Chapter 10- Rigor and Validity in Quantitative Research Flashcards
Validity
“the appropriate truth of an inference”
inferences that a cause results in a hypothesized effect are valid to the extent that researchers can marshal strong supporting evidence
Validity is always a matter of degree, not an absolute
Validity is the property of an inference- not of a research design, but design elements profoundly affect the inferences that can be made.
Threats to validity
reasons that an inference could be wrong
when researchers introduce design features to minimize potential threats, the validity of the inference about relationships under study is strengthened.
statistical conclusion validity
concerns the validity of inferences that there truly is an empirical relationship or correlation, between the presumed cause and effect.
The researcher’s job is to provide strong evidence that an observed relationship is real.
internal validity
concerns the validity of inferences, that, given that an empirical relationship exists, it is the independent variable, rather than something else, that caused the outcome.
Reserachers must develop strategies to rule out the plausability that some factor other than the independent variable accounts for the observed relationship
construct validity
involves the validity of inferences “from the observed persons, settings, and cause and effects operations included in the study to the constructs that this instance might represent:
one aspect concerns the degree to which an intervention is a good representation of the underlying construct that was theorized as having the potential to cause beneficial outcomes.
another issue concerns whether the measures of the outcomes are good operationalizations of the construct for which it is intended.
external validity
concerns whether inferences about observed relationships will hold over variations in persons, setting, or time.
relates to the generalizability of the inferences- a critical concern for evidence-based nursing practice
Controlling Confounding Participant characteristics
1) Randomization
2) Crossover- participants serve as their own controls
3) Homogeneity-participants are homogenous with respect to confounding variables. results can not be generalized to tpye of people who did not participate in the study
4) Stratification/Blocking
5) Matching
6) Statisitical control
Statistical Conclusion Validity
statistical methods are used to support inferences about whether relationships exist
Researchers can make design decisions that protect against reading false statistical conclusions
Low statistical Power
statistical power-
When small sample sizes are used, statistical power tends to be low and the analyses may fail o show that the IV and DV are related- even when they are.
maximizing precision- which is achieved through accurate measuring tools, controls over confounding variables, and powerful statistical methods.
Internal Validity
The extent to which it is possible to make an inference that the IV, rather than another factor, truly had the causal effect on the outcome (DV)
If researchers do not manage confounding variation, the conclusions that the outcome was caused by the independent variable is open to challenge
6 threats to internal validity- each threat represent an alternative explanation that competes with the independent variable as the cause of the outcome.
Temporal ambiguity
the cause most precede the effect
establishing temporal sequencing may be difficult in correlational studies- it may be unclear whether the IV preceded the DV, or vice versa.
This is especially true in cross-sectional studies
Selection threat
emcompasses biases resulting from preexisting differences between groups.
When not selected at randon, the groups copared are seldom completed equivalent
Selection bias is the most problematic and frequent threat to internal valididty in studies not using an experimental design
History threat
Concerns the occurrence of external events that take place concurrently with the IV and that can affect outcomes.
Maturation threat
processes occurring during the study as a result of the passage of time rather than as a results of the IV
a one-group pretest-posttest design is highly suspective to this threat
refers to any change that occurs as a function of time
Mortality/Attrition threat
Individuals dropping out of a stop
attrition bias can occur in single-group quasi-experiments if those dropping out of the study are biased subset that makes it look like a change in average values resulting from the treatment
prospective cohort study- may be differential attributions between groups being compared because of death, illness, relocation.
the longer the study, the greater the risk