consider the threats to validity in quantitative research and explore strategies to mitigate these threats. You will also consider the ethical issues in quantitative research, the implications these i
Threats to Internal Validity
(Shadish, Cook, & Campbell, 2002)
1. Ambiguous temporal precedence. Based on the design, unable to determine
with certainty which variable occurred first or which variable caused the other.
Thus, unable to conclude with certainty cause-effect relationship. Correlation
of two variables does not prove causation.
2. Selection. The procedures for selecting participants (e.g., self-selection or
researcher sampling and assignment procedures) result in systematic
differences across conditions (e.g., experimental-control). Thus, unable to
conclude with certainty that the “intervention” caused the effect; could be due
to way in which participants are selected.
3. History. Other events occur during the course of treatment that can interfere
with treatment effects and could account for outcomes. Thus, unable to
conclude with certainty that the “intervention” caused the effect; could be due
to some other event to which the participants were exposed.
4. Maturation. Natural changes that participants experience (e.g., grow older,0
caused the effect; could be due to the natural change/maturation of the
participants.
5. Regression artifacts. Participants who are at extreme ends of the measure
(score higher or lower than average) are likely to “regress” toward the mean
(scores get lower or higher, respectively) on other measures or retest on
same measure. Thus, regression can be confused with treatment effect.
6. Attrition (mortality). Refers to dropout or failure to complete the
treatment/study activities. If differential dropout across groups (e.g.,
experimental-control) occurs, could confound the results. Thus, effects may
be due to dropout rather than treatment.
7. Testing. Experience with test/measure influences scores on retest. For
example, familiarity with testing procedures, practice effects, or reactivity can
influence subsequent performance on the same test.
8. Instrumentation. The measure changes over time (e.g., from pretest to
posttest), thus making it difficult to determine if effects or outcomes are due to
instrument vs. treatment. For example, observers change definitions of
behaviors they are tracking, or the researcher alters administration of test
items from pretest to posttest.
9. Additive and interactive effects of threats to validity. Single threats interact,
such that the occurrence of multiple threats has an additive effect. For
example, selection can interact with history, maturation, or instrumentation.
Research Theory, Design, and Methods Walden University
© 2016 Laureate Education, Inc. Page 2 of 2
Reference
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasiexperimental designs for generalized causal inference. Boston, MA:
Houghton-Mifflin.