Ch21Research

Question 1

What are the three most common distributions used for statistical tests?

Answer 1

t, F, and chi-squared distributions

Question 2

What are parametric tests?

Answer 2

based on specific assumptions about the distribution of populations; use sample statistics such as the mean, standard deviation, and variance to estimate differences between population parameters

Question 3

What are nonparametric tests?

Answer 3

use rank or frequency information to draw conclusions about differences between populations, not based on specific assumptions about the distributions of populations (parametric are often more powerful and preferred)

Question 4

What are the assumptions of parametric testing?

Answer 4

random selection, homogeneity of variance, measurement level of the data as interval or ratio

Question 5

When are data sets independent?

Answer 5

when values in one set tell nothing about values in another set

Question 6

When are data sets dependent?

Answer 6

when the sets of numbers consist of repeated measures on the same individuals, can be different individuals when they are matched for other factors

Question 7

What are the 10 steps in the statistical testing of differences?

Answer 7

1) state the null & alternative hypotheses in parametric terms 2) decide alpha level 3) determine independent or dependent samples 4) determine if parametric assumptions met 5) determine appropriate statistical test 6) calculate the statistic 7) determine degrees of freedom 8) determine probability of obtaining test statistic given the df 9) compare probability of obtaining test statistic with alpha 10) evaluate statistical conclusion in light of clinical knowledge

Question 8

What is assumed in the z distribution?

Answer 8

population standard deviation is known

Question 9

Why can z distributions not normally be used?

Answer 9

the population standard deviation is not normally known so researchers use distributions that resemble the normal distribution but altered to account for errors that are made when population parameters are estimated (t, F, and chi-square distributions)

Question 10

What distinguishes a t distribution from a z distribution?

Answer 10

greater proportion in the tails and lesser proportion in the center, spread to account for errors when population is estimated from sample statistics, becomes more similar to z distribution with larger sample sizes and increased degrees of freedom

Question 11

What is used to calculate degrees of freedom?

Answer 11

number of participants in study or number of levels of independent variable or both

Question 12

What distinguishes the F distribution?

Answer 12

squared t statistics, asymmetric and only positive values, actual shape depends on two different df: one with number of groups being compared and one associated with sample size

Question 13

What distinguishes the Chi-Square distribution?

Answer 13

squared z scores, shape varies with degrees of freedom

Question 14

What are two major classes of parametric tests?

Answer 14

t tests and ANOVAs

Question 15

What are two strategies for non-normal distributions?

Answer 15

convert the data mathematically to become more normally distributed, or use non-parametric test which does not require normal distribution

Question 16

What is the best strategy to ensure homogeneity of variance?

Answer 16

have equal or nearly equal sample sizes across groups (differences in variance become less of a concern when sample sizes of groups being compared are the same)

Question 17

Which type of data would be appropriate for non-parametric tests?

Answer 17

nominal and ranked ordinal data (interval and ratio data can be converted into ranks or grouped into categories as well)

Question 18

Which type of data is appropriate for parametric tests?

Answer 18

interval and ratio (need means and variances)

Question 19

Can parametric tests be used for ordinal data?

Answer 19

yes, as long as the distribution of ordinal data is approximately normal and parametric assumptions are met

Question 20

What tests are used for differences between two independent groups?

Answer 20

independent t test, Mann-Whitney or Wilcoxon rank sum test, chi-square test of association

Question 21

What is the independent t test?

Answer 21

ratio of differences between groups to the differences within groups, test statistic formula partitions the variability in data into explained and unexplained variability (unexplained is the variance within groups or the denominator)

Question 22

When variability explained by the IV is large, will the test statistic of the independent t test be large or small?

Answer 22

large

Question 23

What must be established about the hypothesis before conducting the independent t test?

Answer 23

differentiation between directional and non-directional hypothesis, directional is only justified if there is existing evidence of the direction of the effect

Question 24

What are the Mann-Whitney and Wilcoxon rank sum tests?

Answer 24

non-parametric alternatives to the independent t test, scores are ranked from the two groups regardless of group membership, used if assumptions of independent t test are violated

Question 25

What is the chi-square test of association?

Answer 25

concerned with proportion of patients with a particular score, convert raw score into categories i.e. “less than” or “greater than or equal to,” used to determine whether two groups had similar proportions of patients in categories

Question 26

What tests are used to determine differences between two or more independent groups?

Answer 26

one-way ANOVA, Kruskal-Wallis test, Chi-Square test of association

Question 27

What distinguishes a one-way ANOVA?

Answer 27

only one IV examined, partitions variability in a sample into between groups and within groups variability, F statistic generated (squared t), if no significant difference then test is done

Question 28

If omnibus F test tells us there is a significant difference between means, then what must be done next?

Answer 28

conduct a multiple-comparison test called a contrast, ex. include planned orthogonal contrasts, Newman-Keuls test, Tukey test, Bonferroni test, Scheffe test

Question 29

What is the Kruskal-Wallis test?

Answer 29

non-parametric equivalent of the one-way ANOVA, ranks the scores regardless of group membership, distribution of KW statistic approximates a chi-square distribution, use Mann-Whitney test (multiple comparison procedure) with a Bonferroni adjustment of alpha if KW test is significant

Question 30

What tests are used to determine the differences between two dependent samples?

Answer 30

paired t-tests, Wilcoxon signed rank test, McNemar test

Question 31

What is the paired t-test?

Answer 31

used to determine the difference between each pair of measurements, mean difference and SD of differences calculated, determine probability of given t statistic

Question 32

What is the Wilcoxon signed rank test?

Answer 32

non-parametric version of the paired t-test, if no difference then sum of positive and negative ranks should be approximately equal to sum of negative ranks

Question 33

What is the McNemar test?

Answer 33

nominal data analogue to paired t-test and Wilcoxon signed rank test, dependent samples version of the chi square test, can only be used to analyze 2x2 contingency tables so usefulness is limited

Question 34

What tests are used to determine differences between two or more dependent samples?

Answer 34

repeated measures ANOVA or Friedman’s ANOVA

Question 35

What is the repeated measures ANOVA?

Answer 35

extension of paired t-test to more than two dependent samples, between subject and within subject categories then generate F ratio, post hoc tests to determine which group is significantly different, if significance is found then some suggest using paired t-test with Bonferroni adjustment of alpha

Question 36

What is Freidman’s ANOVA?

Answer 36

non-parametric equivalent of the repeated measures ANOVA, calculation is based on rankings of the repeated measures for each participant, an appropriate non-parametric multiple comparison procedure is the Wilcoxon signed rank test with a Bonferroni adjustment of alpha level for each test