Author Question: Random selection of 300 subjects yields a sample, but demographic analysis of that sample reveals ... (Read 103 times)

jwb375 · **on:** Jul 8, 2018

Random selection of 300 subjects yields a sample, but demographic analysis of that sample reveals that there are 99 teachers in the sample, despite the fact that there are far fewer than 33 teachers in the total sample. The sample can be said to be

a. Biased
b. Controlled
c. Multicausal
d. Based on probability

Question 2

John Stuart Mill's insistence that in order for causation to be demonstrated, there must be no alternative explanation for why a change in one variable leads to a change in the other variable.

This concept of alternative explanations is the idea that underlies which type of validity?
a. Statistical conclusion validity
b. Internal validity
c. Construct validity
d. External validity

leeeep · **Reply #1 on:** Jul 8, 2018

Answer to Question 1

ANS: A
The term bias means to slant away from the true or expected. A biased sample's composition differs from that of the population from which the sample was drawn. Bias is of great concern in research because of the potential effect on the meaning of the study findings. Multicausality refers to a scenario in which interrelating variables cause a particular effect Control means having the power to direct or manipulate factors to achieve a desired outcome. Error is often discussed in relation to the researchers ability to make accurate conclusions. Probability addresses relative, rather than absolute, causality. From the perspective of probability, a cause will not produce a specific effect each time that particular cause occurs.

Answer to Question 2

ANS: D
External validity is concerned with the extent to which study findings can be generalized beyond the sample used in the study. Statistical conclusion validity is concerned with whether the conclusions about relationships or differences drawn from statistical analysis are an accurate reflection of the real world: did the researcher use the right statistical tests in the proper way? Internal validity is the extent to which the effects detected in the study are a true reflection of reality rather than the result of extraneous variables: did the change in one variable really account for the change in the other variable? Construct validity examines the fit between the conceptual definitions and operational definitions of variables: are the study ideas measured in a way that makes sense?