Author Question: Why would a researcher use an elimination criterion like Must not have baseline liver function tests ... (Read 57 times)

Jkov05 · **on:** Jul 8, 2018

Why would a researcher use an elimination criterion like Must not have baseline liver function tests that are outside the normal range? (Select all that apply.)

a. Impaired liver function may have been identified by the researcher as an extraneous variable.
b. The intervention planned by the researcher could have an adverse effect on individuals with impaired liver function.
c. Liver function tests are possibly being measured in order to detect the independent variable's effect on the dependent variable.
d. The researcher is attempting to eliminate persons with all lab tests that are outside normal range.
e. The dependent variable may be abnormally elevated in persons who do not have normal liver function.

Question 2

A study has a power level of .75. What does that mean? (Select all that apply.)

a. Study interventions will be statistically significant 75 of the time.
b. The study has a 75 chance of detecting differences if they exist.
c. The study's sample has a 25 attrition rate, at worst.
d. There is a 25 chance of detecting a type I error.
e. There is a 25 chance that the study will fail to reveal differences that actually exist.

diesoon · **Reply #1 on:** Jul 8, 2018

Answer to Question 1

ANS: A, B, C, E
Exclusion sampling criteria are those characteristics that can cause a person or element to be excluded from the target population. The usual reasons for exclusion criteria are that they are extraneous variables and, if present, they could affect the value of the dependent variable. Other reasons might include increased risk for some groups.

Answer to Question 2

ANS: B, E
Power is the capacity of the study to detect differences or relationships that actually exist in the population. Expressed another way, power is the capacity to correctly reject a null hypothesis. The minimum acceptable power for a study is commonly recommended to be 0.80 (80). However, the researcher can set this lower, if the researcher is willing to risk a larger likelihood of a type II error.