On Demand Workshop: Effectively Dealing with Missing Data without Biasing your Results

 

Learn the Right Ways to Solve the Problem of Missing Data

 

Missing data can throw a monkey wrench into even the most carefully designed research project… and it affects virtually EVERY data set.

The default “solution” is listwise deletion, which means you drop any case with a missing value.

But that brings with it a whole host of other issues:

  • You lose power when cases get dropped.
  • Results become biased when representative cases aren’t included.
  • Standard errors and p-values can become too large when you lose cases.

Thankfully, there are other solutions, including multiple imputation and maximum likelihood, either of which can give great outcomes and literally save your research.

We’ll teach you how in…

 

Effectively Dealing with Missing Data in SPSS
without Biasing Your Results


A 8-Hour Online Tutorial

 

 

 


 

“I struggled with trying on my own to connect the dots of what I learned in statistical courses, to applying it to my dissertation. The workshop was, in a word, Excellent! It provided me with knowledge and skills that have increased my ability to conduct a missing data analysis. And having the video recordings is the cream in the sauce.Personally I rate my experience as a 99.99% CI.”

— Anthony Brown, PhD Student, Walden University
Effectively Dealing with Missing Data Student


 

 

Karen Grace-MartinI’m Karen Grace Martin, your tutorial instructor for Effectively Dealing with Missing Data.

My goal is that by the end of the tutorial, you will learn the issues involved in missing data, have an in-depth understanding of the possible approaches and how to implement them, and understand the steps to diagnose the best approach in your specific situation.

 

 


 

Who Is This Tutorial For?

 

This tutorial is appropriate for both students and professionals.

This tutorial is for you if:

  • You’ve struggled with the devastating loss of power that comes from missing data.
  • You realize that listwise deletion and mean imputation don’t usually work well, and you’re looking for a better way.
  • You’re wondering if multiple imputation is too good to be true — and you want to learn what it is and how to do it.
  • You’ve struggled with using multiple imputation. You want to know when it’s really necessary, and when (and how) can you use the simple and powerful maximum likelihood instead.

 

 


 

How Does It Work?

 

This course is a 8-hour online tutorial.

The tutorial and accompanying materials are already available for you to access on your private tutorial website. Log in at any time from any internet-enabled computer, phone, or tablet… all from your own home or office.

In the tutorial videos, the instructor will present concepts and demonstrate how to execute those processes in SPSS.

 


 

As a participant in the Effectively Dealing with Missing Data in SPSS tutorial, you’ll have access to a participant-only website, your tutorial “hub.” That’s where you’ll access all tutorial resources and material, including:

  • A set of streaming training videos.

    Watch (and re-watch) these videos on YOUR schedule, as many times as you like, at the speed you desire.

    These videos explain the concepts and steps as well as step-by-step demonstrations in SPSS.

  • Supplemental PDF handouts.

    You’ll have access to the presentation slides, handy checklists, and worksheets to help you set up your next power calculation. You can download, print out, and take notes as you follow along.

  • SPSS data files from real research studies.

    So you can see how to deal with the challenges inherent in real data.

  • Exercises to practice what you’ve learned.

    With answers to check your work.

  • A place to submit written questions.

    Got a question as you’re reviewing the video recording or your notes? Just submit a question on the tutorial website. We’ll answer it right there.

  • Bonus videos.

    To enhance your understanding of related issues in data analysis, we’ve included the following bonus video webinars:

What Happened to R Squared? Assessing Model Fit for Logistic, Multilevel, and Other Models That Use Maximum Likelihood (50-minute video replay)
The 13 Steps to Performing Any Statistical Model (60-minute video replay)
Random Intercept and Random Slope Models (80-minute video replay)
Approaches to Missing Data: The Good, the Bad, and the Unthinkable (60-minute video replay)

You’ll have access to this site and all the related materials and resources for ONE FULL YEAR. That means you can re-watch sessions and ask additional questions any time during that 12-month period.

Often, our students report they understand the material at a deeper level on a second or third pass. This stuff is not always easy. You’ll learn more every time, so take advantage of it!

 

 

 


 

“Thank you so much. I learned so much in this workshop and it will change the way I examine (and design) my data sets from now on! I think the homework is an excellent part of the workshop. The video recordings are terrific. All of the resources provided for this workshop were outstanding.”

— Prudence Plummer-D’Amato, PhD
Effectively Dealing with Missing Data student


 

What’s Covered in the Tutorial?

 

The tutorial material is broken into five modules, which are available immediately via the tutorial website.

Module 1: Missing Data – The Problem and Basic Solutions

  • Part 1: What is Missing Data?
  • Part 2: Missing Data Mechanisms
  • Part 3: The Four Main Approaches
  • Part 4: Complete Case Analysis
  • Part 5: Imputation

In this first module, you’ll get the big picture. The real issues, causes, and the solutions. You’ll learn step by step what the different mechanisms are–exactly how random the missingness is and how that affects your results.

You’ll get an understanding of where missing data fits into an analysis strategy and its relationship to other types of problem data–censoring, truncation, and other partial information.

And finally, we’ll explore two traditional, simple techniques for dealing with missing data–complete case analysis and single imputation. They do work in some situations, but they’re disasters in others. You will learn how to tell the difference, and how to use them well.

Module 2: Multiple Imputation

  • Part 1: What is Multiple Imputation: The Concept
  • Part 2: When to Use it
  • Part 3: How to Do it, Step-by-Step, in SPSS

Multiple Imputation is a godsend in some really hairy missing data situations. Even with up to 50% of data missing, it can give you unbiased parameter estimates, standard errors, and full power. But it has to be done well, and that’s not always easy. It requires a solid imputation algorithm and model.

This module will teach you, in detail, how to build an imputation model, how it differs from your analysis model, and what to do with the resulting imputed data.

Module 3: Multiple Imputation in Practice – Special Cases

  • Part 1: Multiple Imputation for Categorical Variables
  • Part 2: Imputation of the Dependent Variable
  • Part 3: The Role of Interaction Terms and Transformations in Imputations
  • Part 4: Imputing Scales or Scale Items

Multiple Imputation is very simple if only one predictor variable has missing data, it is highly correlated with other variables, and if it is continuous and normally distributed. But real data is never so clean.

Luckily, multiple imputation can handle a lot of mess. So in this module, we’ll explore how to do multiple imputation in many messy situations, so you’ll know how to make solid analysis decisions even with messy data.

Module 4: Maximum Likelihood and Non-Ignorable Missing Data

  • Part 1: Maximum Likelihood Approaches
  • Part 2: Non-Ignorable Missing Data

Multiple Imputation isn’t the only game in town. There are a number of Maximum Likelihood techniques for running models that have all the advantages of Multiple Imputation without the hassle of imputing anything.

You may already be using some of them. And if you’re running linear models, you can take advantage of these techniques right as you run your models. No extra steps required.

It’s actually quite easy to do. But it only works for linear models.

So in part 1 you’ll learn what maximum likelihood estimation is, the types of analyses for which it works, and the exact steps to implement it.

Then in part 2, we’ll briefly discuss the approaches available for non-ignorable missing data. This is where you really have to make some crazy assumptions because the approaches require you to know something about the missing values.

Module 5: Missing Data Diagnosis

  • Part 1: Decision Factors in Choosing an Approach
  • Part 2: Missing Data Diagnosis, Step-by-Step
  • Part 3: Conclusions

Part of the reason it is so hard to learn how to deal with missing data is that the right approach depends on how much data are missing, patterns of missing data, why the data are missing, and how you will use the data in analysis.

These all vary in different types of research. Learning how to analyze the patterns and reasoning for choosing an approach may be the most important part of the workshop.

This is actually the first step in dealing with missing data, but we save it for last so you have a clear picture of what your options are once you do the diagnosis.

So in this module, you’ll learn, in detail, how to analyze the patterns of missingness to figure out the most likely mechanism, the effects of the missing data, and the best way to proceed in dealing with it.

 

 


 

Full Tutorial Investment: $297

 


 

 

Your Tutorial from The Analysis Factor Includes:

  • 1 year of access to the tutorial website
  • All the exercises and handouts you’ll need
  • The opportunity to submit questions to be answered on your tutorial website

 


 

“Karen, I would like to thank you for the wonderful job with the workshop. I got a lot from it. You are a great presenter (very understandable, given the complexity of some of the presented concepts). It is a nice push for further self-studies when working on my projects. I will definitely will register for other ones.”

— Anna Nadirova, Educational Researcher (Edmonton, Alberta, Canada)
Effectively Dealing with Missing Data Student


 

 

About Your Instructor

 

I’m Karen Grace-Martin, your instructor.

As president and founder of The Analysis Factor, I’ve been supporting researchers like you through their statistical planning, analysis, and interpretation since 1997.

With masters degrees in both applied statistics and social psychology, I’ve been honored over the past 15 years to work with everyone from undergrad honors students to Cornell professors, and from non-profit evaluators to corporate data analysts.

After seeing so many smart people get nervous, uncertain, and downright phobic about analyzing their data, I made it my mission to remove the barrier between research and statistical analysis.

I want to banish the confusion that makes eyes glaze over, and instead explain statistical concepts in plain English.

My goal is to help you improve your statistical literacy so you can bring your important research results into the light with confidence.

 

Prerequisites

 

So what kind of background in statistics do you need?

Our tutorials and workshops are for researchers, not statisticians.

That being said, you’ll get the most from this tutorial if you have a MINIMUM two statistics classes, and at least two years experience in data analysis.

We’ll be using SPSS. Important: SPSS can only do multiple imputation in version 17.0 and higher, but there is a work-around for earlier versions, which I will show you.

For any of the SPSS work, you will need to have the missing values add-on module. If you have it, “Missing Values” will appear in your Analyze menu. If you don’t and are employed by a university, you can get a one-year license for Windows or Mac to the full SPSS suite, including all their modules at On the Hub. Note that the Grad Pack does NOT contain the Missing Values Module, but the Faculty Pack does.

We will use AMOS for Full Information Maximum Likelihood. AMOS now comes bundled with most versions of SPSS. No prior experience using AMOS is necessary.

If you have questions about whether you’re ready for this tutorial, just email us. We’ll give you our honest opinion. We want you to succeed!

 

 


 

“I would recommend this one and any other (and I already did) workshop organized by The Analysis Factor. Excellent materials provided, available question and answer sessions, and ability to study at our own time. Karen and the whole team are amazing!”

— Effectively Dealing with Missing Data Student


 

 

Your Satisfaction Is Guaranteed

As with all our programs, your satisfaction is guaranteed. If you participate fully in this tutorial – read, and try out everything included – and find you are not satisfied for any reason, we will give you a full refund, no questions asked. Just notify us within 90 days of purchasing the program.

 

 

FAQs

Q: Will I be able to keep up with the tutorial material?

If you meet the prerequisites listed above, you’ll be prepared to understand the tutorial materials.

Each module includes about 1.5 hours of instructional videos, plus exercises and supplemental materials. We also suggest aside some time (2-3 hours) to re-run class examples, attempt the exercises on your own, and watch the bonus videos.

Remember — you also have 12 months to revisit the material! It’s completely on YOUR schedule.

You know how this goes: The more time you put in, the deeper your understanding.

Q: Things are very busy at work right now, and I’m afraid I won’t have time to keep up. How much time will this really take?

We’ve worked really hard to make sure people can keep up with the tutorial in the midst of their normal work life. And with on-demand access, you’re all set to deal with unexpected deadlines or “life” emergencies.

The core videos total approximately 8 hours. If you have a crazy hectic month ahead, you can fit watching the materials into your schedule. You can also submit your questions in writing through the tutorial website.

One reminder: You have access to the tutorial website, all materials, and a place to ask questions for a year. So if you get behind, you aren’t missing out. You can catch up on your schedule.

Q: I teach/work/sleep during this time/live outside the U.S and cannot attend at specific times. Is there any way to register and access these sessions later?

Yes! That’s the beauty of our on-demand tutorials. We have participants from many time zones with many different work schedules. In order to support our diverse student base, all tutorial sessions are pre-recorded and available immediately. You can also download and keep these videos forever, so if you need to refresh your memory at any point in the future, all the material is yours to review.

Q: I’m outside the U.S. Can I still participate?

Yes. We have participants in our tutorials and workshops from many different countries.

Q: Can I pay with PayPal?

Yes. When you check out, you can pay directly using our system or paying with PayPal.

Q: This really isn’t a good time for me. Will you be offering this tutorial again soon?

This tutorial (and several others!) is available year-round. You can sign up now and get access to all the course materials (trainings, support, exercises, the option to ask questions, and any additional bonuses). You’ll get all the materials to download and review at your convenience, and you can access them for free any time in the next 12 months.

Plus, ongoing support is included through access to the tutorial website for a full year.

Q: What software do you support for this class?

You do NOT need prior experience with any specific statistical software. During the tutorial, we will be using SPSS. You will need the SPSS Missing Values module and AMOS, so make sure your license has both.

 

Have additional questions? We’re here to help! Just email us at support@analysisfactor.com.

 

 


“Well set out, great support. excellent content.”

— Effectively Dealing with Missing Data student