1. A. Validity
In research, validity refers to whether the study actually represent the real life situation in its claim.
If the questions do not related to pathological gambling in any sense, the conclusion from the study would stray away from the actual cause that might influence the gambling. This make the data from the study cannot be used in any way or form to draw a conclusion.
2. A. Content validity
Content validity refers to whether the researchers have include all measures that could possibly influence the result of the research. Content validity must exist to ensure that the researchers could make the best possible conclusion for the phenomenons that occurs during the researches.
3. C. Known-groups paradigm
Known-groups paradigm refers to a situation where the researchers could ensure the validity of a certain research by including the group that they can control/discriminate. This group tend to filled with subjects with the same characteristic that can influence the result of the research.
4. B. Criterion validity.
Criterion validity commonly consist of predictive validity and concurrent validity. Concurrent validity is used to create a comparison among the measurements and outcome created by the measurement at the same time. Predictive validity on the other hand is used to create a comparison among the measurements and outcome created by the measurement that occurs later in the future.
Answer:
these are those I know
Explanation:
you can try to do other research
Answer:
option D
Explanation:
The correct answer is option D
For Senator Sly's reelection, the runner club contributed to there campaign fund and then he helps to pass legislation to add more jogging path from this incident we cannot conclude anything.
There can be two scenario
First, that Road Runner club donated his reelection fund so, to fulfill his promise he must build more jogging Path
Another scenario is that Senator Sly supported for jogging path i.e. Road Runner club funded him so, that he can build Jogging Path.
Answer: Independence of Samples
Explanation:
ANOVA is the analysis of variance/Assumptions
. The Assumptions under ANOVA are:
The experimental error of data are normally distributed: a mean of the experiments when repeated should be normally distributed.
There are equal variance between treatments: variances between samples must be evenly spread on each side of the mean
.
Independence of Sample: this assumes that each sample is chosen at random and not dependent on one another.
Jen's study violates this assumption as her samples are not random.