Assessment of Statistics Degree Programs

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BS in Statistics

BS Alignment Matrix

Student Learning Outcomes

A student should demonstrate knowledge of the underlying principles, computational methods, and applications of:

1. Calculus-based probability
   1. Demonstrate knowledge of probability laws and conditional probability.
   2. Set up and solve distributional problems including problems that involve calculus.
   3. Demonstrate knowledge of properties of well-known distributions such as normal and binomial.
   4. Demonstrate knowledge of the basic theorems for linear combination of independent random variables.
2. Statistical inference
   1. Role of Central Limit Theorem in constructing large sample confidence intervals and tests of hypotheses for means and proportions.
   2. Use of the t-distribution including assumptions under which its use is appropriate.
   3. Frequency interpretation of confidence intervals, tests of hypothesis, and p-values.
   4. Knowledge of methods of estimation including maximum likelihood and method of moments, mean square error.
3. Analysis of variance
   1. Set up models for single factor and multiple factor treatment structures and interpret interaction and main effects.
   2. Apply multiple comparison procedures such as LSD and Tukey HSD.
   3. Distinguish between completely random and randomized complete block design and know how to use blocking effectively.
   4. Carry out analysis using SAS and interpret output in a way non-statisticians would understand.
4. Regression analysis
   1. Set up models for simple linear, multiple regression, and variants of the multiple regression model such as polynomial regression in which the parameters are linear.
   2. Carry out inferences for the model parameters.
   3. Construct prediction intervals and confidence intervals for E(Y|X), assess model fit or lack of fit.
   4. Carry out analysis using SAS and interpret output in a way non-statisticians would understand
5. Study design (experimental design or sampling)
   1. Select the appropriate experimental design or sampling plan for a scientific study.
   2. Be able to identify standard study designs
   3. Select appropriate models and analyses for standard study designs.
   4. Report results using tabular and graphical methods as appropriate and explain results in a way non-statisticians would understand.

Summary of Undergraduate Assessment Activities

All student learning outcomes were assessed in 2011 and changes from the previous year were noted. Based on a 3 point scale (1 = deficient, 2 = satisfactory, 3 = excellent), average scores ranged from 2.06 to 2.69 for our five learning outcomes indicating overall satisfactory outcomes. To provide resources to newer faculty in preparing their courses and to help achieve continuity of content across semesters, the department created what we call the Stat Course Share web site where faculty post instructional and assessment materials for others to use at their discretion. As the site develops we would also see it becoming an electronic forum where faculty may share ideas about teaching.

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Graduate Certificate in Applied Statistics

Certificate Alignment Matrix

Student Learning Outcomes

1. Student will understand the basics of applied statistics.
2. Student will understand the major issues in design of experiments.
3. Student will demonstrate capability with a major software package.

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Master's Degree in Statistics

Master’s Alignment Matrix

Student Learning Outcomes

A student in the Masters Program should demonstrate mastery in the following areas:

1. Communication Skills
   1. Public speaking and presentation skills
   2. Report-writing skills
   3. The ability to use graphical methods to display and interpret information
   4. The ability to work and communicate with researchers in other disciplines
2. Computer Skills
   1. The ability to use spreadsheets, word processors, and graphics packages
   2. Statistical computing skills / ability to use at least one statistical software package
3. Fundamental Statistical Knowledge
   1. An understanding of fundamental ideas of statistical theory
   2. An understanding of fundamental ideas of linear model theory
   3. An understanding of fundamental ideas of study design
4. Applications of Statistics
   1. An understanding of practical application of statistics to real problems
   2. An understanding of the research process / scientific method
5. Extension of Basic Statistics to Complex Problems
   1. The ability to link theory and applications
   2. The ability to draw parallels between different kinds of statistical methods
   3. The ability to investigate and implement new statistical methods

Summary of Activities for Assessment of the Masters Degree

The following assessment activities have been completed (as of Spring 2009):

• Student Learning Outcomes (SLOs) and the alignment matrix have been posted on the Department of Statistics website.
• Assessment forms have been created for Statistics 713, 710, 720, 722, 770, 771, and 860.
• Assessment rubrics have been created for Statistics 945 (Consulting Seminar) and the Masters Defense.

Current assessment activities (as of Spring 2009):

• Data is being collected from faculty instructors in Statistics 713, 710, 720, 722, 770, 771, and 860.
• Data is being collected upon a student’s completion of a consulting project for Statistics 945.
• Data is being collected from faculty observers of Masters Defenses.

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PhD in Statistics

Ph.D. Alignment Matrix

Student Learning Outcomes

1. The ability to carry out research in statistical science.
2. The ability to access and read statistical literature.
3. An understanding of the major modes of statistical inference: frequentist, decision theoretic, Bayesian, and likelihood.
4. Knowledge of statistical procedures based on computation and simulation.
5. The ability to communicate with applied scientists and the statistical community.

Summary of Progress on PhD Assessment

Student learning outcomes (SLOs) and an alignment matrix have been developed and posted on the Statistic’s Department Website. The alignment matrix lists courses, qualifying exams, and a dissertation as tangible criteria that will be used to assess how the PhD program aligns with SLOs. Preliminary exams, a research update, and a final defense are also listed, as is an exit interview. To supplement evaluation of the PhD program, survey forms for the preliminary exam and final exam have been drafted for the collection of quantitative data from faculty and students for program assessment. The survey form for the preliminary exam has been “piloted” for several cases and some initial data collected. Based on feedback from faculty that have completed this survey, some minor revisions may be made to improve the tracking of assessment criteria in the surveys to departmental and university SLOs.