consider the threats to validity in quantitative research

consider the threats to validity in quantitative research

consider the threats to validity in quantitative research

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 issues have on design decisions, and the strategies used to address them. You will also annotate a quantitative journal article on noncompliance patients research topic.

  • Explain threats to internal validity and external validity in quantitative research
  • Explain strategies to mitigate threats to internal validity and external validity in quantitative research
  • Identify ethical issues in quantitative research
  • Explain how ethical issues influence design decisions in quantitative research
  • Explain criteria for a research topic to be amenable to scientific study using a quantitative approach
  • Apply strategies for addressing ethical issues in quantitative research
  • Annotate a quantitative research article
  • APA
  • attachment

    ThreatstoInternalValidity.docx

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.

Get a 10 % discount on an order above $ 100
Use the following coupon code :
NURSING10