Statistics: A Review

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A one-day, intensive review of common statistical methods of design, measurement analysis and presentation of scientific investigations.  The workshop is designed for any scholar engaged in quantitative research.

Statistics: A Review discusses answers to the following questions:

  • What should we measure?
  • What are the main design types; what are the comparative advantages of each?
  • How are the sample sizes determined?
  • What are the appropriate inference procedures?
  • What do standard error, p-value and confidence level mean?
  • What are some dangers we need to avoid?
  • How should we display our results?
  • What are the statistical software options?

Applied Structural Equation Modeling

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This workshop is designed to help participants develop skills in defining, estimating and testing structural equation models. Applied Structural Equation Modeling will focus on covariance structure models with latent variables. Two submodels, confirmatory factor analysis and path analysis, will also be covered.  Lectures covering structural equation modeling in general will be interspersed with hands-on computer work. The workshop is intended as an introduction to structural equation modeling. The software used is Stata–no prior use of Stata is required

Introduction to Survey Design: Data Collection, Questionnaire Design and Response Processes

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This workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design.  Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics will include:

  • Survey errors, in particular measurement, coverage, and nonresponse error.
  • What to consider when selecting a data collection method for a particular research question.
  • Measurement (response) error and how to reduce it through question wording/format and questionnaire structure.

The role of the interviewer and interviewer effects.

Introduction to Proc Mixed (Longitudinal and Clustered Data)

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Do you have two or more observations on the same person (longitudinal or repeated measures data)?

Do you have observations on students from several different classes, or rats from different litters (clustered or hierarchical data)?

Normal linear regression doesn’t work in these situations.

Fortunately, SAS Proc Mixed is a flexible tool that can handle all of these problems, as long as the response variable is approximately continuous. It works even when some data are missing.

The workshop will consist of morning lectures and afternoon hands-on computer sessions.

Introduction to SPSS

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Note: Topic order is subject to change.  Participants must sign up for the entire series.


This portion introduces SPSS for Windows, the menu and the help systems, the three main types of files used, and printing from within SPSS.  It then addresses defining variables, attaching labels, defining missing values, and various ways to enter data into SPSS.  Finally, it covers a brief introduction to obtaining frequency distributions, descriptive statistics, and cross tabulations of variables.

Within-Case Transformations

This portion introduces data management capabilities, including recoding variables (manual and automatic), computing new variables using formulas, and counting occurrences of values within subjects.  Attention then turns to temporary transformations, conditional processing of transformations, and repetitive transformations.  SPSS syntax is also introduced.

Data Management with Multiple Files

This portion begins with a discussion of subsetting data files by drawing samples, selecting groups and excluding groups from analysis.  Then, the two main methods of merging SPSS data files are covered: adding additional variables and adding additional cases.  Next, creating aggregated data sets and applying aggregated data to individuals is covered.  Lastly, importing and exporting data between SPSS and other statistical programs (Excel, dBase, SAS) is demonstrated.

Basic Statistics and Graphics

This portion covers basic exploratory procedures, including obtaining percentiles, frequencies, descriptive statistics, and cross tabulations. Basic comparative procedures including two-sample t-tests, paired t-tests, and one-way analysis of variance are also covered.  Then, simple bivariate correlation analysis is introduced.  Participants are given a basic introduction to commonly used graphical procedures for displaying data, including scatter plots, bar graphs, histograms, and boxplots.

Applied Survival Analysis (Event History Analysis, Reliability Analysis)

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This workshop, held over two days, covers basic concepts of and common analytical approaches for time-to-event data, known variously as survival analysis (in biological and medical sciences), event history analysis (in social sciences), or reliability analysis (in engineering).

The workshop will be held in a computer lab and the methods will be illustrated with hands-on exercises.

Exercises and examples will use SAS, R, SPSS, and/or Stata as necessary. This workshop covers:

  • Basic concepts associated with the analysis of censored data (survival function, hazard function)
  • Methods for estimating the survival function (Kaplan-Meier, Nelson-Aalen, and life-table analysis)
  • Two-sample tests with censored data (log-rank and Wilcoxon tests)
  • Regression analysis with censored data (Cox proportional hazards, Weibull, Aalen additive hazards), including time varying covariates, correlated data, and stratified Cox models
  • Discrete models for censored data (logistic regression, Poisson regression)
  • Basics of power and sample size estimation for time-to-event studies.

Modeling Multivariate Data with Principal Component Analysis, Multidimensional Scaling, Factor Analysis and Clustering Algorithms

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This and the companion workshop (Logistic Regression and Related Techniques on May 29) are designed to teach participants a) the logical basis for a set of techniques and b) how to carry out analyses using statistical software SPSS. Participants may register for one or both days.

Multidimensional Data introduces factor analysis, multidimensional scaling, cluster analysis and principal components analysis.  The workshop includes hands-on computer work for each technique.

Logistic Regression and Related Techniques

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This workshop and the companion Modeling Multivariate Data with Principal Component Analysis, Multidimensional Scaling, Factor Analysis and Clustering Algorithms (May 20, 2014) are designed to help participants a) learn about the logical and statistical basis for a set of techniques and b) learn how to carry out these analysis using IBM/SPSS ® statistical software. This workshop introduces binary, ordinal, and multinomial logistic regression analysis and similar techniques.