Ch22Research

Question 1

ANOVA

Answer 1

Analysis of Variance, used to evaluate differences among two or more independent or dependent groups by partitioning the variance in the data set in different ways; also can be used to analyze differences between more than one independent variable at a time, between more than one dependent variable, or to remove the impact of an intervening variable

Question 2

Two ANOVA techniques

Answer 2

Factorial (between subjects and mixed design) and multivariate (MANOVA and analysis of covariance ANCOVA)

Question 3

Two tests to determine difference between more than one independent variable

Answer 3

Between-Subjects Two Way ANOVA

Mixed-Design Two Way ANOVA

Question 4

Between Subjects Two Way ANOVA

Answer 4

also called two factor ANOVA; two IVs are examined; it can be described as a 3x2 ANOVA describing number of levels of each of the factors

Question 5

Mixed Design Two Way ANOVA

Answer 5

used when one IV is repeated measures variable and the other IV is independent; frequently used to analyze pretest-posttest control group designs

Question 6

Tests to determine differences across several dependent variables

Answer 6

One way ANOVA for each dependent variable, MANOVA

Question 7

MANOVA

Answer 7

multivariate procedure uses an omnibus test to determine whether there are significant differences on the factor of interest when the DVs of interest are combined mathematically

Question 8

Which multivariate test statistic is used most frequently?

Answer 8

Wilks’ Lambda, converted to an estimated F statistic and the probability of is estimated F statistic is used to test the null hypothesis

Question 9

How do you determine the effect of removing an intervening variable?

Answer 9

analysis of covariance = ANCOVA; uses overall relationship between a DV and an intervening variable, or covariate, to adjust the DV in light of the covariate scores

Question 10

What are the four most common ways to analyze single-subject design data?

Answer 10

celeration line analysis; level, trend, slope, and variability analysis; two standard deviation band analysis; C statistic

Question 11

How is a celeration line analysis used?

Answer 11

compares data in different phases by generating a line based on the median of subsets of data in each phase; the number of data points in the intervention phase and the number exceeding the celeration line are counted and the probability of having scores above the celeration line is generated

Question 12

Level Analysis (Single-Subject Design)

Answer 12

celeration line is calculated; level is the difference between numerical value of observations in one phase and the numerical value of observations in a subsequent phase

Question 13

Trend Analysis (Single-Subject Design)

Answer 13

celeration line is calculated; trend is the direction of change in the pattern of results

Question 14

Slope Analysis (Single-Subject Design)

Answer 14

celeration line is calculated; slope is the difference between two Y values divided by the difference between the X values

Question 15

Variability Analysis (Single-Subject Design)

Answer 15

celeration line is calculated; variability is the change in the range of scores in one phase compared with the range of scores in an adjacent phase

Question 17

How is the C statistic used in single-subject designs?

Answer 17

appropriate for small data sets and sets with serial dependency, similar to the logic of ANOVA and the sum of squared deviation scores related to the treatment effect are divided by the sum of squared deviation scores related to the baseline

Question 18

What is a survival analysis?

Answer 18

mathematical tool used to analyze the changing proportion of survivors over time after some naturally occurring event; there is also expanded use of survival analysis for outcomes other than death

Question 19

What is the survival curve?

Answer 19

two defined events form the basis for a survival curve: the event that qualifies the patient for inclusion in the analysis and the event that removes the patient from the analysis (could be death but could also be a different outcome)

Question 20

What is the significance of the differences between survival curves?

Answer 20

researchers are often more interested in the significant difference in survival curves for participants who have experienced different events or procedures versus a single event or procedure

Question 21

How are confidence intervals (CIs) calculated and used?

Answer 21

adding and subtracting standard errors to and from the mean; the error that the researcher is willing to tolerate is based on the number of standard deviations above and below the mean that are included in the CI

Question 22

When is CI testing used?

Answer 22

1) when sample statistics are used as estimates of population parameters 2) in addition to or instead of the results of hypothesis testing 3) means to assess the clinical importance of research results 4) with adjusted confidence levels when multiple intervals are calculated (controlling for alpha inflation) 5) when reporting the results of individual studies that are included with meta-analyses

Question 23

When using the CI, when can we conclude that there is a significant difference between means?

Answer 23

when the CI does not contain “0”

Question 24

How is the interpretation of the CI used clinically?

Answer 24

tells us that there is a 95% chance a population difference in means exists, this information provides sound basis on which to judge clinical importance of the finding

Question 25

What does maximizing the power in a research design involve?

Answer 25

maximizing the size of the difference in the DV between levels of the IV, minimizing the amount of variability on the DV within levels of the IV, maximizing the sample size

Question 26

How does the alpha level selected by the researcher influence power?

Answer 26

higher power associated with larger alpha levels

Question 27

When is power analysis used?

Answer 27

in the design phase and after the analysis phase

Question 28

How is power analysis used in the design phase?

Answer 28

typically 80% power used to detect differences that exist; estimate size of between-group difference that would be important, variability expect to see in groups studied, and sample size that is reasonable; dry run statistical analysis and if power <80% then reconsider elements of design

Question 29

What is involved when power analysis is used to estimate sample size?

Answer 29

must specify their desired power level, as well as the anticipated between-group differences and within-group variability

Question 30

How can effect size be used?

Answer 30

to help plan sample size when areas in which little previous research exists; it is the ratio of the difference between the means to the pooled standard deviation of the groups being compared; for comparison of two group means .20 is small, .50 is medium, and .80 is large

Question 31

How is a power analysis used after a statistical test has failed to identify a statistically significant difference?

Answer 31

to look for possible explanations of non-significant findings, low probabilities of type II errors are associated with higher power values, lack of power (or a type II error) is often a likely explanation for a non-significant result in rehabilitation research

Question 35

How is a two standard deviation band analysis used in single-subject designs?

Answer 35

horizontal line representing mean is drawn across the baseline and intervention phases; two other horizontal lines are drawn two standard deviations above and below the mean (2-SD band); scores falling outside the band in the intervention phase would represent change as the result of treatment