sample size

How Big of a Sample Size do you need for Factor Analysis?

Most of the time when we plan a sample size for a data set, it’s based on obtaining reasonable statistical power for a key analysis of that data set. These power calculations figure out how big a sample you need so that a certain width of a confidence interval or p-value will coincide with a scientifically meaningful effect size.

But that’s not the only issue in sample size, and not every statistical analysis uses p-values.

Chi-Square Test of Independence Rule of Thumb: n > 5

by Audrey Schnell Leave a Comment

We all want rules of thumb even though we know they can be wrong, misleading or misinterpreted.

Rules of Thumb are like Urban Myths or like a bad game of ‘Telephone’. The actual message gets totally distorted over time.
For example, you may have heard this one: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.

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?

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?

How to Interpret the Width of a Confidence Interval

by Christos Giannoulis 2 Comments

One issue with using tests of significance is that black and white cut-off points such as 5 percent or 1 percent may be difficult to justify.

Significance tests on their own do not provide much light about the nature or magnitude of any effect to which they apply.

One way of shedding more light on those issues is to use confidence intervals. Confidence intervals can be used in univariate, bivariate and multivariate analyses and meta-analytic studies.

Member Training: Model Building Approaches

by TAF Support

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box?

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?