Graduate Certificate in Psychological Statistics

The Department of Psychological Sciences offers a “Graduate Certificate in Psychological Statistics.”  This is a graduate-level certification program that is available to students currently admitted to a graduate degree program at Purdue. Students earning the certificate will demonstrate a broad theoretical understanding of advanced quantitative methods.  They will also learn the technical skills (e.g., software; computer programming) necessary to integrate these advanced methods into their substantive research programs. 

CERTIFICATE LEVEL

The certificate is offered at the graduate level only, not the post-baccalaureate level.

ADMISSIONS CRITERIA

  • Bachelor’s degree from an accredited institution.
  • Minimum undergraduate GPA of 3.0/4.0 with the possibility of conditional admission for applicants who do not meet this requirement.
  • Minimum TOEFL score of 550 or higher on the paper-based test, or 77 or higher on the Internet-based test (iBT) for applicants whose native language is not English. Applicants who take the TOEFL IBT must achieve the following minimum test scores, in addition to the overall score of at least 77: reading, 19; listening, 14; speaking, 18; and writing, 18. Applicants taking the IELTS must score at least 6.5 on the Academic Module. Applicants taking the PTE must score at least 58.
  • Students must already have completed a two-course introductory statistics sequence that is required of all graduate students in Psychological Sciences (as outlined immediately below) prior to being eligible to enroll in any of the courses comprising the certificate.  

 

Specifically, students must take one course from among the following. The dates in parentheses indicate when these courses will be offered in the next few years:

 

PSY 63100 (Multiple Regression Analysis) {Spring 2018; Spring 2019; Spring 2020}

STAT 51100 (Statistical Methods) {Fall 2017; Spring 2018; Fall 2018, Spring 2019}

STAT 51200 (Applied Regression Analyses) {Fall 2017; Spring 2018; Fall 2018, Spring 2019}

 

AND one different course from among the following:

 

PSY 60601, (ANOVA for the Behavioral Sciences) {Fall 2017; Fall 2018, Fall 2019, Fall 2020}

HDFS 61300, (Statistical Approach for Dev and Fam Researchers) {Fall 2017; Fall 2018; Fall 2019}

STAT 51200, (Applied Regression Analysis) {Fall 2017; Spring 2018; Fall 2018, Spring 2019}

STAT 51400, (Design of Experiment) (Fall 2017; Spring 2018; Fall 2018, Spring 2019}

 

OR students may take:

 

PSY 60000 (Statistical Inference) {Fall 2017, Fall 2018, Fall 2019, Fall 2020}

and

PSY 60100 (Experimental Design) {Spring 2018, Spring 2019, Spring 2020}

Students must be admitted to a graduate degree seeking program at Purdue University.

  • Students in Psychological Sciences will be eligible to earn the certificate.
  • Students outside of Psychological Sciences will be eligible to earn the certificate.

 

ADMISSION PROCESS

The admission process will parallel that for degree-seeking students at the graduate level.

All applicants to the Psychological Statistics Graduate Certificate Program must complete and submit the on-line application through our Graduate School at: https://gradapply.purdue.edu/apply/

Applicants will be required to pay a $60 application fee at the time of application.

In addition, applicants must provide a current Purdue transcript OR indicate “YES” on the application where it says “I consent and authorize Purdue University faculty and/or staff to access my Purdue University academic record for university business.”  Students from outside of Psychological Sciences must also provide a completed G.S. Form 18, Dual Graduate Program Request signed by the applicant and the Head of the applicant’s home department.

All application materials must be received by:  August 1 (for Fall admission) and December 1 (for Spring admission). Students may apply at any time prior to, during, or after completion of the course requirements, but no later than August 1 to qualify for a December certificate or December 1 to qualify for a May certificate.  

 

COMPLETION REQUIREMENTS

  • The certificate shall require a minimum of 9 credit hours taken for a letter grade. 
  • No more than 9 credit hours earned in non-degree status, including credit hours earned toward completion of a single certificate or more than one certificate, may be applied toward a graduate degree.
  • Courses that have been certified as undergraduate excess may be used to satisfy requirements for a certificate.

 

COURSES

Students must select three courses from the following list of courses with at least two of those courses coming from the Department of Psychological Sciences. The dates in parentheses indicate when these courses will be offered in the next few years:


PSY 60500 – Applied Multivariate Analysis {Spring 2018; Spring 2020}

A survey of the most frequently employed multivariate research techniques, such as multivariate generalizations of univariate tests and analysis of variance, principal components, canonical analysis, and discriminant analysis. A central theme of the course is the general linear model, both univariate and multivariate. A multipurpose program for this model provides the student with practical experience in conducting multivariate research. Some prior exposure to elementary matrix algebra is recommended. 


PSY 60600 – Intensive Repeating Measures {Fall 2017; Fall 2019}

The primary purpose of this course is to learn how to model data from intensive repeated measures designs, also known as intensive longitudinal data, collected from individuals, dyads, and groups. We will primarily use a multi-level modelling framework for these analyses, so prior experience with MLM or HLM is helpful, though not explicitly required. Intensive longitudinal data analysis can be applied to a wide range of data. These analyses are well-suited to modelling processes collected via methods such as daily diary studies, ecological momentary assessment, psychophysiological approaches, and behavioral assessments that capture multiple data points per observation. These approaches are most effective when applied to samples with a minimum of 10-20 data points per individual, and up to thousands of data points per individual. ​


PSY 60700 – Scaling & Measurement {Spring 2019; Spring 2021}

An introduction to the theory of measurement and a survey of modern scaling methods (unidimensional and multidimensional, metric and nonmetric) within the framework of the modern theory of measurement. Some prior exposure to elementary matrix and set algebra is recommended.


PSY 60800 – Measurement Theory & The Interpretation of Data

The theory of measurement and the development of reliability and the Spearman Brown equations, true scores and variables, and correction for attenuation. Variance or covariance of combinations of variables. Item analysis and test construction strategies. Reliability and validity of measurements and the influence of measurement error and measurement threats to research design.


PSY 61000 – Multivariate Analysis in Behavioral Sciences {Fall 2017; Fall 2018; Fall 2019; Fall 2020}

This course examines the application of multivariate methods to the analysis of organizational data. Topics to be covered include: matrix algebra, the general linear model, multivariate analysis of variance, canonical correlation, discriminant function analysis, and factor/component analysis. Time will also be spent on issues in data screening. Be aware, this is an advanced doctoral-level statistics course. As such, emphasis is placed on the theory, mathematics, assumptions, application, and interpretation of multivariate statistics, specifically within the context of organizational research.


PSY 61101 – Multilevel Theory, Measurement, and Analysis

This class is designed to provide doctoral students with an introductory treatment of multilevel theory building and testing. Issues to be discussed include: multilevel theory building, composition and compilation models, aggregation, aggregation bias, the role of within-group agreement in multilevel modeling, cross-level inference, cross-level interactions, and hierarchical linear modeling. If time permits, we will discuss other special topics based on class interest (e.g., HLM & dyadic data, HLM & missing data, HLM & ordinal data, multilevel mediation).


PSY 64600 – Multilevel Modeling {Spring 2018; Spring 2019; Spring 2020}

This course gives students a basic grounding in the class of statistical techniques known as multilevel modeling (MLM), also known as hierarchical linear modeling (HLM), mixed models, or random coefficient models. Primary discussions will be on applications of these models to the study of marriages, relationships, families, aging, and child and adult development, but also will touch on biomedical, educational, and economic examples. The focus is on three types of multilevel models: growth-curve models, organizational models, and daily experience models. Students will also learn how to use SAS Proc Mixed for conducting MLM analyses. Students are assumed to have taken at least two graduate statistics courses and have a solid understanding of regression analysis.


PSY 64600 – Bayesian Statistics {Fall 2018; Fall 2020}

The course will explain why you might want to use Bayesian methods instead of frequentist methods (such as t-tests, ANOVA, or regression). The general plan is to explain some problems/difficulties with frequentist methods: Publication bias, optional stopping, questionable research practices; discuss differences between hypothesis testing and prediction: mean squared error, shrinkage; discuss methods for prediction: likelihood, AIC, BIC, cross-validation, lasso; explain the basic ideas of Bayesian methods: non-informative priors, informative priors; provide hands-on examples of applying Bayesian methods: Bayes Factors, hierarchical models; and discuss ways to make decisions: utility.


PSY 67400 – Structural Equation Modeling {Fall 2017; Fall 2019}

This is an advanced course in structural equation modeling (SEM), intended to provide doctoral students with an introductory treatment of a wide variety of models, including path models, exploratory and confirmatory factor models, structural regression models, and latent growth models. We will focus on path, factor, and structural regression models, as these will be most widely applicable to the students in the class. SEM has been used in a wide variety of disciplines, including economics, marketing, medicine, biology, etc. In this class, we will focus on using SEM within the social and behavioral sciences, and many of the examples presented in class will specifically come from psychological science. The instructor will introduce the students to three types of SEM software, Mplus, SPSS/AMOS, and SAS, but a majority of examples will be given in Mplus. Students are assumed to have taken at least two graduate statistics courses and have a solid understanding of linear modeling.


HDFS 62700 – Multilevel Modeling in Developmental and Family Research

This course gives students a basic grounding in the class of statistical techniques known as multilevel modeling (MLM), also known as hierarchical linear modeling (HLM), mixed models, or random coefficient models. Primary discussions will be on applications of these models to the study of marriages, relationships, families, aging, and child and adult development, but also will touch on biomedical, educational, and economic examples. The focus is on three types of multilevel models: growth-curve models, organizational models, and daily experience models. Students will also learn how to use SAS Proc Mixed for conducting MLM analyses. Students are assumed to have taken at least two graduate statistics courses and have a solid understanding of regression analysis.


HDFS 62800 – Structural Equation Modeling

This course is an introduction to classic structural equation models with latent variables (SEM). It provides an overview of the method including the origins of the method and two major model components: simultaneous equations and confirmatory factor analysis. We will learn model notation and review the matrix algebra and covariance structures that are used to define SEMs. The primary steps of implementing SEMs will be covered to include: model specification, model identification, parameter estimation, and model evaluation (model fit). Additional topics include moderation analysis using multiple groups, estimation for non-normal and categorical outcomes, and estimation with missing data.

CRITERIA FOR COMPLETING CERTIFICATE

  • Students must earn a minimum grade of B in each of the three (3) classes they select from the above list.
  • A maximum of 3 credits may be transferred from another institution. (Transfer credit must be approved by the Director of Graduate Studies (DGS) in the Department of Psychological Sciences, the instructor of the equivalent Purdue course and the student’s program area coordinator.  Students requesting transfer course equivalency must provide a syllabus for the course to be transferred as well as a transcript indicating that a grade of “B” or better was achieved. These documents should be provided to the Graduate Office in the Department of Psychological Sciences.)  
  • No credits may be used from undergraduate-level courses.
  • The certificate must be completed within 7 years of a student being admitted to the graduate school.
  • Courses used to satisfy the requirements for this certificate may not be used towards the completion of another certificate.
  • A total of 9 credits may be taken prior to admission to the certificate program and counted toward completion of the certificate.

 

COMPLETION REPORTING

Students enrolled in the Certificate Program are responsible for notifying the Graduate Office in the Department of Psychological Sciences once they have successfully completed the course requirements for award of the certificate.  This notification must be received at least 60 days prior to the expected award date.   Once notification is received, the Graduate Office will conduct an audit to certify completion and notify the Graduate School.        

 

TRANSCRIPTING 

 

The certificate will be posted separately once the requirements have been completed.

The graduate certificate will be recorded in the following manner:

  • Awarded: Graduate Certificate
  • Program: Psychological Sciences – Grad Cert
  • College: Graduate School
  • Campus: West Lafayette
  • Major: Psychological Sciences
  • Major Concentration: Psychological Statistics

-- Credits earned toward the certificate will be included in the computation of the overall GPA posted on the transcript.

-- The certificate will be printed by the Office of the Registrar and will share the common format and style of all certificates under the purview of the Graduate School.

-- The certificate will be awarded jointly by the Department of Psychological Sciences and the Graduate School. It will bear the signature of the head of Psychological Sciences and the dean of the Graduate School.

-- The certificate will be awarded at the normal times when degrees are awarded.

 

 

 

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