Design for Learning- Principles, Processes, and Praxis, 2021a

technique than simple correlation (r). This introduces the concept of a
statistical decision tree. A statistical decision tree helps a researcher
make the right decisions about using the appropriate methods for
analysis. Click here to see an image of a statistical decision tree or
here for a computerized model.
Analyzing Group Differences (Causality and Experimental
Analysis)
T-tests are used to compare differences in two groups, most often the
mean (average) difference of two groups. Essentially, all statistical
tests are measuring whether differences are due to more than chance
alone. The assumption behind comparing two differing tests are that
some experimental treatment caused the differences; in instructional
design, usually the designed curriculum or tool is assumed to be
causal.
The most common t-test analysis is an independent samples t-test.
This type of test compares two different samples on a common
measure. For example, online and live student’s final test scores in a
course are compared. A paired samples t-test analysis is used when
you have two measurements on the same person (or thing). For
example, a pre- or posttest where student one’s pretest score is
compared with student one’s posttest score. A third type of t-test is a
one-sample t-test. This compares the mean of a single group with a
known group. For example, comparing a current cohort’s attitudes
towards learning math with baseline historical data from prior years.
But how do you analyze differences in samples when there are three
or more groups? You could perform several different t-tests, but this
can become very complicated when there are several groups. Analysis
of Variance (ANOVA) analyzes mean differences among several
samples and yields similar statistics to t-tests to show differences are
more than due to chance alone.
Analysis of Covariance is similar to ANOVA but seeks to remove the
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