Chapter 14: Inferential Data Analysis

Front 1

What kind of statistics does chapter 14 focus on?

Back 1

inferential statistics

Front 2

define inferential statistics

Back 2

statistics used in the process of making inference from a sample to a population.

Front 3

differences between techniques being tested may be due to what?

Back 3

real difference or sampling error

Front 4

define sampling error

Back 4

difference among group means because the samples are not 100 percent representative of a population; the extent to which sample values (statistics) deviate from those that would be obtained from the entire population (parameter).

Front 5

is it reasonable to conclude the differences are real and the treatments are not equally effective if the means (rope-skipping example) are large?

Back 5

yes, but if the differences among means are small, it could be sampling error.

Front 6

what are the steps in the hypothesis-testing procedure?

Back 6

1. state hypothesis

2. select the probability level

3. consult the statistical table

4. conduct the statistical test

5. accept or reject the null hypothesis

Front 7

define statistical hypothesis?

Back 7

hypothesis tested with a statistical test. (null hypothesis). Ho

Front 8

statistical or null hypothesis states what?

Back 8

there is no difference (in treatments, measurements, etc.) Statistical hypothesis does not have to agree with the research hypothesis.

Front 9

define research hypothesis

Back 9

a tentative explanation or prediction of the eventual outcome of a research problem; normally this is the outcome expected by the investigator.

Front 10

define alpha level

Back 10

probability level selected that warrants rejection of the null hypothesis.

Front 11

the difference between the population statement in the null hypothesis and what is found in the samples is due to what?

Back 11

null hypothesis being rejected or sampling error.

Front 12

what does the statistical test during step 4 of the hypothesis test procedure determine?

Back 12

provides the probability of the sample finding occurring if the null hypothesis is true.

Front 13

if probability is small....

Back 13

...the null hypothesis is rejected and the difference is considered to be real. Otherwise, the null hypothesis is accepted and the difference is considered to be due to sampling.

Front 14

what is the purpose of step 2 of the hypothesis testing procedure?

Back 14

to select the probability level that warrants rejection of the null hypothesis. This probability level is called the alpha level and is usually .05 or .01

Front 15

in step three what happens if the p value is less than or equal to the alpha level?

Back 15

the null hypothesis is rejected.

Front 16

how is the p value determined?

Back 16

consult statistical table to fine the value that that the statistical test of the sample would need to equal or exceed in order to reject the null hypothesis at the chosen alpha level.

Front 17

what is purpose of step 4 in the hypothesis-testing procedure?

Back 17

statistical test is calculated either by hand or computer.

Front 18

what is the purpose of step 5 in the hypothesis-testing procedure?

Back 18

this is the final step, where the decision is made either to accept or to reject the null hypothesis.

Front 19

in step 5 what happens if the value of the statistical test is greater than the table value or if the p value for the value of the statistical test is less than the alpha level?

Back 19

null hypothesis is rejected

Front 20

does accepting the null hypothesis prove it is true?

Back 20

no...only that it is plausible.

Front 21

what does it mean when literature says a statistical test was significant?

Back 21

indicates the value of the statistical test warranted rejection of the null hypothesis and there is a real difference between sample not due to sampling error.

Front 22

what does it mean when literature says a statistical test nonsignificant?

Back 22

indicates that the null hypothesis was retained, and any differences were attributed to sampling error.

Front 23

t test and analysis of variance (ANOVA) generally applied in what types of research?

Back 23

experimental

Front 24

chi-square test test generally applied in what types of research?

Back 24

descriptive

Front 25

define one-group t test

Back 25

a statistical test used with one sample.

Front 26

define degrees of freedom

Back 26

a value associated with a statistical test which is used when finding a table value of the statistical test. DOF = (n-1)

Front 27

define critical value

Back 27

the tabled value of the statistical test needed to reject the null hypothesis.

Front 28

define critical region

Back 28

the region at which all values of the statistical test are at or beyond the critical value.

Front 29

define two independent groups t test.

Back 29

a statistical test used with two independent samples. Used when either two samples are drawn from the same population and each administered a different treatment or when the sample is drawn from each of two populations.

Front 30

two dependent groups t test

Back 30

a statistical test used with two dependent groups or columns of scores.

Front 31

define repeated measures

Back 31

a two dependent groups t-test design where participants are tested before and after a treatment.

Front 32

what is the purpose of a repeated measures design?

Back 32

to determine if an experimental treatment is effective. (test, treatment, re-test).

Front 33

what is an alternative to the two independent variable design?

Back 33

matched pair design. (individuals closely matched in the distributed to two different groups).

Front 34

what is a problem with match paired method?

Back 34

loss of participants. When one member of a pair is lost from the experiment, the other member was be removed from the study.

Front 35

why are two alternate hypotheses normally chosen?

Back 35

because the null hypothesis and the two alternated hypothesis cover all possibilities.

Front 36

the number of alternate hypothesis determines what?

Back 36

whether the statistical test is one-tailed or two-tailed. The smaller the table value, the easier it is to reject the null hypothesis.

Front 37

what does a smaller alpha level mean (.01 compared to .05)

Back 37

makes it more difficult to reject the null hypothesis. The smaller the alpha level, the larger the difference must be between what is hypothesized at the population level and what was found at the sample level.

Front 38

define type 1 error

Back 38

rejection of a true null hypothesis

Front 39

define type 2 error

Back 39

acceptance or non-rejection of a false null hypothesis. probability of making a type 2 error is symbolized by beta.

Front 40

what effects the probability of type 2 error.

Back 40

1. larger alpha level = smaller probability

2. larger sample size = smaller probability

3. more false the null hypothesis = less probability

Front 41

define statistical power

Back 41

probability of not making a type 2 error (1-B)

Front 42

what factors give more statistical power?

Back 42

1. one-tailed test over two-tailed test

2. 2 dep group over 2 indep group design

everything that decreases probability of type 2 error increases statistical power.

Front 43

define analysis of variance (ANOVA)

Back 43

a statistical test with two or more groups

Front 44

when is an ANOVA used?

Back 44

when a research problem or question involves more than two treatment groups. ANOVA is used to analyze the data. Can also be used when there are just two treatment groups in place of a t test.

total variability in a set of scores is divided into two or more components or sums of squares (SS)

Front 45

define sums of squares

Back 45

variability values in ANOVA

Front 46

define square (MS) value

Back 46

values used in ANOVA to calculate an F statistic.

Front 47

what is an F statistic

Back 47

the ratio of two mean square values, which is used to test the null hypothesis. F test is conducted at step 4 of five-step hypothesis testing procedure.

Front 48

define one-way ANOVA

Back 48

a statistical analysis used with two or more independent groups. An extension of the two independent groups design.

Front 49

define repeated measures ANOVA

Back 49

a statistical analysis where each participant is measured on two or more occasions. If only two measures for each participant, this is an alternate test to t test for dependent groups design.

Front 50

what is the best way to conduct a repeated measures ANOVA with SPSS

Back 50

with the reliability program

Front 51

define random blocks ANOVA

Back 51

a statistical analysis where participants similar in terms of a variable are placed together in a block and then randomly assigned to treatment groups. extension of matched-pair t test when there are three or more groups.

Front 52

what is Maucly's Test of Sphericity?

Back 52

the results of the Mauchly test determines whether a univariate test or multivariate test should be used for the repeated-measures ANOVA. If the approximate chi-square value is not significant, then the univariate test should be used. If chi-square value is significant multivariate test should be used.

Front 53

a "pair" in matched pair t test is to _________ in a random blocks ANOVA

Back 53

block. The matched pairs in a random blocks ANOVA are place together in a block.

Front 54

true or false: the analysis for the random blocks design is the same as the analysis for the repeated measures ANOVA design.

Back 54

true

Front 55

define two-way ANOVA

Back 55

an ANOVA design with rows and columns. Also called two-dimensional ANOVA

have multiple scores within one cell compared to only one score in a cell with one-way ANOVA

Front 56

examples of two-way ANOVA

Back 56

1. random blocks design

2. factorial design

3. factorial design with rows as levels of one treatment, columns are levels of second treatment and there are multiple participants in each cell.

Front 57

define factorial design

Back 57

a two-way ANOVA design with rows representing a classification or treatment and columns representing a treatment.

Front 58

simple effect test

Back 58

test to compare the column means for each row or the row means for each column in a two-way ANOVA design.

Front 59

two-way ANOVA is also called what?

Back 59

factorial analysis

Front 60

what are the assumptions underlying statistical tests?

Back 60

1. scores are at least interval.

2. random sampling of participants

3. for t test for two independent groups, one-way ANOVA and factorial ANOVA it is assumed that groups are independent of each other

4. assumed that scores are normally distributed at the population level (30 participants is normally the minimum)

5. in studies using multiple samples, populations represented are assumed to be equally variable

Front 61

what is effect size (ES)

Back 61

it is a way to decide whether the statistically significant difference is a practically significant difference. Cohen ES less than .20 is small, around .50 is medium, and greater than .80 is large

Front 62

how do you calculate ES?

Back 62

mean score A - mean score B/SD for one group or pooled SD for combined groups.

Front 63

define two-group comparisons

Back 63

statistical techniques to compare groups two at a time following a significant F in ANOVA. Also called multiple comparisons and posteriori comparisons. Used to determine if two groups differ significantly.

Front 64

list a few two-group comparison techniques

Back 64

1. Scheffe

2. Tukey

3. Duncan

4. Bonferroni

Front 65

define per-comparison error rate

Back 65

probability of making a type 1 error in a single two-group comparison

Front 66

define experiment-wise error rate

Back 66

probability of making a type 1 error somewhere in all the two-group comparisons conducted.

Front 67

what is the ANCOVA?

Back 67

analysis of covariance

alternative to most ANOVA designs. statistically adjusts the difference among the group means on the criterion variable to allow for the fac that groups differ in mean score on some other variables and then applies ANOVA to the adjusted criterion.

reduces error variance (reflected in the denominator of the F test).

Front 68

define parametric statistic

Back 68

statistic that requires interval data and a normal distribution.

Front 69

define nonparamtric statistic

Back 69

statistic that has no requirement of interval data and normal distribution

Front 70

define parametric test

Back 70

statistical tests that assume interval data and normal distribution scores.

Front 71

define nonparamtric tests

Back 71

statsitical tests that do not assume interval data and normal distribution scores.

Front 72

define one-way (goodness of fit) chi-square tests.

Back 72

a nonparametric statistical test using frequencies arranged in columns. defines whether or not expected and observe frequencies are in agreement.

Front 73

define expected frequency

Back 73

the frequency hypothesized or expected for an answer in a chi-square test.

Front 74

observed frequency

Back 74

the frequency in the sample for an answer in a chi-square test

Front 75

true or false: the less agreement there is between expected and observed outcome the bigger the chi-square value.

Back 75

true (if there was perfect agreement, the value would be 0)

Front 76

define two-way (contingency table) chi-square tests.

Back 76

nonparametric statistical test using frequencies arranged in rows and columns.

Front 77

define correlation

Back 77

a mathematical technique for determining the relationship between two sets of scores. When two variables are correlated, it becomes possible to make predictions.

Front 78

define regression

Back 78

that statistical model used to predict performance on on variable from another.

Front 79

define prediction

Back 79

estimation of a person's score on one measure based on the individuals' score on one or more other measures.

Front 80

simple prediction

Back 80

prediction of an individual's score on a measure based on the individual's score on another measure.

Front 81

y-intercept

Back 81

the point where the graphed prediction line crosses the y-axis; the value of the predicted score when the predictor score is zero.

Front 82

define standard error prediction

Back 82

the standard deviation for the errors of prediction; an index of the accuracy of a prediction equation.

Front 83

define cross validation

Back 83

method for checking the accuracy of a prediction equation on a second group of individuals similar to the first group.

Front 84

define multiple prediction

Back 84

prediction of an individuals score on a measure based on the individuals scores on several other measures.

Front 85

define polynomomial regression

Back 85

a regression model which does not assume a linear relationship; a curvilenear correlation coefficient is computed.