# Jeff Meyer, MBA, MPA

### Statistical Consultant and Instructor

Jeff Meyer is a professional statistical consultant with an MBA from the Thunderbird School of Global Management and a Master’s Degree in Public Policy from NYU, with a focus in quantitative analysis.

In consultation, Jeff’s main focus areas are multivariate, logistic (bivariate, ordinal and multinomial), mixed models,count models (Poisson, negative binomial, zero inflated, truncated, and censored regression) multiple imputation models for missing data, exploratory factor analysis, confirmatory factor analysis using SEM and latent class analysis.

In the event the client is short on time and needs their data set cleaned, Jeff has created templates incorporating macros and loops to quickly prepare a data set for analysis.

Jeff understands that to be an effective instructor and consultant it takes more than subject matter knowledge and a logical approach to analyzing data. You must enjoy working with people and care about their success.

Jeff started working professionally with data in 1987 after obtaining his MBA. His responsibilities included creating financial models to analyze economic and market risks.

With the collapse of the financial markets in 2008 Jeff turned his efforts to using statistical analysis for the purpose of creating sound public policy.

Since joining The Analysis Factor, he has taught workshops on linear regression and count models, written the Stata code for all workshops, served as a panelist for weekly Statistically Speaking Q&A and written articles on numerous topics.

## The Craft of Statistical Analysis Webinars

All of these were taught by Jeff and access to all recordings are available at no charge.

**How to Benefit from Stata’s Bountiful Help Resources****Ask Us Anything! Your Questions Answered by Our Consultants****Five Tips and Tricks: How to Make Stata Easier to Use****Unlocking the Power of Stata’s Macros and Loops**

## Jeff’s Workshops

**Linear Models: Increasing Your Statistical Confidence**

**Level:**Introductory

**Software Used:**SPSS, SAS, R, Stata

**Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models**

**Level:**Advanced (Solid experience running linear models is required)

**Software Used:**SPSS, SAS, R, Stata

**Structural Equation Modeling**

**Level:**Advanced (Solid experience running linear models is required)

**Software Used:**SPSS, SAS, R, Stata, Mplus

## Jeff’s Blog Posts

- October 2019 Member Training: Reporting Structural Equation Modeling Results
- How Does the Distribution of a Population Impact the Confidence Interval?
- How Confident Are You About Confidence Intervals?
- Correlated Errors in Confirmatory Factor Analysis
- The Importance of Including an Exposure Variable in Count Models
- Member Training: Multiple Imputation for Missing Data
- Recoding a Variable from a Survey Question to Use in a Statistical Model
- A Strategy for Converting a Continuous to a Categorical Predictor
- A Useful Graph for Interpreting Interactions between Continuous Variables
- Descriptives Before Model Building
- Using Predicted Means to Understand Our Models
- Removing the Intercept from a Regression Model When X Is Continuous
- Statistical Models for Truncated and Censored Data
- Count vs. Continuous Variables: Differences Under the Hood
- Differences in Model Building Between Explanatory and Predictive Models
- Member Training: Latent Growth Curve Models
- Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
- Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
- Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model
- Getting Accurate Predicted Counts When There Are No Zeros in the Data
- The Problem with Linear Regression for Count Data: Predicting Length of Stay in Hospital
- Member Training: Marginal Means, Your New Best Friend
- Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression
- Segmented Regression for Non-Constant Relationships
- Interpreting Interactions in Linear Regression: When SPSS and Stata Disagree, Which is Right?
- Member Training: Segmented Regression
- Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims
- Two-Way Tables and Count Models: Expected and Predicted Counts
- Understanding Incidence Rate Ratios through the Eyes of a Two-Way Table
- Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model
- The Impact of Removing the Constant from a Regression Model: The Categorical Case
- Count Models: Understanding the Log Link Function
- The Difference Between Truncated and Censored Data
- Creating Graphs in Stata: From Percentiles to Observe Trends (Part 2)
- Converting Panel Data into Percentiles to Observe Trends in Stata (Part 1)
- Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
- Member Training: Working with Truncated and Censored Data
- Incorporating Graphs in Regression Diagnostics with Stata
- Free May Craft of Statistical Analysis Webinar: Unlocking the Power of Stata’s Macros and Loops
- Linear Regression in Stata: Missing Data and the Stories it Might Tell
- Issues with Truncated Data
- Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
- The Wonderful World of User Written Commands in Stata
- Missing Data Diagnosis in Stata: Investigating Missing Data in Regression Models
- Using the Same Sample for Different Models in Stata
- Hierarchical Regression in Stata: An Easy Method to Compare Model Results
- Free December COSA Webinar: How to Benefit from Stata’s Bountiful Help Resources
- Stata Loops and Macros for Large Data Sets: Quickly Finding Needles in the Hay Stack
- Using the Collapse Command in Stata
- Five Tips and Tricks: How to Make Stata Easier to Use
- Using Stored Calculations in Stata to Center Predictors: an Example
- Argggh! How Do I Output Tables and Graphs From Stata?
- Loops in Stata: Making coding easy
- Macros in Stata, Why and How to Use Them
- Using Stata Efficiently to Understand Your Data
- Why Use Stata?