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effect size

Member Training: Interpretation of Effect Size Statistics

by guest

Effect size statistics are required by most journals and committees these days ⁠— for good reason. 

They communicate just how big the effects are in your statistical results ⁠— something p-values can’t do.

But they’re only useful if you can choose the most appropriate one and if you can interpret it.

This can be hard in even simple statistical tests. But once you get into  complicated models, it’s a whole new story. [Read more…] about Member Training: Interpretation of Effect Size Statistics

Tagged With: Cohen's d, Correlation, correlation indexes, effect size, effect size statistics, empirically derived, Glass, Hedges, interpreting, null hypothesis, probability of superiority, Proportion, strength association, superiority, variance

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Your Questions Answered from the Interpreting Regression Coefficients Webinar

by Karen Grace-Martin Leave a Comment

Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.

As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)

We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar

Tagged With: dummy coding, dummy variable, effect size, eta-square, Interpreting Interactions, interpreting regression coefficients, Reference Group, spotlight analysis, statistical significance

Related Posts

  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
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  • Interpreting (Even Tricky) Regression Coefficients – A Quiz

Member Training: Meta-analysis

by guest Leave a Comment

Meta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power, precision in effect size estimates, and providing a summary of individual studies.

But not all meta-analyses are done well. The three threats to the validity of a meta-analytic finding are heterogeneity of study results, publication bias, and poor individual study quality.

[Read more…] about Member Training: Meta-analysis

Tagged With: effect size, fixed effect, meta-analysis, PRISMA guidelines, random effect

Related Posts

  • Member Training: Power Analysis and Sample Size Determination Using Simulation
  • January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models
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Member Training: Power Analysis and Sample Size Determination Using Simulation

by guest Leave a Comment

This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:
  • A review of basic concepts of statistical power and effect size
  • A simulation-based approach to power analysis
  • An overview of how to implement simulations in various popular software programs.
[Read more…] about Member Training: Power Analysis and Sample Size Determination Using Simulation

Tagged With: ANOVA, effect size, mediation, mixed model, Path Analysis, Power Analysis, quantitative research, sample size, simulation

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Member Training: The Fundamentals of Sample Size Calculations

by Karen Grace-Martin 3 Comments

Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe that’s just me.

Regardless of how they make you feel, they are super important to do for your study before you collect the data.

[Read more…] about Member Training: The Fundamentals of Sample Size Calculations

Tagged With: accuracy, data collection, effect size, sample size, sample size estimates, scientific interest, testing

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Sample Size Estimation Without Past Reliable Pilot Data or Evidence

by Karen Grace-Martin Leave a Comment

Here’s a common situation.

Your grant application or committee requires sample size estimates.  It’s not the calculations that are hard (though they can be), it’s getting the information to fill into the calculations.

Every article you read on it says you need to either use pilot data or another similar study as a basis for the values to enter into the software.

You have neither.

No similar studies have ever used the scale you’re using for the dependent variable.

And while you’d love to run a pilot study, it’s just not possible.  There are too many practical constraints — time, money, distance, ethics.

What do you do?

[Read more…] about Sample Size Estimation Without Past Reliable Pilot Data or Evidence

Tagged With: effect size, Power Analysis, sample size estimates, standardized effect size

Related Posts

  • Member Training: Power Analysis and Sample Size Determination Using Simulation
  • Member Training: The Fundamentals of Sample Size Calculations
  • Two Types of Effect Size Statistic: Standardized and Unstandardized
  • The Effect Size: The Most Difficult Step in Calculating Sample Size Estimates

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