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?

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 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.

Statistical Analysis with R

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R is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a key feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R has attained wide popularity.


  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

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 21) 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 19) 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.