Research

Question 1

Hawthorne effect

Answer 1

Change in behavior that occurs when individuals know they are being observed by researchers or others

Question 2

Belmont effect or report (3 principles & 3 practices)

Answer 2

Report in ‘78/’79 as result of ethical violations in human subject/medical research:
3 core principles: respect for persons, beneficence, and justice
3 core practices: informed consent, assessment of risks and benefits, and selection of subjects

Question 3

Convergent validity

Answer 3

Correlating one test to another similar existing measure

Question 4

Concurrent validity

Answer 4

Correlating test to external measures (“here and now”)

Question 5

Content validity vs criterion validity

Answer 5

Content = test items properly represent what they're meant to test 
Criterion = relationship btw test scores and another standard (concurrent or predictive)

Question 6

Kuder-Richardson or coefficient alpha reliability

Answer 6

Statistical analysis of each item against all other items (all possible split half combinations). K-D for true/false and alpha for rating scales.

Question 7

Z score

Answer 7

Z = x - m / sd

Question 8

positively skewed curve–where are mean, median, mode?

Answer 8

positive skew=long tail positive;
mean highest,
median middle,
mode lowest

Question 9

Internal validity

Answer 9

Study measures what it says it’s going to measure (extraneous variables don’t interfere)

Question 10

External validity

Answer 10

Generalizability beyond the study

Question 11

Reliability

Answer 11

Repeatability of results.

Question 12

Type I (alpha) error

Answer 12

False rejection

Question 13

Type II (beta) error

Answer 13

False acceptance

Question 14

Validity vs reliability

Answer 14

If valid, must be reliable (repeatable). If reliable, does not necessarily mean valid.

Question 15

T score

Answer 15

mean = 50, SD = 10

Question 16

Rosenthal effect

Answer 16

higher expectations lead to increased performance

Question 17

ANOVA

Answer 17

Compares the mean of k groups based on one independent variable.
-tests the significance of group differences between two or more groups

-the IV has two or more categories
-only determines that there is a difference between groups, but doesn’t tell which is
different

ex: Do SAT scores differ for low-, middle-, and high-income students?

Question 18

chi-square

Answer 18

Determines if the observed frequencies are different from what we would expect to find.

ex: Is the distribution of sex and voting behavior due to chance or is there a difference between the sexes on voting behavior?

Question 19

independent sample t-test

Answer 19

Compares the mean difference between two independent groups on a given variable

-the IV must have only two groups (male/female, undergrad/grad)

Do males and females differ in the amount of hours they spend shopping in a given
month?

Question 20

ANCOVA

Answer 20

same as ANOVA, but adds control of one or more covariates that may influence the DV

ex: Do SAT scores differ for low-, middle-, and high-income students after controlling for
single/dual parenting?

Question 21

MANOVA

Answer 21

same as ANOVA, but you can study two or more related DVs while controlling for the
correlation between the DV

-if the DVs are not correlated, then separate ANOVAs are appropriate

ex: Does ethnicity affect reading achievement, math achievement, and overall scholastic
achievement among 6 graders?

Question 22

MANCOVA

Answer 22

same as MANOVA, but adds control of one or more covariates that may influence the
DV

ex: Does ethnicity affect reading achievement, math achievement, and overall scholastic
achievement among 6 graders after controlling for social class?

Question 23

correlation coefficient

Answer 23

r
.55 strong correlation
.3-.55 moderate

Question 24

coefficient of determination

Answer 24

r2
denotes the strength of the linear association between x and y

For example, if r = 0.922, then r 2 = 0.850, which means that 85% of the total variation in y can be explained by the linear relationship between x and y (as described by the regression equation).  The other 15% of the total variation in y remains unexplained.    The coefficient of determination is a measure of how well the regression line represents the data.  If the regression line passes exactly through every point on the scatter plot, it would be able to explain all of the variation. The further the line is away from the points, the less it is able to explain.