Critiquing research findings

Question 1

descriptive statistics

Answer 1

describe/synthesize data about the sample and the study variables

• frequency distribution
• measures of central tendency (mode/mean/median)
• measures of dispersion (range/variance/SD)

Question 2

inferential statistics

Answer 2

make inferences about population based on sample data

test hypotheses

answer research questions

Question 3

parametric statistics

Answer 3

a class of statistical tests that involve assumptions about the distribution of the variables and the estimation of a parameter

data are NORMALLY DISTRIBUTED

Question 4

nonparametric statistics

Answer 4

a class of statistical tests that do NOT involve stringent assumptions about the distribution of critical variables

data are NOT NORMALLY DISTRIBUTED

Question 5

null hypothesis

Answer 5

rejected if relationship is statistically significant (p < 0.05)

accepted if relationship is NOT statistically significant (p = 0.05 or greater)

Question 6

p-value

Answer 6

probability of rejecting the null hypothesis when the null is actually true

typically, p<0.05 is real effect

Question 7

confidence interval (CI)

Answer 7

range of values within which a population parameter is estimated to lie, at a specified probability (eg 95% CI)

Question 8

confidence limit

Answer 8

upper/lower boundary of a CI

Question 9

correlation statistics

Answer 9

• indicate direction and magnitude of relationship between 2 variables
• used with ordinal/interval/ratio measures
• can be shown graphically (scatter plot)
• correlation coefficient can be computed
• with multiple variables, a correlation matrix can be displayed

Question 10

bivariate correlation

Answer 10

2 variables

• Pearson’s r, a parametric test (lowercase “r” indicates a correlation b/w 2 variables)
• tests that the relationship b/w 2 variables is not zero
• used when measures are on an interval/ratio scale (continuous level data)

Question 11

strengths of relationships

Answer 11

weak: 0.00~0.30 (+ or -)
moderate: 0.30~0.50 (+ or -)
strong: >0.50 (+ or -)

Question 12

nonparametric alternatives to bivariate correlation analysis

Answer 12

• Spearman’s rank-order correlation coefficient: measures association b/w ordinal-level variables
• Kendall’s tau: measures association b/w ordinal-level variables
• Cramer’s V: measures association b/w nominal-level variables

Question 13

factor analysis

Answer 13

• examines interrelationships among large #s of variables to reduce them to a smaller set of variables
• IDs clusters of variables that are most closely linked together
• typically used to assist with validity of a new measurement method or scale

Question 14

simple linear regression

Answer 14

• provides a means to estimate the value of a dependent (outcome) variable based on the value of an independent variable (predictor)
• outcome variable is continuous (interval/ratio-level data)
• predictor variables are continuous or dichotomous (dummy variables)
• change in Y given a one unit change in X

Question 15

multiple linear regression

Answer 15

• predicts a dependent variable based on 2+ independent variables (predictor)
• dependent variable is continuous (interval/ratio-level data)
• predictor variables are continuous or dichotomous (dummy variables)

Question 16

simultaneous multiple regression

Answer 16

enters all predictor variables into regression equation at the same time

Question 17

stepwise multiple regression

Answer 17

enters predictors in a series of empirically determined steps, in the order that produces the greatest increment to R^2

Question 18

hierarchical multiple regression

Answer 18

enters predictors into equation in a series of steps, controlled by researcher

Question 19

multiple correlation coefficient (R)

Answer 19

• is the correlation index for a dependent variable and 2+ independent variables
• does not have negative values: shows strength of relationship, but not direction
• can be squared (R^2) to estimate proportion of variability in dependent variable accounted for by independent variables
• can’t be less than highest bivariate correlation b/w the dependent v and an independent v

Question 20

power analysis

Answer 20

• method of reducing risk of Type II errors and estimating their occurrence
• if power = .80, risk of Type II error is 20%
• estimates how large a sample is needed to reliably test hypotheses

4 components:

• significance criterion (α)
• sample size (N)
• population effect size (γ): magnitude of relationship b/w research variables
• power: probability of obtaining a significant result (1 - β)
• generally need 20-30 subjects per independent v
• <10 subjects per independent variable leads to serious error

Question 21

odds

Answer 21

based on probabilities

probably of occurrence/probability of nonoccurrence

Question 22

odds ratio (OR)

Answer 22

ratio of odds for the treated vs. untreated group, with the odds reflecting the proportion of people with the adverse outcome relative to those without it

Question 23

dichotomous outcome variable

Answer 23

mean of dependent (outcome) variable will be between 0-1

OR = 1 probability of event is same for both groups
OR >1 probability is higher for subjects exposed
OR <1 probability of event is lower among subjects exposed

(slides have more interpretations)

Question 24

commonly used parametric stats

Answer 24

student’s t-test

analysis of variance (ANOVA)

Question 25

commonly used nonparametric stats

Answer 25

Mann-Whitney U test: analogous to parametric t-test for independent groups for group comparisons (2 groups)

Kruskal Wallis test: analogous to parametric ANOVA (more than 2 groups)

Question 26

t-test

Answer 26

testing for significant differences b/w group means of 2 samples

• for independent groups (b/w subjects): examines differences b/w 2 independent groups
• for dependent groups (paired/within subjects): matched or paired groups - comparison of pretest and posttest measurements

Question 27

ANOVA

Answer 27

• compares data b/w 2+ groups/conditions to investigate the presence of differences b/w those groups
• the outcome or dependent variable must be continuous (interval/ratio)
• independent (predictor) variable is nominal
• F test: typically accompanied by an ANOVA
• doesn’t say which groups differ from one another, just that they’re different

Question 28

chi-square test

Answer 28

nonparametric test

compares differences in proportions of nominal level variables

compares actual/observed frequency with expected frequency

Question 29

Cohen’s effect sizes for the magnitude of tx effect

Answer 29

small = 0.2
moderate = 0.5
large = 0.8

Question 30

power of a test

Answer 30

probability of detecting a difference or relationship if such a difference or relationship really exists

anything that dec probability of a Type II error inc power, vice versa

a more powerful test is one that’s likely to reject H0

Question 31

statistical significance

Answer 31

these results say nothing about clinical importance or meaningful significance of results

researcher must always determine if statistically significant results are substantively meaningful

Question 32

post-hoc analyses

Answer 32

performed in studies w/ more than 2 groups when analysis indicates that groups are significantly different but doesn’t indicate which groups are different

developed specifically to determine location of group difference after ANOVA is performed

• Scheffe
• Bonferroni
• Least Significant Difference (LSD) Test
• Tukey’s Honestly Significant Difference (HSD)
• Student Newman-Keuls
• Tukey’s Wholly Significant Difference (WSD)