Pretest-Posttest - Clinfowiki (2024)

FAQs

What is a major problem with the pretest-posttest design? ›

The main problem with this design is that it improves internal validity but sacrifices external validity to do so. There is no way of judging whether the process of pre-testing actually influenced the results because there is no baseline measurement against groups that remained completely untreated.

Administrators should not post-test a student with the same questions they encountered in the pre-test. Doing so can produce invalid data because a student's progress cannot necessarily be attributed to the skills they have developed if they are already familiar with the test questions.

When it comes to conducting pre-post analysis, Repeated Measures ANOVA is probably the default method taught to all social science students. If repeated measures ANOVA sounds unfamiliar to you, fret not – it is actually just an extension of the more commonly known paired t-test, aka dependent t-test.

The main weakness of pre- and post-test design is that it cannot detect other possible causes of positive or negative results among the participants.

Two groups, Nonrandom Selection, Pre-test, Post-test

The main weakness of this research design is the internal validity is questioned from the interaction between such variables as selection and maturation or selection and testing.

Disadvantages of Pre-Post Test Design (meera, n.d.): In the absence of a control group, it cannot account for confounding variables or non-programmatic influences on outcome indicators.

Pre- and post-tests are used to measure knowledge gained from participating in a training course. Active learning strategies include students are engaged in activities (e.g., reading, discussing, and writing) i.e., involved in more than passive listening, greater emphasis placed on developing student skills.

If you read a pretest and posttest study that does not include test-retest information, know that you cannot use the pretest and posttest scores to assess test-retest reliability due to the treatment introduction and the assumption that at least one treatment is likely to produce changes in pretest to posttest scores, ...

Use a paired t-test to determine whether this pre-post change is statistically significant. This test takes into account that the pre and post data are from the same set of individuals.

Which t-test should we use when we have pretest and posttest samples? ›

The Paired Samples t Test compares the means of two measurements taken from the same individual, object, or related units. These "paired" measurements can represent things like: A measurement taken at two different times (e.g., pre-test and post-test score with an intervention administered between the two time points)

The usual statistical method for comparing the pre- to the post-analysis is called the two-sample t-test. For each outcome of interest, you can perform a t-test to decide whether there is a statistically significant difference between the new version of the site versus the old.

This design can also increase the internal validity of a study by reducing threats such as history, maturation, testing, instrumentation, regression, and selection. Additionally, a pretest-posttest design can enhance external validity by allowing generalization of findings to other populations, settings, or times.

Pretest-posttest designs are a common type of experimental research that measure the effect of an intervention or treatment on an outcome variable by comparing the scores of the same participants before and after the exposure.

One-group research designs lack a comparison/control group. A one-group posttest-only design is a quasi- experimental research design in which a dependent variable is measured for one group of participants following a treatment.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case.

A major limitation to this design is the lack of a control or comparison group.

The biggest problem is that you have little evidence that what is observed at the posttest is due to the treatment because you have no pretest to use as a baseline measure (i.e., you do not know where they started out). This is the weakest of all experimental designs.