation of epidemiological data [2001]
 Abramson, J. H. (Joseph Herbert), 1924
 3rd ed.  Oxford ; New York : Oxford University Press, 2001.
 Description
 Book — xvi, 367 pages : illustrations ; 24 cm
 Online
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
RA652.4 .A27 2001  Unknown 
 Abramson, J. H. (Joseph Herbert), 1924
 2nd ed.  New York : Oxford University Press, 1994.
 Description
 Book — xii, 404 pages : illustrations ; 25 cm
 Online
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
RA652.4 .A27 1994  Unknown 
 Abramson, J. H. (Joseph Herbert), 1924
 New York : Oxford University Press, 1988.
 Description
 Book — xii, 326 pages : illustrations ; 24 cm
 Online
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
RA652.4 .A27 1988  Unknown 
14. Applied mining geology [2016]
 Abzalov, Marat, author.
 Switzerland : Springer, 2016.
 Description
 Book — 1 online resource.
 Summary

 1. Introduction. PART I. MINE MAPPING AND SAMPLING.
 2. Mining Methods.
 3. Mine Mapping.
 4. Drilling Techniques and Drill Holes Logging.
 5. Sampling of the Mine Workings.
 6. Geotechnical Study.
 7. Dry Bulk Density (DBD) of Rocks.
 8. Data Points Location (surveying). PART II. SAMPLING ERRORS.
 9. Introduction to the Theory of Sampling.
 10. Quality Control and Assurance (QAQC).
 11. Twin Holes.
 12. Database. PART III. MINERAL RESOURCES.
 13. Data Preparation.
 14. Geological Constrains of Mineralisation.
 15. Exploratory Data Analysis.
 16. Resource Estimation Methods. PART IV. APPLIED MINING GEOSTATISTICS.
 17. Introduction to Geostatistics.
 18. Variography.
 19. Methods of the Linear Geostatistics (Kriging).
 20. Multivariate Geostatistics.
 21. Multiple Indicator Kriging.
 22. Estimation of the Recoverable Resources.
 23. Model Review and Validation.
 24. Reconciliation with New Data. PART V. ESTIMATING UNCERTAINTY.
 25. Grade Uncertainty.
 26. Quantitative Geological Models. PART VI. CLASSIFICATION.
 27. Principles of Classification.
 28. Methodology of the Mineral Resource Classification.
 29. Conversion Resources to Reserves.
 30. Balance Between Quantity and Quality of Samples. PART VII. MINERAL DEPOSIT TYPES.
 31. Lode Gold Deposits.
 32. Uranium Deposits (InSitu Leach Projects).
 33. IronOxide Deposits.
 34. Bauxite Deposits.
 35. Mineral Sands.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Acevedo, Miguel F.
 Boca Raton : CRC Press, c2013.
 Description
 Book — xxi, 535 p. : ill. ; 26 cm.
 Summary

 PART I Introduction to Probability, Statistics, Time Series, and Spatial Analysis Introduction Brief History of Statistical and Probabilistic Analysis Computers Applications Types of Variables Probability Theory and Random Variables Methodology Descriptive Statistics Inferential Statistics Predictors, Models, and Regression Time Series Spatial Data Analysis Matrices and Multiple Dimensions Other Approaches: ProcessBased Models Baby Steps: Calculations and Graphs Exercises Computer Session: Introduction to R Supplementary Reading Probability Theory Events and Probabilities Algebra of Events Combinations Probability Trees Conditional Probability Testing Water Quality: False Negative and False Positive Bayes' Theorem Generalization of Bayes' Rule to Many Events BioSensing Decision Making Exercises Computer Session: Introduction to Rcmdr, Programming, and Multiple Plots Supplementary Reading Random Variables, Distributions, Moments, and Statistics Random Variables Distributions Moments Some Important RV and Distributions Application Examples: Species Diversity Central Limit Theorem Random Number Generation Exercises Computer Session: Probability and Descriptive Statistics Example Binomial Supplementary Reading Exploratory Analysis and Introduction to Inferential Statistics Exploratory Data Analysis (EDA) Relationships: Covariance and Correlation Statistical Inference Statistical Methods Parametric Methods Nonparametric Methods Exercises Computer Session: Exploratory Analysis and Inferential Statistics Supplementary Reading More on Inferential Statistics: Goodness of Fit, Contingency Analysis, and Analysis of Variance Goodness of Fit (GOF) Counts and Proportions Contingency Tables and CrossTabulation Analysis of Variance Exercises Computer Session: More on Inferential Statistics Supplementary Reading Regression Simple Linear Least Squares Regression ANOVA as Predictive Tool Nonlinear Regression Computer Session: Simple Regression Supplementary Reading Stochastic or Random Processes and Time Series Stochastic Processes and Time Series: Basics Gaussian Autocovariance and Autocorrelation Periodic Series, Filtering, and Spectral Analysis Poisson Process Marked Poisson Process Simulation Exercises Computer Session: Random Processes and Time Series Supplementary Reading Spatial Point Patterns Types of Spatially Explicit Data Types of Spatial Point Patterns Spatial Distribution Testing Spatial Patterns: Cell Count Methods NearestNeighbor Analysis Marked Point Patterns Geostatistics: Regionalized Variables Variograms: Covariance and Semivariance Directions Variogram Models Exercises Computer Session: Spatial Analysis Supplementary Reading PART II Matrices, Tempral and Spatial Autoregressive Processes, and Multivariate Analysis Matrices and Linear Algebra Matrices Dimension of a Matrix Vectors Square Matrices Matrix Operations Solving Systems of Linear Equations Linear Algebra Solution of the Regression Problem Alternative Matrix Approach to Linear Regression Exercises Computer Session: Matrices and Linear Algebra Supplementary Reading Multivariate Models Multiple Linear Regression Multivariate Regression TwoGroup Discriminant Analysis Multiple Analysis of Variance (MANOVA) Exercises Computer Session: Multivariate Models Supplementary Reading Dependent Stochastic Processes and Time Series Markov SemiMarkov Processes Autoregressive (AR) Process ARMA and ARIMA Models Exercises Computer Session: Markov Processes and Autoregressive Time Series Supplementary Reading Geostatistics: Kriging Kriging Ordinary Kriging Universal Kriging Data Transformations Exercises Computer Session: Geostatistics, Kriging Supplementary Reading Spatial AutoCorrelation and AutoRegression Lattice Data: Spatial AutoCorrelation and AutoRegression Spatial Structure and Variance Inflation Neighborhood Structure Spatial AutoCorrelation Spatial AutoRegression Exercises Computer Session: Spatial Correlation and Regression Supplementary Reading Multivariate Analysis I: Reducing Dimensionality Multivariate Analysis: EigenDecomposition Vectors and Linear Transformation Eigenvalues and Eigenvectors EigenDecomposition of a Covariance Matrix Principal Components Analysis (PCA) Singular Value Decomposition and Biplots Factor Analysis Correspondence Analysis Exercises Computer Session: Multivariate Analysis, PCA Supplementary Reading Multivariate Analysis II: Identifying and Developing Relationships among Observations and Variables Introduction Multigroup Discriminant Analysis (MDA) Canonical Correlation Constrained (or Canonical) Correspondence Analysis (CCA) Cluster Analysis Multidimensional Scaling (MDS) Exercises Computer Session: Multivariate Analysis II Supplementary Reading Bibliography Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Earth Sciences Library (Branner)
Earth Sciences Library (Branner)  Status 

Stacks  
G70.2 .A26 2013  Unknown 
 Achen, Christopher H.
 Berkeley : University of California Press, c1986.
 Description
 Book — xiv, 172 p. : ill. ; 24 cm.
 Online
17. A gentle introduction to Stata [2006]
 Acock, Alan C., 1944
 College Station, Tex. : StataCorp LP, c2006.
 Description
 Book — xx, 289 p. : ill. ; 24 cm.
 Summary

 PrefaceSupport Materials for the BookGETTING STARTEDIntroductionThe Stata screenUsing an existing datasetAn example of a short Stata sessionConventionsChapter summaryExercisesENTERING DATACreating a datasetAn example questionnaireDevelop a coding systemEntering dataSaving your datasetChecking the dataChapter summaryExercisesPREPARING DATA FOR ANALYSISIntroductionPlan your workCreate value labelsReversecode variablesCreate and modify variablesCreate scalesSave some of your dataSummaryExercisesWORKING WITH COMMANDS, DOFILES, AND RESULTSIntroductionHow Stata commands are constructedGetting the command from the menu systemSaving your resultsLogging your command fileSummaryExercisesDESCRIPTIVE STATISTICS AND GRAPHS FOR A SINGLE VARIABLEDescriptive statistics and graphsWhere is the center of a distribution?How dispersed is the distribution?Statistics and graphsunordered categoriesStatistics and graphsordered categories and variablesStatistics and graphsquantitative variablesSummaryExercisesSTATISTICS AND GRAPHS FOR TWO CATEGORICAL VARIABLESRelationship between categorical variablesCrosstabulationChisquaredPercentages and measures of associationOrdered categorical variablesInteractive tablesTableslinking categorical and quantitative variablesSummaryExercisesTESTS FOR ONE OR TWO MEANSTests for one or two meansRandomizationHypothesesOnesample test of a proportionTwosample test of a proportionOnesample test of meansTwosample test of group meansRepeatedmeasures t testPower analysisNonparametric alternativesSummaryExercisesBIVARIATE CORRELATION AND REGRESSIONIntroduction to bivariate correlation and regressionScattergramsPlotting the regression lineCorrelationRegressionSpearman's rho: rankorder correlation for ordinal dataAlpha reliabilityKappa as a measure of agreement for categorical dataSummaryExercisesANALYSIS OF VARIANCE (ANOVA)The logic of oneway analysis of varianceANOVA exampleANOVA example using survey dataA nonparametric alternative to ANOVAAnalysis of covarianceTwoway ANOVARepeatedmeasures designIntraclass correlationmeasuring agreementSummaryExercisesMULTIPLE REGRESSIONIntroductionWhat is multiple regression?The basic multiple regression commandIncrement in Rsquared: semipartial correlationsIs the dependent variable normally distributed?Are the residuals normally distributed?Regression diagnostic statisticsWeighted dataCategorical predictors and hierarchical regressionFundamentals of interactionSummaryExercisesLOGISTIC REGRESSIONIntroductionAn exampleWhat are an odds ratio and a logit?Data used in rest of chapterLogistic regressionHypothesis testingNested logistic regressionsSummaryExercisesWHAT'S.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
18. A gentle introduction to Stata [2008]
 Acock, Alan C., 1944
 2nd ed.  College Station, Tex. : StataCorp LP, 2008.
 Description
 Book — xx, 333 p. : ill. ; 24 cm.
 Online
Green Library, Science Library (Li and Ma)
Green Library  Status 

Find it Velma Denning Room (Social Science Data and Software)  
QA276.4 .A36 2008  Inlibrary use 
Science Library (Li and Ma)  Status 

Stacks  
QA276.4 .A36 2008  Unknown 
19. A gentle introduction to Stata [2016]
 Acock, Alan C., 1944 author.
 Fifth edition.  College Station, Texas : A Stata Press Publication, StataCorp LP, 2016.
 Description
 Book — xxxvi, 546 pages : illustrations ; 24 cm
 Summary

 Getting started Conventions Introduction The Stata screen Using an existing dataset An example of a short Stata session Video aids to learning Stata Summary Exercises
 Entering data Creating a dataset An example questionnaire Developing a coding system Entering data using the Data Editor Value labels The Variables Manager The Data Editor (Browse) view Saving your dataset Checking the data Summary Exercises
 Preparing data for analysis Introduction Planning your work Creating value labels Reversecode variables Creating and modifying variables Creating scales Save some of your data Summary Exercises
 Working with commands, dofiles, and results Introduction How Stata commands are constructed Creating a dofile Copying your results to a word processor Logging your command file Summary Exercises
 Descriptive statistics and graphs for one variable Descriptive statistics and graphs Where is the center of a distribution? How dispersed is the distribution? Statistics and graphsunordered categories Statistics and graphsordered categories and variables Statistics and graphsquantitative variables Summary Exercises
 Statistics and graphs for two categorical variables Relationship between categorical variables Crosstabulation Chisquared test Degrees of freedom Probability tables Percentages and measures of association Odds ratios when dependent variable has two categories Ordered categorical variables Interactive tables Tableslinking categorical and quantitative variables Power analysis when using a chisquared test of significance Summary Exercises
 Tests for one or two means Introduction to tests for one or two means Randomization Random sampling Hypotheses Onesample test of a proportion Twosample test of a proportion Onesample test of means Twosample test of group means Testing for unequal variances Repeatedmeasures t test Power analysis Nonparametric alternatives MannWhitney twosample ranksum test Nonparametric alternative: Median test Video tutorial related to this chapter Summary Exercises
 Bivariate correlation and regression Introduction to bivariate correlation and regression Scattergrams Plotting the regression line An alternative to producing a scattergram, binscatter Correlation Regression Spearman's rho: Rankorder correlation for ordinal data Power analysis with correlation Summary Exercises
 Analysis of variance The logic of oneway analysis of variance ANOVA example ANOVA example with nonexperimental data Power analysis for oneway ANOVA A nonparametric alternative to ANOVA Analysis of covariance Twoway ANOVA Repeatedmeasures design Intraclass correlation<measuring agreement Power analysis with ANOVA Power analysis for oneway ANOVA Power analysis for twoway ANOVA Power analysis for repeatedmeasures ANOVA Summary of power analysis for ANOVA Summary Exercises
 Multiple regression Introduction to multiple regression What is multiple regression? The basic multiple regression command Increment in Rsquared: Semipartial correlations Is the dependent variable normally distributed? Are the residuals normally distributed? Regression diagnostic statistics Outliers and influential cases Influential observations: DFbeta Combinations of variables may cause problems Weighted data Categorical predictors and hierarchical regression A shortcut for working with a categorical variable Fundamentals of interaction Nonlinear relations Fitting a quadratic model Centering when using a quadratic term Do we need to add a quadratic component? Power analysis in multiple regression Summary Exercises
 Logistic regression Introduction to logistic regression An example What is an odds ratio and a logit? The odds ratio The logit transformation Data used in the rest of the chapter Logistic regression Hypothesis testing Testing individual coefficients Testing sets of coefficients More on interpreting results from logistic regression Nested logistic regressions Power analysis when doing logistic regression Next steps for using logistic regression and its extensions Summary Exercises
 Measurement, reliability, and validity Overview of reliability and validity Constructing a scale Generating a mean score for each person Reliability Stability and testretest reliability Equivalence Splithalf and alpha reliabilitinternal consistency KuderRichardson reliability for dichotomous items Rater agreementkappa (K) Validity Expert judgment Criterionrelated validity Construct validity Factor analysis PCF analysis Orthogonal rotation: Varimax Oblique rotation: Promax But we wanted one scale, not four scales Scoring our variable Summary Exercises
 Working with missing valuesmultiple imputation The nature of the problem Multiple imputation and its assumptions about the mechanism for missingness What variables do we include when doing imputations? Multiple imputation A detailed example Preliminary analysis Setup and multipleimputation stage The analysis stage For those who want an R and standardized sss When impossible values are imputed Summary Exercises
 The sem and gsem commands Linear regression using sem Using the SEM Builder to fit a basic regression model A quick way to draw a regression model and a fresh start Using sem without the SEM Builder The gsem command for logistic regression Fitting the model using the logit command Fitting the model using the gsem command Path analysis and mediation Conclusions and what is next for the sem command Exercises
 An introduction to multilevel analysis Questions and data for groups of individuals Questions and data for a longitudinal multilevel application Fixedeffects regression models Randomeffects regression models An applied example Research questions Reshaping data to do multilevel analysis A quick visualization of our data Randomintercept model Random interceptlinear model Randomintercept modelquadratic term Treating time as a categorical variable Randomcoefficients model Including a timeinvariant covariate Summary Exercises
 Item response theory (IRT) How are IRT measures of variables different from summated scales? Overview of three IRT models for dichotomous items The oneparameter logistic (PL) model The twoparameter logistic (PL) model The threeparameter logistic (PL) model Fitting the PL model using Stata The estimation How important is each of the items? An overall evaluation of our scale Estimating the latent score Fitting a PL IRT model Fitting the PL model The graded response modelIRT for Likerttype items The data Fitting our graded response model Estimating a person's score Reliability of the fitted IRT model Using the Stata menu system Extensions of IRT Exercises
 What's next? Introduction to the appendix Resources Web resources Books about Stata Short courses Acquiring data Learning from the postestimation methods Summary.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
20. A Gentle introduction to Stata [2006]
 Acock, Alan C., 1944
 College Station, TX : StataCorp LP, c2006.
 Description
 Book — [xxi], 289 pages : illustrations ; 24 cm
 Online
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
QA276.4 A36 2006  Unknown 
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