64 terms

Research & Evidence Based Practice III, NPTE

From O'Sullivan& Siegelman 2009 NPTE guide
STUDY
PLAY
Systematic review, RCT, cohort study,homogeneity, case control study, case report
Types of Evidence and Research
Systematic Review (including meta-analysis)
a review in which the primary studies are summarized, critically appraised, and statistically combined; quantitative w/specific inclusion/exclusion criteria.
RCT (Randomized Controlled Trial)
experimental study, random sampling, random assignment to experimental or control group to receive different tx or placebo.
Cohort Study
prospective (forward in time) study; cohort w/similar condition is followed for defined period of time; comparison made to matched group w/out the condition.
Homogeneity
Systematic review free of variations in the directions and degree of results b/w individual studies.
Case Control Study
Retrospective (backward in time) study; group with similar condition compared with group that does not have condition to determine factors that might have played a role in the condition.
Case Report
Type of descriptive research in which only one individual is studied in depth, often retrospectively.
Levels of Evidence (best study design)
Level 1 (A), Level 2 (B) Level 3 (B) Level 4 (C)-Level 5(D)
Case series, poor quality cohort and case control studies. Descriptive
Level 4 (C)
Systematic review of cohort studies, prospective.
Level 2a (B)
Individual cohort study or low quality RCT (small N)
Level 2b (B)
Systematic review case controlled studies
Level 3a (B)
Individual case-control study, retrospective.
Level 3b (B)
Systematic review of multiple RCT's (large N)substantial agreement of size and direction of tx.
Level 1a (A)
Individual RCT w/narrow confidence level; tx effects precisely defined
Level 1b (A)
All or none case series. In absence of RCT, overwhelming evidence of substantial tx effect following intro of a new tx. (vaccine)
Level 1c (A)
Mean
average of all scores (X). Add all scores together and divide by the number of subjects (N). Used for interval and ratio data. Most common measure for central tendency.
median
midpoint, 50% of scores are above the mean and 50% are below, appropriate for ordinal data.
Mode
most frequently occurring score; used for nominal data.
Mean, Median, Mode
Measures of central tendency; determination of avg or typical scores.
Range, Standard Deviation, Normal distribution, Percentiles and quartiles
Measures of variability; determination of the spread of a groups scores
Range
difference b/w the highest score and lowest
SD (Standard Deviation)
determination of variability of scores (difference) from the mean. Subtract each score from the mean, square each difference, add up all the squares, and divide by the number of scores. For interval, ratio data.
Normal distribution
Symmetrical bell-shaped curve indicating the distribution of scores; the mean, median, and mode are similar. 1/2 the scores are above the mean and 1/2 below.
Normal distribution
Most scores are near the mean, within 1 SD; approx 68% of scores fall within +1 or -1 SD of mean.
Frequency of scores decreases further from the mean (Normal distribution)
95% of scores fall +2 or -2 SD of mean.
99% of scores fall +3 or -3 SD of the mean.
Percentiles
data divided in 100 equal parts
Quartiles
data divided into 4 equal parts and position of score is placed accordingly.
Inferential statistics
allow determination of how likely results of study of a sample can be applied to whole population.
Standard error of measurement
estimate of expected errors in individual's score; a measure of response stability or reliability.
Tests of significance
estimation of true differences, not due to chance; a rejection of the null hypothesis. ie. Probability levels or alpha levels.
Alpha levels (preselected level of statistical significance)
0.05 or 0.01; indicates expected difference is due to chance ie. 0.05, only 5 X out of 100 or 5% chance. P value. Allows rejection of null hypothesis; there are true differences on the measured DV.
Degrees of Freedom
based on # of subjects and # of groups; allows determination of level of significance based on consulting appropriate tables for each statistical test.
Standard Error
expected chance variation among the means, result of sampling error.
Type I Error
null hypothesis rejected by the researcher when it is true
Type II Error
null hypothesis is NOT rejected by the researcher when it is false.
Parametric statistics
based on population parameters; includes tests of significance based on interval and ratio data. Assume normal distribution in population, random sampling, variance in groups is equal.
T-test
parametric test compares 2 independent groups created by random assignment and identifies difference at a selected probability level ie 0.05.
T-test for independent sample, Paired samples; one tailed and two tailed T-tests.
Types of T tests
T-test for independent samples
compares the difference b/w 2 independent groups;
T-test for paired samples
compares difference b/w 2 matched samples; 2 subtests are one-tailed and two-tailed t-test.
One tailed t test
based on a directional hypothesis; evals difference in data only on 1 end of distribution, either negative or positive.
Two-tailed t-test
based on a nondirectional hypothesis; evals differences in data on both positive and negative ends of a distribution; tests of significance are almost always two-tailed.
Analysis of Variance (ANOVA)
parametric test too compare 3+ independent tx groups or conditions at a selected probability level.
Simple (one way) ANOVA
compares multiple groups on a single independent variable ie. 3 sets of posttest scores
Factorial ANOVA (multifactorial)
compares multiple groups on two or more independent variables.
Analysis of Covariance (ANCOVA)
parametric test used to compare 2 or more treatment groups or conditions while also controlling for the effects of intervening variables ie. subjects in one group taller than subjects in the second group-ht is covariate.
Nonparametric statistics
testing not based on population parameters; includes tests of significance based on ordinal or nominal data. Less powerful than parametric tests, more difficult to reject null hypothesis.
Chi square test
non-parametric test of significance used to compare data in the form of frequency counts occurring in 2 or more mutually exclusive categories ie. subjects asked to rate tx preferences.
Correlational statistics
to determine relative strength of a relationship b/w 2 variables.
Pearson product-moment coefficient (r)
used to correlate continuous data with underlying normal distribution on interval or ration scales. ie relationship b/w proximal and distal development in infants examined.
Spearman's rank correlation coefficient (rss)(rho)
nonparametric test to correlate ordinal data ie. verbal and reading scores
Types of Correlational statistical tests
Pearson-product-moment coefficient (r)
Spearman's rank correlation coefficient (rho), (rss)
Point biserial correlation
Rank biserial correlation
Intraclass correlation coefficient (ICC)
Strength of relationships
Common variance
Point biserial correlation
one variable is dichotomous (nominal) and other is ratio or interval eg. the relationship b/w elbow flexor spasticity and side of stroke.
Rank biserial correlation
one variable is dichotomous (nominal) and other is ordinal ie. relationship b/w gender and functional ability
Intraclass correlation coefficient (ICC)
a reliability coefficient based on an analysis of variance
Strength of relationships
Positive correlations range 0-+1.00; indicates as variable X increases so does Y
High Correlations
>0.76 to +1.00
Moderate correlations
0.51 to 0.75
Fair correlations
0.26 to 0.50
Low correlations
0.00 to 0.25, 0 means no correlation
Negative correlations
range from -1.0 to 0, indicates as variable X increases, variable Y decreases; an inverse relationship.
Common variance
representation of the degree that variation in one variable is attributable to another variable. Determined by squaring the correlation coefficient ie. coefficient of .70 means common variance is 49%.
Linear regression
used to establish relationship b/w 2 variables as a basis for prediction. X is the independent variable or predictor, Y is the dependent or criterion variable. ie. Can SBP be predicted from age?