WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Research on the relation between attitude and behavior is examined in light of the correspondence between attitudinal and behavioral entities. Such entities are defined by their target, action, context, and time elements. A review of available empirical research supports the contention that strong attitude-behavior relations dre obtained only under high correspondence between at least the target and action elements of the attitudinal and behavioral entities. This conclusion is compared with the rather pessimistic assessment of the utility of the attitude concept found in much contemporary social psychological literature.

/pdf/attitude-behavior-relations-a-theoretical-analysis-and-2wr3erzb6j.pdf

Attitude-behavior relations: A theoretical analysis and review of empirical research.

Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

Appendix I: List of Statistical Fact Sheets. URL: http://www.americanheart.org/presenter.jhtml?identifier=2007

We wish to thank Drs Brian Eigel and Michael Wolz for their valuable comments and contributions. We would like to acknowledge Tim Anderson and Tom Schneider for their editorial contributions and Karen Modesitt for her administrative assistance.

Disclosures 



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# Summary {#article-title-2}

Each year, the American Heart Association, in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on disease …

Heart Disease and Stroke Statistics—2010 Update A Report From the American Heart Association

The establishment of measurement invariance across groups is a logical prerequisite to conducting substantive cross-group comparisons (e.g., tests of group mean differences, invariance of structura...

A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research

Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustrated for a linear model, and a series of recommendations is provided. When missing values cannot be avoided, multiple imputation and full information methods offer substantial improvements over traditional approaches. Selected results using SPSS, NORM, Stata (mvis/micombine), and Mplus are included as is a table of available software and an appendix with examples of programs for Stata and Mplus.

Working With Missing Values

The impact of kindergarten learning-related skills on academic trajectories at the end of elementary school

Getting started Conventions Introduction The Stata screen Using an existing dataset An example of a short Stata session Summary Exercises Entering data Creating a dataset An example questionnaire Develop 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 Reverse-code variables Creating and modifying variables Creating scales Save some of your data Summary Exercises Working with commands, do-files, and results Introduction How Stata commands are constructed Creating a do-file 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 graphs--unordered categories Statistics and graphs--ordered categories and variables Statistics and graphs--quantitative variables Summary Exercises Statistics and graphs for two categorical variables Relationship between categorical variables Cross-tabulation Chi-squared test Degrees of freedom Probability tables Percentages and measures of association Odds ratios when dependent variable has two categories Ordered categorical variables Interactive tables Tables--linking categorical and quantitative variables Power analysis when using a chi-squared test of significance Summary Exercises Tests for one or two means Introduction to tests for one or two means Randomization Random sampling Hypotheses One-sample test of a proportion Two-sample test of a proportion One-sample test of means Two-sample test of group means Testing for unequal variances Repeated-measures t test Power analysis Nonparametric alternatives Mann--Whitney two-sample rank-sum test Nonparametric alternative: Median test Summary Exercises Bivariate correlation and regression Introduction to bivariate correlation and regression Scattergrams Plotting the regression line Correlation Regression Spearman's rho: Rank-order correlation for ordinal data Summary Exercises Analysis of variance The logic of one-way analysis of variance ANOVA example ANOVA example using survey data A nonparametric alternative to ANOVA Analysis of covariance Two-way ANOVA Repeated-measures design Intraclass correlation--measuring agreement Summary Exercises Multiple regression Introduction to multiple regression What is multiple regression? The basic multiple regression command Increment in R-squared: 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 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 rest of chapter Logistic regression Hypothesis testing Testing individual coefficients Testing sets of coefficients Nested logistic regressions Power analysis when doing logistic regression Summary Exercises Measurement, reliability, and validity Overview of reliability and validity Constructing a scale Generating a mean score for each person Reliability Stability and test--retest reliability Equivalence Split-half and alpha reliability--internal consistency Kuder--Richardson reliability for dichotomous items Rater agreement--kappa (K) Validity Expert judgment Criterion-related 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 values--multiple 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 multiple-imputation stage The analysis stage For those who want an R2 and standardized I s When impossible values are imputed Summary Exercises A What's next? Introduction to the appendix Resources Web resources Books about Stata Short courses Acquiring data Summary References Author index (pdf) Subject index(pdf)

A Gentle Introduction to Stata

Introduction to confirmatory factor analysis Introduction The "do not even think about it" approach The principal component factor analysis approach Alpha reliability for our nine-item scale Generating a factor score rather than a mean or summative score What can CFA add? Fitting a CFA model Interpreting and presenting CFA results Assessing goodness of fit A two-factor model Parceling Extensions and what is next Exercises Using the SEM Builder to run a CFA Using structural equation modeling for path models Introduction Path model terminology A substantive example of a path model Estimating a model with correlated residuals Auxiliary variables Testing equality of coefficients A cross-lagged panel design Moderation Nonrecursive models Exercises Using the SEM Builder to run path models Structural equation modeling Introduction The classic example of a structural equation model Equality constraints Programming constraints Structural model with formative indicators Exercises Latent growth curves Discovering growth curves A simple growth curve model Identifying a growth curve model An example of a linear latent growth curve How can we add time-invariant covariates to our model? Explaining the random effects-time-varying covariates Constraining variances of error terms to be equal (optional) Exercises Group comparisons Interaction as a traditional approach to multiple-group comparisons The range of applications of Stata's multiple-group comparisons with sem A measurement model application Multiple-group path analysis Multiple-group comparisons of structural equation models Exercises Epilogue-what now? What is next? The graphical user interface Introduction Menus for Windows, Unix, and Mac Designing a structural equation model Drawing an SEM model Fitting a structural equation model Postestimation commands Clearing preferences and restoring the defaults B Entering data from summary statistics

Alan C. Acock

Papers

Working With Missing Values

The impact of kindergarten learning-related skills on academic trajectories at the end of elementary school

A Gentle Introduction to Stata

Discovering Structural Equation Modeling Using Stata

Relations between Preschool Attention Span-Persistence and Age 25 Educational Outcomes