NCE Research and Program Evaluation
UPDATED Exam Questions And CORRECT
Answers
Parsimony (AKA Occam's Razor) - CORRECT ANSWER - interpreting results in the
simplest way
Basic research - CORRECT ANSWER - advances our theory
Applied research - CORRECT ANSWER - advances our practices
Alternative Hypothesis - CORRECT ANSWER - states that the treatment or IV does affect
the outcome of the experiment.
Null Hypothesis - states that the treatment or IV will not have an affect
Between Subjects Design - CORRECT ANSWER - different subjects get exposure or lack
of exposure to different things
In Subjects Design - CORRECT ANSWER - one pool of subjects receive or don't receive
the treatment (pre-test post-test design)
Parameter - CORRECT ANSWER - a value obtained from a population; summarizes a
characteristic of a population (e.g., age, sex, average male height)
It is theorized that all population parameters "fit" the normal curve
Statistic - CORRECT ANSWER - A value drawn from a sample
,Probability (aka significance level) - CORRECT ANSWER - the likelihood that something
will happen,
Used in true experiments
P for our field is generally .05 but can range from .000 to .10 and still be considered significant
findings or not due to chance,
P can be translated into a percentage that describes the portion of the sample whose results were
achieved by chance (i.e. .05 = 5% of the sample's scores were obtained by chance - not your
experimental design)
.05 means that differences would occur via chance only 5 times in 100; the experimenter will
obtain the same results 95 times out of 100
May be referred to as confidence level in the exam
Type 1 error (aka alpha error) - CORRECT ANSWER - rejecting the null when it is true
(saying there is significance in your treatment when there isn't),
False positive
The probability of committing a Type 1 error = the level of significance (P)
HINT: Alphas want power and will want to win even if that means faking a win
Type II Error (Beta Error) - CORRECT ANSWER - accepting the null when it is not true
(saying there isn't significance in your treatment when there is),
False negative
, How do you reduce Type 1 AND Type 2 errors? - CORRECT ANSWER - Increase sample
size
How do you reduce Type 1 errors but increase chance of Type 2 Errors? - CORRECT
ANSWER - decrease P levels
T-Test - CORRECT ANSWER - determines if a significant difference between two means
exist; t value statistic has to be higher than the t value in the table to be significant
Uses mean of 50 with each SD as 10 (Hence, a Z score of -1 would be a t-score of 40)
used for two samples to compare means, you obtain a single t score and compare it to the critical
t value based on the sample size and your significance level and if the t value you found is
greater than the critical t you have significance
for 2 groups
The values of a t-test are determined by degrees of freedom (influenced by sample size).
Analysis of variance
ANOVA - CORRECT ANSWER - F Statistic
used when there are 2 or more means to compare; if F obtained exceeds F in table, it is
significant;
one-way ANOVA = one independent variable (more than 1 level of one IV; e.g., 4 and 6 weeks of
assertiveness training);
two-way ANOVA = two independent variables, etc. (also called factorial analysis of variance
when 2+ IVs or MANOVA);
UPDATED Exam Questions And CORRECT
Answers
Parsimony (AKA Occam's Razor) - CORRECT ANSWER - interpreting results in the
simplest way
Basic research - CORRECT ANSWER - advances our theory
Applied research - CORRECT ANSWER - advances our practices
Alternative Hypothesis - CORRECT ANSWER - states that the treatment or IV does affect
the outcome of the experiment.
Null Hypothesis - states that the treatment or IV will not have an affect
Between Subjects Design - CORRECT ANSWER - different subjects get exposure or lack
of exposure to different things
In Subjects Design - CORRECT ANSWER - one pool of subjects receive or don't receive
the treatment (pre-test post-test design)
Parameter - CORRECT ANSWER - a value obtained from a population; summarizes a
characteristic of a population (e.g., age, sex, average male height)
It is theorized that all population parameters "fit" the normal curve
Statistic - CORRECT ANSWER - A value drawn from a sample
,Probability (aka significance level) - CORRECT ANSWER - the likelihood that something
will happen,
Used in true experiments
P for our field is generally .05 but can range from .000 to .10 and still be considered significant
findings or not due to chance,
P can be translated into a percentage that describes the portion of the sample whose results were
achieved by chance (i.e. .05 = 5% of the sample's scores were obtained by chance - not your
experimental design)
.05 means that differences would occur via chance only 5 times in 100; the experimenter will
obtain the same results 95 times out of 100
May be referred to as confidence level in the exam
Type 1 error (aka alpha error) - CORRECT ANSWER - rejecting the null when it is true
(saying there is significance in your treatment when there isn't),
False positive
The probability of committing a Type 1 error = the level of significance (P)
HINT: Alphas want power and will want to win even if that means faking a win
Type II Error (Beta Error) - CORRECT ANSWER - accepting the null when it is not true
(saying there isn't significance in your treatment when there is),
False negative
, How do you reduce Type 1 AND Type 2 errors? - CORRECT ANSWER - Increase sample
size
How do you reduce Type 1 errors but increase chance of Type 2 Errors? - CORRECT
ANSWER - decrease P levels
T-Test - CORRECT ANSWER - determines if a significant difference between two means
exist; t value statistic has to be higher than the t value in the table to be significant
Uses mean of 50 with each SD as 10 (Hence, a Z score of -1 would be a t-score of 40)
used for two samples to compare means, you obtain a single t score and compare it to the critical
t value based on the sample size and your significance level and if the t value you found is
greater than the critical t you have significance
for 2 groups
The values of a t-test are determined by degrees of freedom (influenced by sample size).
Analysis of variance
ANOVA - CORRECT ANSWER - F Statistic
used when there are 2 or more means to compare; if F obtained exceeds F in table, it is
significant;
one-way ANOVA = one independent variable (more than 1 level of one IV; e.g., 4 and 6 weeks of
assertiveness training);
two-way ANOVA = two independent variables, etc. (also called factorial analysis of variance
when 2+ IVs or MANOVA);
