The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
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
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?
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?
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
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
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