Competence I: Ch 5, 7, & 8 Flashcards
Alternative explanations for study results
to draw valid conclusions about research question, the researcher must design a study that minimizes potential to generate alternative explanations for the studies results. Four major inferences made by researcher when evaluating validity of a design to avoid alternate explanations.
Statistical Conclusion Validity
the degree to which the researcher has come to the correct conclusion about the relationship.
Internal Validity
degree of certainty with which one can make statements about the existence of a causal relationship between variables.
Construct Validity
how well the variables chosen to represent a hypothetical construct actually capture the essence of the hypothetical construct.
External Validity
refers to the degree to which the causal relationship is generalizable across units, treatments, outcomes, and settings.
Philosophy of Science
conceptual specifics of validity and threats to validity are not concretely fixed as innate properties of research. These concepts are deeply embedded in the philosophy of science and evolve over time as philosophers think what it means to conduct research and make inferences about their findings.
Research Consumers
should examine threats independently because publication of results does not ensure that conclusions are valid. The DARE program is an example of this- generally believed effective when in fact not
Null Hypothesis
predicts that there is no relationship between the variables
Plausibility of a threat to validity
most threats discussed are possibly present in a study. The validity of a conclusion is suspect if a threat is plausible and the threat created conditions hat could have produced evidence supporting the conclusion.
-ex: significant difference in treatment effectiveness for volunteers versus nonvolunteers. However nonrandom assignment is a plausible threat and also could have explained the effectiveness of treatment found for the volunteer group
Low statistical power (Type II Error)
Incorrectly conclude there is no relationship when in fact one does exist. Major reason for this is that variability in the participants responses tend to obscure true relationships. This is also called error variance. Low power often comes from too few participants
Pretest
test given before manipulation. Can provide baseline or starting point to assist in measuring progress and growth. Also can be used to ensure groups are both at same starting level.
Time-Series Designs
multiple observations over time. Observations can involve same participant or similar participants. Interrupted time series design treatment is administered at some point in the series of observations. The point at which treatment takes place is interruption of series. Compare observations before and after treatment. (quasi-experimental)
Baker, Johnson, Kopala & Strout
Experimental post test only control group design in a laboratory setting (observe fake interpretation of client in a lab). Random assignment of participants to 3 conditions. Analogue b/c of the rehearsed fake client. Delayed control like waiting list control group
One-shot Pretest/Posttest Design
O1 X O2
Nonequivalent group post test only design
O2
Randomized post test only Design
R X O1
R O2
Determining tradeoffs in design and implementation of a study
designs that increase the certainty of both causal inferences (internal validity) and statistical conclusion validity may decrease the certainty of generalizing inferences from samples to populations (external validity) or meaning of the operations (construct validity). Tradeoffs occur with different types of research designs not only with regard to validity but also other factors.
Statistical tests in research studies
statistical test is used to examine whether there is a relationship between the variables in a study
Alternative Hypothesis
there is some true relationship between the variables in the study.
Statistical Significance
When a statistical test suggests chances of incorrectly concluding a relationship are fewer than 5 out of 100, 1 out of 100, or 1 out of 1000
P values and probability theory
statistical tests are based in probably theory and used to indicate whether you should reject or accept the null hypothesis. You would reject the null hypothesis is p<.05. The chances of incorrectly concluding that a true relationship exists are fewer than 5 in 100. Statistical testing of null hypothesis is embedded in probability theory and the philosophy of science and remains source of controversy.
Population
everyone fitting certain predetermined characteristics in the entire human species. This is what you are attempting to generalize your study results to.
Sample
The individuals in the study that have predetermined characteristics. You want your study to be representative of the diverse nature of the population so you can generalize your results to this population.
Type I Error
Incorrectly concluding that a true relationship exists