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Latent Class Analysis (LCA) is an increasingly popular method for classifying people into subgroups based on their classifications/responses to a set of variables chosen by the researcher. In LCA, probabilities are used to assign each individual into a subgroup. The subgroups can then be investigated further or used as categorical variables in comparative or regression models.

LCA is similar to factor analysis, except kind of reversed. In LCA variables are used to classify people into groups, whereas in factor analysis items or variables are combined onto factors.

Anyone is welcome to attend the webinar. However some knowledge of factor analysis and structural equation modeling will be useful for attendees in understanding the concepts presented.

All registrants will receive handouts and a link to a recording of the webinar. So if you cannot attend the live event, you can watch later at your convenience.

The webinar and recording are FREE! But space is limited.

Feel free to click on any of the webinars listed below to be directed to the Omega Statistics channel on YouTube to view for free!

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I’d love to hear from you about statistical topics or analyses you’d like to see in more detail so send me an email at [email protected] with your ideas and/or suggestions!

Spend some time with Elaine Eisenbeisz of Omega Statistics as she presents a four-part series on probability.

Part 1 of this four part series begins with the definition of probability and why it is used. Presentation will include the concepts of set notation, Rules of Probability, defining events A and/or B, and the difference between independent and mutually exclusive events.

What does it all mean? What is a mean? How do I know my data is normal?

IF it’s not NORMAL, does that mean it’s ABNORMAL?

Would you like to get to know your data better? Or maybe you kinda, but not really, understand the difference between a standard deviation and a standard error…

Then join Elaine Eisenbeisz of Omega Statistics as she presents an overview of compiling and interpreting descriptive statistics.

— Want to get a good handle on a treatment effect or risk factor but don’t have the time or means to run your own study?

— Or are you interested in synthesizing the current information into a study that pools data from many studies?

— Do you just like to read lots of journal articles in a particular body of work and want a better idea of what it all means?

Then join Elaine Eisenbeisz of Omega Statistics as she presents an overview of systemic reviews and meta-analysis.

We want our research to be reproducible. That is, we want to be sure that the measurements we use in our testing will give us similar results again and again in repeated testing.

Reliability is essential to building trust in the statistical analysis and measurements we use, and in the results we obtain.

Join Elaine Eisenbeisz for an overview of various reliability tests:

— Inter-rater reliability

— Test-retest reliability

— Internal consistency reliability

— Plus learn about various statistical tests of reliability with a bit of demonstration in SPSS

Anyone is welcome to attend the webinar. Some understanding of correlation is useful but not needed.

Cox regression (also called proportional hazards regression) is used to analyze the effect of several variables on survival or other time-to-event outcomes.

Join Elaine Eisenbeisz to learn about:

— Review of the Kaplan-Meier method with log-rank test

— Review of Interpreting Survival curves

— Assumptions for Cox proportional hazards models

— Analysis & Interpretation of Cox proportional hazards models.

Anyone is welcome to attend the webinar. However knowledge of multiple linear regression and logistic regression will be useful for attendees in understanding the concepts presented.

You see them reported in the literature. You see them in your computer output. You add them to your reports.

You even (kinda) understand them!

Or, maybe, not quite?

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of the how and why of various ratios we use often in statistical practice.

Which ones you ask? How about:

- Coefficient of determination
- Coefficient of variation
- Correlation coefficient
- Hazard ratio
- Likelihood ratio
- Odds ratio
- Relative risk ratio
- Variance inflation factor

Elaine takes some time during the event to show examples of some of the ratios “at work.”

Unlike Frequentist statistics, in which we assume we know nothing about phenomena until we sample and test it, Bayesian statistics allow us to take into account information we already know in our analysis and our conclusions.

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of Bayesian thought and techniques. Emphasis will be on applications to diagnostic tests and genetics.

Want to know how to find the right sample size for your study? Too few subjects and you’ll waste time. Too many subjects and you’ll waste time and money!

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of power analysis techniques for determining sample size (a priori techniques). Elaine will take some time during the event to work examples in GPower software.

You’ve developed some possible study questions and are well into your literature review. It is time to start building your study “for reals”.

The next step will be to define your research design and research method. What design should you use to best inform your research? What method would work best with what you have available? What is the dang difference between design and method? (Oh yes, there is a difference).

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of the types of research designs and methods which are the foundation of building a solid structure for collecting, analyzing, and interpreting your research.

A thorough and solid literature review should be your first step in designing a study. Join Elaine Eisenbeisz of Omega Statistics as she shares the tips and tools for navigating the literature review.

Contrary to popular opinion, the p-value is not the most important way, and in many cases not the best way, to support a study’s findings. Find out why effect sizes ARE important to research and why they are becoming more so.

In Back-to-Basics I, you learned the Why. Now understand the How. Elaine will present the processes of statistics as relates to the Scientific Method. This webinar is a must-see if you struggle with concepts of hypothesis testing and inference.

Why Statistics? Statistics are just guesses. Good guesses, if applied properly. Learn the value of, and limitations of, statistics in application. Elaine will also explain what some of those pesky statistics terms mean in the real world.

Wondering what to do with those categorical variables? Please join Elaine Eisenbeisz of Omega Statistics as she introduces you to chi-square and similar tests of association using contingency tables. She will also cover the use of contingency tables involving repeated measures.

Logistic Regression is something every researcher should have in his or her statistics toolbox.

Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of logistic regression. Learn how to code your data properly, check your assumptions for analysis, and how to understand and use odds ratios and likelihoods.

Multiple Regression is one of the most informative data analysis techniques there is!

Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of multiple regression using ordinary least squares techniques (OLS). Learn how to code your data properly, check your assumptions for analysis, and how to use a regression model to predict outcomes.

So many correlations, so little time!

Join Elaine Eisenbeisz of Omega Statistics as she presents an overview of various correlational analysis techniques. Learn which correlation fits your data best. You will also learn how correlation, chi-squares, and t-tests are related!

It will sound like cheating, but it isn’t. It’s so righteous dude! Multiple imputation (MI) is an effective and responsible way to handle data which is missing at random (MAR). You’ll find out what that means too…

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of MI concepts.

Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents a few tools and techniques for assuring your data entry and statistical analyses are in good form.

You have an idea of what you want to do for your research, but where do you start with the analysis plan? Start with knowing about the types of research designs and their applications. Join Elaine Eisenbeisz of Omega Statistics as she presents a brief yet useful overview of study design.