Q1 behavioral variability

Question 1

variability

Answer 1

the degree to which scores in a data set vary or differ from one another

Question 2

how do we distinguish between individual differences and behavioral variability? what factors account for or explain variability?

Answer 2

research design, measurement of variable, and statistical analyses

Question 3

how does research design help us parse variability?

Answer 3

we can identify the source of variability using experiments or non-experimental designs (correlational, longitudinal, cross-sectional)

Question 4

how does the measurement of a variable help us parse variability?

Answer 4

to correctly parse variability the measurement of a variable must be appropriate and quantifiable

Question 5

how do statistical analyses help us parse variability?

Answer 5

they summarize and answer questions about observed variability
descriptive stats give us averages, percentages, and frequencies which summarize and simplify our data
inferential statistics allow us to make assumptions or generalizations to a wider population; they also allow us to directly test hypotheses (is the relationship meaningful or significant?)

Question 6

what are measures of central tendency? (definition)

Answer 6

they help us to see where the general distribution lies in our data set

Question 7

what are the three measures of central tendency?

Answer 7

1. mean
2. median
3. mode

Question 8

what information do measures of variability give us?

Answer 8

they tell us how loosely or tightly packed scores are around the mean

Question 9

what are 3 different ways we can measure variability? (2/3 are kinda the same)

Answer 9

1. range
2. standard deviation
3. variance

Question 10

range

Answer 10

max - min values (but we miss out on the middle values)

Question 11

variance and standard deviation

Answer 11

subtract every score from the mean and square each value to get rid of negative values; sum of squares then divide by N - 1 (and take the square root to get SD)

Question 12

what are the two types of variance?

Answer 12

1. systematic
2. error

Question 13

systematic variance

Answer 13

variance that occurs in a predictable and orderly fashion; aka your variable of interest
how much variability in y is systematically explained by variable x?

Question 14

error variance

Answer 14

variability not accounted for by the explanatory variable
variability in y that is unrelated to variable x
this comes from factors that we fail to measure in the study

Question 15

what is the problem with having high error variance?

Answer 15

we decrease our understanding of the full picture or the systematic variance that we are looking for (more noise/static); makes it harder to detect a true effect

Question 16

how do we parse out variance to reveal systematicity?

Answer 16

using regression, t-tests, ANOVA, etc.

Question 17

what do p values tell us?

Answer 17

whether there is a significant difference or relation between two variables or groups

Question 18

what do effect sizes tell us?

Answer 18

effect sizes measure the magnitude or strength of an observed difference or the strength of the association between variables of interest
is the difference between groups or the relation between two variables impactful or meaningful?

Question 19

do effect sizes help us decide whether to reject the null hypothesis?

Answer 19

no

Question 20

R^2

Answer 20

used for correlational/regression designs
if R^2 = 0.2, then 20% of variance in variable y is explained by variable x (80% is unaccounted for)

Question 21

n^2

Answer 21

proportion of variance, ANOVA

Question 22

cohen’s d

Answer 22

mean differences, t-tests

Question 23

which effect size is used in correlational studies?

Answer 23

R^2, pearson’s correlation coefficient

Question 24

which effect size is used in tests of the mean difference between two groups (t-tests)?

Answer 24

cohen’s d

Question 25

which effect size is used in tests of means of multiple groups (ANOVA)?

Answer 25

n^2

Question 26

what are small, medium, and large effects for R^2?

Answer 26

small = 0.1
medium = 0.5
large = 0.8

Question 27

what are small, medium, and large effects for cohen’s d?

Answer 27

small = 0.2
medium = 0.5
large = 0.8

Question 28

what are small, medium, and large effects for n^2?

Answer 28

small = 0.01
medium = 0.06
large = 0.14

Question 29

what is good about effect sizes as compared to p values?

Answer 29

they are a common index, meaning they use the proportion of total variance that is systematic variance to determine the magnitude of the effect between variables

Question 30

why are meta-analyses helpful when determining effect?

Answer 30

we can determine the magnitude of a phenomenon by averaging the effect sizes across studies

Question 31

how can we reduce error variance? (2)

Answer 31

1. identify and control for potential confounding variables
2. use reliable and valid measures