N701 Research - Final

Front 1

What are the 2 types of statistics?

Back 1

Descriptive
Inferential

Front 2

Define Descriptive statistics.

Back 2

Allows us to 'describe.' Involves:
measure of central tendencies
measure of variability
crosstabs/Chi Square
Correlations

Front 3

What are the measures of central tendencies? Define each.

Back 3

Mean - average

median - the 'middle' element when the data is lined up in order of magnitude

mode - the data element that occurs most frequently

Front 4

What is the best measure of central tendency?

Back 4

mean

Front 5

What are the Levels of Data? Define them.

Back 5

nominal - has no order; only gives names or labels to categories; e.g. sex being male or female
ordinal - has order but the interval b/w measurements has no meaning; e.g. Likert scale
interval - meaningful intervals b/w measurements but no true zero
ratio - highest level of measurement; has a starting point (zero) with meaningful intervals b/w measurements

Front 6

What is probability?

Back 6

the chance that a particular event or outcome will occur

values range from 0 to 1
percentages range from 0 to 100%

Front 7

What is standard deviation (SD)?

Back 7

a descriptive statistic measuring the degree of variability in a set of scores

square root of the variance

Front 8

Define Standard Error of the Mean (SEM)?

Back 8

SEM = SD/square root of N

where N=sample size

when SEM is large there is more variability in the sample means

Front 9

What is a Z-score?

Back 9

the number of SDs a score of interest lies from the mean

Z = (x-xbar)/SD
x=score
xbar=mean

Front 10

What does p<.05 mean?

Back 10

5 times out of 100 the difference is due to chance

it is a significance level

Front 11

What is a type I error?

Back 11

known as alpha (usually .05)

reject the null hypothesis when it is true

it is a false positive

Front 12

What is a type II error?

Back 12

beta (usually .2)
accept the null hypothesis when it is false

it is a false negative

Front 13

What are inferential statistics & give types of tests used.

Back 13

explores the relationship b/w variables & the generalizability from sample to population

tests:
t-tests
ANOVA & ANCOVA
Linear & logistic regression

Front 14

characteristics of a positively skewed distribution

Back 14

mean>median>mode

Front 15

characteristics of a negatively skewed distribution

Back 15

mode>median>mean

Front 16

define parametric tests

Back 16

DV is I/R
IV is nominal or I/R
uses T-test, ANOVA, ANCOVA or regression

Front 17

define non-parametric tests

Back 17

Measurement level of variables are nominal or ordinal

uses Chi-Square Test or crosstabs

Front 18

True or False

In a normal distribution, the mean, median, and mode are equal.

Back 18

True

Front 19

What is variability?

Back 19

measures how dispursed the scores are in a distribution

not a reliable measurement as extreme scores can distort the range

Front 20

What is variance?

Back 20

average of the squared differences of the mean

Front 21

what is standard deviation?

Back 21

the square root of the variance (where variance is the average of the squared differences from the mean)

SD is a descriptive statistic that measures the degree of variability in a set of scores

Front 22

What is 1SD from the mean?

Back 22

34%

if you want the range + or - 1, then it's 68%

Front 23

What is 2SD from the mean?

Back 23

47.5%

if you want the range + or - 1, then it's 95%

Front 24

What is 3SD from the mean?

Back 24

98%

if you want the range + or - 1, then it's 49%

Front 25

What level of data are these:

weight
sex: male/female
Likert scale

Back 25

weight - ratio
sex: male/female - nominal
Likert scale - ordinal

Front 26

When do we use the Chi Square test? What kind of data does it use?

Back 26

We use Chi Squared when we want to know if the expected number differs significantly from the observed number.

uses nominal or ordinal data

Front 27

Characteristics of Chi squared

Back 27

non-parametric test that measures if the expected number differs significantly from the observed
char. by one parameter called degrees of freedom
measures are independent of each other
always positive

Front 28

Define simple correlation

Back 28

Pearson's R

relationship between two variables

ranges between -1 and +1

the absolute value of the coefficient reflects the strength of the correlation

Front 29

What does r=.8 mean?

Back 29

strong positive correlation

Front 30

What does r=.1 mean?

Back 30

nothing or very weak correlation

Front 31

What is shared variance?

Back 31

the square of the correlation coefficient

it is a measure of the amount of variance shared by two variables

eg r=.20, so the shared variance is r squared, which = .04 or 4%

another way to say it is the IV accounts for 4% of the variance of the DV

Front 32

assumptions of correlations

Back 32

can be calculated with all levels of data
sample must represent the population
relationship of X & Y must be linear
assume homoscedasticity

Front 33

What is homoscedasticity?

Back 33

variance homogeneity

for every value of X, the distribution of Y scores must have approximately equal variability

Front 34

What are the Rules of Thumb for strength of correlations?

e.g. 0.4-0.6 means what?

Back 34

0.8 to 1.0 (very strong relationship)
0.6 to 0.8 (strong relationship)
0.4 to 0.6 (moderate relationship)
0.2 to 0.4 (weak relationship)
0.0 to 0.2 No to weak relationship)

Front 35

Which group of people are we talking about when we use the following terms?

statistic
parameter

Back 35

statistic ---> sample
parameter ---> population

Front 36

Define non-parametric.

Back 36

categorical or non-normal data
Measurement level of variables are nominal or ordinal
Chi-Square Test
Crosstabs

Front 37

Define parametric.

Back 37

based on normality of data

DV is I/R
IV is nominal or I/R
T-test, ANOVA and ANCOVA
Regression

Front 38

If your correlation statistic is -.702, what is the strength of the correlation?

Back 38

strong negative correlation

Front 39

How much variance is shared between 2 variables with an r= .702?

Back 39

r2 = .702 *.702 = .493 or 49.3%

so the IV accounts for 49.3% of the variance of the DV, OR the variance shared between the 2 variables is 49.3%

Front 40

Z=2. What does this mean?

Back 40

This is a Z-score, the number of SDs the score lies away from the mean.

Z=2 means the score of interest is 2SD away from the mean

Front 41

another name for significance.

Back 41

P-value

Front 42

What type of error is this or is it the correct decision?

The null hypothesis is true but the test rejects it

Back 42

Type I error

you are rejecting the null hypothesis when it's true (false negative)

Front 43

What type of error is this or is it the correct decision?

The null hypothesis is false but the test accepts it

Back 43

Type II error

you are accepting the null hypothesis when it's false (false negative)

Front 44

Why use a T-test? What are some characteristics of it?

Back 44

to find the difference between 2 means in 2 groups

IV is nominal
DV (or outcome) is continuous (ordinal, I/R)
assume 2 mutually exclusive groups
equal variances are assumed

Front 45

What is the purpose of ANOVA?

Back 45

to find if a difference between the means of 2 or more groups exists

Front 46

What is the purpose of a Post-Hoc test?

Back 46

to define the difference between groups

Front 47

How many degrees of freedom in a T-test with 24 people in each group?

Back 47

46

df = (24 people x 2 groups) - 2

Front 48

For a T-test, what is the scale of measurement for the dependent variable?

Back 48

ordinal or I/R --> treated as continuous

Front 49

in a T-test, are the distributions of the 2 groups homogeneous?

Back 49

yes, homogeneity of variance

Front 50

what are the 2 types of T-tests?

Back 50

independent - 2 independent groups that are mutually exclusive

paired - one group with 2 measures over time

Front 51

A Levene's test has the following results:
F=1.153
Sig.=.286

What does this mean?

Back 51

the results are not significant, there is no difference between the groups therefore we assume equal variances.

Front 52

A Levene's test has the following results:
F=6.228
Sig.=.002

What does this mean?

Back 52

significant results, there are differences between the groups therefore equal variances can not be assumed

Front 53

In an ANOVA test, what type of data is used for the IV?

Back 53

nominal (categorical)

Front 54

In an ANOVA test, what type of data is used for the DV?

Back 54

ordinal, I/R (continuous)

Front 55

What is the difference between a One-way and a Two-way ANOVA?

Back 55

A one-way ANOVA has one IV and one DV.

A two way has two IV and one DV

Front 56

What is the purpose of a T-test?

Back 56

It assesses the difference between the means of 2 groups.

Front 57

In a T-test, what kind of variable is the IV? the DV?

Back 57

IV is nominal

DV is ordinal, interval or ratio (continuous)

Front 58

Name and define the 2 types of T-tests.

Back 58

independent T-test - looking at the difference in means of 2 mutually exclusive groups

paired T-test - looking at the difference of the means of one group with 2 measures over time. (e.g. pre & post test)

Front 59

What are the assumptions of a T-test?

Back 59

IV is categorical (nominal)
2 mutually exclusive groups
normal distribution of the DV
homogeneity of variance (equal variances are assumed, i.e. Levene's test)

Front 60

Define degrees of freedom.

Back 60

the number of categories or classes being tested minus 1

Front 61

What is the purpose of ANOVA?

Back 61

tests the difference of means among more than 2 groups

Front 62

What level of data is the IV in an ANOVA study? DV?

Back 62

IV is nominal

DV is continuous (I/R)

Front 63

What are the 'assumptions' of an ANOVA?

Back 63

same as the T-test

groups are mutually exclusive
normal distribution of DV
homogeneity of variance.

Front 64

Define mean square.

Back 64

the average amount of variance per degree of freedom

Front 65

The F-statistic correlates with what test? The t-statistic correlates with what test?

Back 65

F-statistic is the result of ANOVA

t-statistic is the result of a T-test

Front 66

What does the F-statistic mean in an ANOVA?

Back 66

in order to reject the null hypothesis, the F-statistic must meet or exceed the critical value (that is looked up on a chart in the back of Munro)

Front 67

True or False

The F-statistic in an ANOVA can either be positive or negative.

Back 67

FALSE

The F-statistic is always positive b/c we are only testing to see if there is a difference b/w groups, not the direction of the difference.

Front 68

True or False

the ANOVA produces only one critical F-value and it is always positive.

Back 68

TRUE

Front 69

True or False

An ANOVA tells you there are differences b/w groups and where they are.

Back 69

FALSE

ANOVA only tells you there are differences. A post-hoc test tells you where those differences are.

Front 70

How is F derived in an ANOVA?

Back 70

F = MS(between)/MS(within)

where MS=mean square

Front 71

What is an ANCOVA test?

Back 71

it is an ANOVA but control for extraneous variable.

Front 72

What are the assumptions of an ANCOVA?

Back 72

the same as an ANOVA:
groups should be mutually exclusive
homogeneity of variances
DV should be normally distributed
the covariate should be continuous (age)
2 additional assumptions are:
the covariate & DV must show a linear relationship
directrion & strength of the relationship b/w DV and covariate must be similar in each group-->homogeneity of regression

Front 73

What are the types of regression?

Back 73

simple - one IV used to predict a DV

multiple - multiple IVs used to predict a DV

Logistic - used when DV is categorical (nominal data) in nature

Front 74

Define the components of this equation:
Y = B0 + B1X1 + E

Back 74

Y = the predicted score or the DV (outcome)

B0 = constant (Y-intercept on a line graph)

B1 = regression coefficient, representing the amt Y changes when the IV (x) changes by one unit

E = error

Front 75

When do we use regression?

Back 75

It's used to make predictions:

when the relationship b/w 2 variable is perfectly linear, knowledge of value of one variable allows you to predict the value of another variable with accuracy

Front 76

The regression line is also known as what?

Back 76

The "line of best fit."

Front 77

True or False

Regression is the line of best fit therefore it can curve through the scatterplot to create the best fit.

Back 77

FALSE

It is the best linear representation of the data

Front 78

Characteristics of the regression line.

Back 78

It is the line of best fit.
The line passes through the exact center of the data on a scatterplot.
The distance b/w the point (value) & the line is the easurement error
The regression line is the best line with the least amount of error.

Front 79

True or False

The regression line is the best line with the least amount of error.

Back 79

TRUE

Front 80

In a regression, define the following:
R squared
F-statistic
T-statistic

Back 80

R squared is shared variance (it is the correlation coefficient squared)
F-statistic is the same as ANOVA, the overall significance of the model.
T-statistic is the significance of each IV

Front 81

In a regression equation, what does the y-intercept represent? the Y?

Back 81

Y-intercept is the constant

Y is what you are solving for, the DV or the outcome

Front 82

What is the purpose of Logit regression?

Back 82

It uses MLE (maximum likelihood estimation) to transform the probability of an event occurring into it's odds

Front 83

Define Odds Ratio.

Back 83

ratio of 2 probabilities; the probability of the event occurring versus the probability that it will not occur

Front 84

What is a cohort study?
What is a case control study?

Back 84

both are epidemiological studies.
The cohort studies look at relative risk, and work from treatment to outcome.

The case control studies look at the odds ratio and work from outcome to treatment.

Front 85

Which study does not obtain relative risk directly, cohort or case control studies?

Back 85

case control studies

case control studies obtain the odds ratio which is then used to estimate the relative risk. It tends to overestimate it.

Front 86

True or False

Logistic regression is only used case control studies to produce an odds ratio.

Back 86

FALSE

It is used in both cohort and case control studies. It produces an odds ratio which is often interpreted as relative risk.

Front 87

Define odds.

Back 87

another way of presenting probability

the probability of occurrence over the probability of non-occurrence

Front 88

Define odds ratio

Back 88

comparing the odds of 2 groups

e.g. the odds of rolling a six if female

group 1 - odds of rolling a 6
group 2 - odds of being female

Front 89

Define probability, odds, and odds ratio.

Back 89

probability - measure of likelihood of an event happening

odds - the probability of occurrence over the probability of non-occurrence

odds ratio - comparing the odds of 2 groups

Front 90

Which study uses odds ratio, cohort or case control?

Back 90

case control

Front 91

why use odds ratio?

Back 91

provides an estimate for the relationship b/w a binary variable (1 & O or nominal data)

Front 92

What is relative risk?

Back 92

the risk given one condition versus the risk given the other condition.

A more direct method of calculating 'odds' (for lack of a better word)

Front 93

True or False

the odds ratio is an accurate estimate of relative risk.

Back 93

FALSE

it is at least equivalent to but often overestimates relative risk.

Front 94

Interpret this.
OR= 2.53, 95% CI: 1.66 - 3.55

Back 94

positive (95% CI: 1.66 - 3.55)

two and a half times more likely to have the outcome (significant b/c CI doesn't include 1.00, therefore equal odds).

Front 95

Interpret this.
OR= 0.60, 95% CI: 0.26 - 0.92

Back 95

negative (95% CI: 0.26 - 0.92)

40% less likely to have the outcome (1 - 0.60).

significant b/c CI doesn't include 1.00, therefore equal odds.

Front 96

In logit, what does the Hosmer and Lemeshow Test tell us?

Back 96

That is the 'goodness of fit' test. If the test is not significant (p>.05), then the data fits the model.

Front 97

True or False

A raw score tends to overestimate an adjusted score.

Back 97

TRUE

Front 98

What is ethnography?

Back 98

study of culture

Describes & analyzes aspects of ways of life of a particular culture, subculture, or subculture groups

Front 99

4 types of qualitative research designs.

Back 99

phenomenology
grounded theory
ethnography
historical research

Front 100

What is phenomenology?

Back 100

to describe the 'lived experience' of study participants

Front 101

What is grounded theory?

Back 101

how people deal with a phenomenon over time

explores social processes with the goal of developing a theory

Front 102

What is historical research?

Back 102

examines event of the past