Discussion: Designing Quantitative Research

 NEED SOMEONE WITH SCIENCE? PSYCHOLOGY/ RESEARCH BACKGROUND

In the context of research design, two types of validity, which speak to the quality of different features of the research process, are considered: internal validity and external validity. Assuming that the findings of a research study are internally valid—i.e., the researcher has used controls to determine that the outcome is indeed due to manipulation of the independent variable or the treatment—external validity refers to the extent to which the findings can be generalized from the sample to the population or to other settings and groups. Reliability refers to the replicability of the findings.

For this Discussion, you will consider threats to internal and external validity in quantitative research and the strategies used to mitigate these threats. You will also consider the ethical implications of designing quantitative research.

With these thoughts in mind:

By Day 4

Post an explanation of a threat to internal validity and a threat to external validity in quantitative research. Next, explain a strategy to mitigate each of these threats. Then, identify a potential ethical issue in quantitative research and explain how it might influence design decisions. Finally, explain what it means for a research topic to be amenable to scientific study using a quantitative approach.

Be sure to support your Main Issue Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

Read a selection of your classmates’ postings.

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© 2016 Laureate Education, Inc. Page 1 of 2

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,
get tired) during the course of the intervention could account for the
outcomes. Thus, unable to conclude with certainty that the “intervention”
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 quasi-

experimental designs for generalized causal inference. Boston, MA:

Houghton-Mifflin.

Threats to Internal Validity
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton-Mifflin.