Statistical Consulting Services
Statistical Consulting Services (SCS) is provided by the UMass Boston Office of Graduate Studies. It serves as a statistical consulting resource for faculty members, graduate students, and research staff. It provides statistical advice to faculty and graduate student researchers throughout the university and generates collaborative research with them. It also serves as a training facility to graduate students in applied statistics, providing a center for discussion on research problems and methodological advances in statistics and quantitative research.
The SCS provides advice on statistical problems arising in the preparation of studies, the analysis of data, and the interpretation of results. It also provides assistance in the preparation of data analysis sections for inclusion in grant proposals and manuscript revisions. In addition, the SCS provides help with statistical issues arising at any stage of the research process.
Services provided by Statistical Consulting Services
Workshop schedule | Statistical software
Spring 2013 Workshop schedule
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Workshops for Using SPSS, SAS & Stata in Research |
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Thursday, February 12: 10:00am - 12:00pm Wednesday, March 27: 10:00am - 12:00pm |
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Thursday, February 12: 10:00am - 12:00pm |
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Thursday, February 26: 10:00am - 12:00am |
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Thursday, March 5: 10:00am - 12:00am |
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Thursday, March 12: 2:00pm - 4:00am |
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Thursday, March 26: 10:00am - 12:00am |
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Advanced Workshops for Using SPSS, SAS, R and WinBUGS |
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Wednesday, March 6: 10:00am - 12:00pm |
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Monday, April 1: 10:00am - 12:00pm |
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Thursday, April 18: 10:00am - 12:00am |
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Thursday, April 18: 2:00pm - 4:00am |
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Tuesday, April 23: 2:00pm - 4:00am |
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Tuesday, April 30: 2:00pm - 4:00am |
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(All the workshops will be hold at Presentation Room 1, Lower Level of Healey Library)
SPSS I
This is a hands-on workshop to enable students to perform useful analysis of data using SPSS for Windows. The topics covered include: entering and reading data, documenting the file with variable and value labels, examining frequency and crosstab tables for individual and group data, recoding variables, independent sample t-test, and simple linear regression. Participants will be provided extensive notes on the topics covered in the workshop, work with sample datasets, and will produce examples of the graphic output available from the program.
SPSS 2
This workshop covers more data management and statistical procedures. Data management includes selections of cases, combining cases from two files, and linking together files with different information. Statistical procedures include Chi-square test, One-way ANOVA, repeated measurement analysis, non-parametric statistics, multiple regression, and logistic regression.
SAS II
This workshop is an introduction to the SAS system that concentrates on the SAS DATA STEP with emphasis on data input, manipulation, output, and summary. The topics covered include: creating SAS working data sets and data files, importing data from SPSS and Excel files, formatting variable and value labels, and simple statistical procedures, such as PROC FREQ, PROC MEANS.
SAS II
This workshop covers the analysis of designed experiments with PROC ANOVA and PROC GLM and Analysis of data utilizing linear and non-linear regression techniques in SAS with PROC REG, and PROC GENMOD. Topics include ANOVA, linear and multiple regression, regression diagnostics, and logistic regression.
Stata I
This workshop is an introduction to the Stata that covers both graphic user interface and intuitive command syntax approaches. It aims to learn basic Stata operation in a fast and accurate way. Topics include Browsing the Data, Data Management, Descriptive Statistics, Independent Sample t-test and Simple Regression Models.
Stata II
This workshop covers more about data management such as data transforming, recoding variables, and computing new variables. It also covers the use of log, *.do files and more of statistical procedures such as Chi-Square Test, One-Way ANOVA, linear and multiple regression and regression diagnostics.
Introduction to R for Statistical Analysis
This workshop introduces interactive statistics software, R, which is specialized for probability statistics. This workshop introduces students to perform useful operation and data analysis using R. The topics include: download and install R, entering and reading data, basic operations, independent sample t-test, Chi-Square test, simple and multiple linear regression, One-Way ANOVA and using R for graphs.
Introduction to HLM (Mixed Models) using SPSS
This workshop introduces the basic principles of multilevel/hierarchical linear models. It is designed to help attendees understand the following: the need for the appropriate models of dependencies (e.g., clustering of students within schools, etc.), how to formulate and interpret two-level multilevel models and the relevant parameters, and how to use SPSS software to estimate the models' parameters.
Sample Size Estimation and Power Calculation using SAS
This workshop covers sample size determinations and power estimation for various statistical comparison and tests using PROC POWER procedure in SAS.
Missing Data Analysis using SAS
This workshop covers missing data mechanisms, analysis of non-randomly selection bias, and practice methods of single and multiple imputations using SAS software. Missing data are very common in all types of data sets, but most statistical software packages automatically eliminate entire cases from the analysis. This approach can lead to very low sample size and biased results.
Introduction to Survival Data Analysis (Even History Analysis)
This workshop using SPSS introduces statistical methods of survival analysis, that is, the analysis of studies where the outcome is a time-to-event variable. It covers the estimation of survival time using life table and Kaplan-Meier Methods, and modeling survival risk and assessing the relationship of risk factors and survival times using the Cox regression model. A statistical package IMB SPSS Statistics 19.0 will be used for data analyses.
Introduction to Practical Bayesian Modeling
This worshop using WinBUGS -Bayesian approach uses both prior and sample information. This workshop introduces the basic ideas of Bayesian Statistics with the use of Bayes’s theorem. Topics cover prior and posterior distributions, Bayesian analysis for a binomial proportion and a normal mean and a simple linear regression model.
Statistical software
IBM SPSS Statistics
SPSS is a Statistical Package for the Social Sciences that is among the most widely used programs for statistical analysis, data management, and data documentation. SPSS can read and write data from ASCII text files, spreadsheets, data bases, and other statistics packages. SPSS is accessible via easy to use pull-down menus and can be programmed with a command syntax language. IBM SPSS Statistics Modules include IBM SPSS Statistics Base, IBM SPSS Advanced Statistics, and other modules such as Bootstrapping, Categories, Custom Tables, Exact Tests and Missing Values.
SAS
SAS stands for Statistical Analysis System. It is a statistical and information system that performs sophisticated data management and statistical analysis. It is better for the manipulation of large data sets or multiple datasets at the same time. SAS has many components. For statistical analysis we use SAS Base and SAS/STAT. SAS uses SAS programs to manipulate data and to conduct statistical procedures. SAS programs have two major parts, the DATA step and steps for a variety of procedures. The Data step is used to read and write data and to manipulate data sets. Procedure steps are used for statistical analyses. Statistical procedures in SAS go beyond the basics to much more advanced statistical analyses.
