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Jeff Meyer

Member Training: Reporting Structural Equation Modeling Results

by Jeff Meyer

The last, and sometimes hardest, step for running any statistical model is writing up results.

As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.

Any good statistical report includes enough information that someone else could replicate your results with your data.

[Read more…] about Member Training: Reporting Structural Equation Modeling Results

Tagged With: CFA, discriminant analysis, error term, factor loadings, Intercept, Latent Growth Curve Model, mean, mediation, parameter estimates, principal component analysis, reliability, reporting, SEM, Structural Equation Modeling

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Latent Growth Curve Models
  • Member Training: Confirmatory Factor Analysis

How Does the Distribution of a Population Impact the Confidence Interval?

by Jeff Meyer Leave a Comment

Spoiler alert, real data are seldom normally distributed.

How does the distribution influence the estimate of the population mean and the resulting confidence interval?

To figure this out, we randomly draw 100 observations 100 times from three distinct populations and plot the mean and corresponding 95% confidence interval of each sample.
[Read more…] about How Does the Distribution of a Population Impact the Confidence Interval?

Tagged With: confidence interval, Estimated marginal Means, normal distribution, population, right skewed, sample, sample size, shape of distribution, standard deviation, Uniform distribution

Related Posts

  • How Confident Are You About Confidence Intervals?
  • How to Interpret the Width of a Confidence Interval
  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
  • 5 Ways to Increase Power in a Study

How Confident Are You About Confidence Intervals?

by Jeff Meyer 2 Comments

The results of any statistical analysis should include the confidence intervals for estimated parameters.

How confident are you that you can explain what they mean? Even those of us who have a solid understand of confidence intervals can get tripped up by the wording.

Let’s look at an example. [Read more…] about How Confident Are You About Confidence Intervals?

Tagged With: confidence interval, estimate sample sizes, sample size

Related Posts

  • How Does the Distribution of a Population Impact the Confidence Interval?
  • How to Interpret the Width of a Confidence Interval
  • Member Training: Statistical Rules of Thumb: Essential Practices or Urban Myths?
  • The Effect Size: The Most Difficult Step in Calculating Sample Size Estimates

Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer 3 Comments

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.

But we can use a set of questions on a scale, called indicators, to represent the construct together by combining them into a latent factor.

Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.

[Read more…] about Correlated Errors in Confirmatory Factor Analysis

Tagged With: Confirmatory Factor Analysis, error term, Factor Analysis, latent variable, Model Fit

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • Measurement Invariance and Multiple Group Analysis
  • Why Adding Values on a Scale Can Lead to Measurement Error
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis

Member Training: Multiple Imputation for Missing Data

by Jeff Meyer

There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results.

Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the simplistic methods in many missing data situations.
[Read more…] about Member Training: Multiple Imputation for Missing Data

Tagged With: data issues, Missing Data, Monotone missing data, Multiple Imputation

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Recoding a Variable from a Survey Question to Use in a Statistical Model

by Jeff Meyer Leave a Comment

Survey questions are often structured without regard for ease of use within a statistical model.

Take for example a survey done by the Centers for Disease Control (CDC) regarding child births in the U.S. One of the variables in the data set is “interval since last pregnancy”. Here is a histogram of the results.

[Read more…] about Recoding a Variable from a Survey Question to Use in a Statistical Model

Tagged With: categorical predictor, continuous predictor, predictor variable, recode, survey, survey questions

Related Posts

  • A Strategy for Converting a Continuous to a Categorical Predictor
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  • Should I Specify a Model Predictor as Categorical or Continuous?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

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