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True experiment (Randomized controlled trial) compares two groups, two groups being manipulated, with participants randomly assigned to a groups
Quasi-experiments (non randomized control trial)- participants NOT randomly assigned, limitation-decrease researcher’s ability to draw cause-effect conclusions, pragmatic/ethical issues can arise, bias selection process can occur, often groups are preexisting
Preexperimental research pretest/posttest design, single group is compared before and after, one group, nothing manipulated receive the same intervention, no random assignment Much less powerful than a design with a control or comparison group
PICO Population, Intervention, Comparison, Outcome
Levels I, II, III, IV, V I Systematic review randomized control trials II randomized control trial III Nonrandomized control trial IV One group pretest and postest (preexpermental) V Case reports and expert opinion
Randomized controlled trial 2 groups: experimental and control Participants randomly assigned Intervention applied to experimental group
Systematic Review analyzes multiple randomized controlled trials
Reliability consistency of measure, dependability of scores
Validity assess what it was intended to measure
Sensitivity proportion of individuals who are accurately identified as possessing a condition of interest
Specificity proportion of individuals who do not have condition
Prevalence People who have a particular condition
Incidence Risk of developing a condition
Cross sectional/ longitudinal CS- single point in time, L: multiple times
Impact factor How many times journal has been cited
Publication bias Research to have positive outcomes
Experimental research Cause and Effect, efficacy/ intervention studies, one intervention, one group control
Nonexperimental research cannot determine cause and effect, descriptive relationship, observational and correlation
Third variable 2 constructs could be related but the third variable could have caused the relationship (ie cold weath causes flu, or person's bx causes flue (3rd variable))
Experimental/ Efficacy all study designs true, quasi, preexperiment
Nonexperimental- Descriptive-- all study designs D-Group-two or more existing groups identify differences, Incid/Preval- occurrence of one or more characteristics calculated
Nonexperimental Relationship all study designs R-Correlation-relationship between two or more constructs calculated Predictive-predictors are considered in terms of their impact on an outcome
Directional hypothesis researcher has assumption/belief on particular outcome
Nondirectional hypothesis exploratory, no prior notion on results
Quantitative Focus on confirmation, test hypothesis, objective, deductive ,numbers, is it reliable and valid
Qualitative builds on theory, exploratory, subjective, inductive, interviews, trustworthy
Basic research questions to better understanding individual concepts ( environmental conditions and stress rxn)
Applied research direct to health care ( efficacy of fall prevention programs)
Type I and Type II error Type I- hypothesis is accepted even though it is false-occure bc chance Type II- hypothesis is rejected even though it is true- sample size is too small
When do we accept research hypothesis? p < 0.05- still a 5% chance that hypothesis is false, 95% chance that it is true
Categorical variables autism- girls and boys, gender is a catergory
Continuous variable numbers have meaning to one another, higher-- more than something- autism spectrum- age and grade level
Independent variable what is being manipulated or compared, intervention (intervention and control--2 variables)
Dependent variable intended to measure the result of the manipulation ( interested in communication skills in of participants- communication skills is dependent variable) outcome
Control variable remain constant
Extravenous variable tracked and then later examined to determine influence
Descriptive stats describe study in a meaningful way, patterns that emerge from data
Inferential stats techniques that allow us to use study samples and make generalizations that apply to population (descriptive used in the calculation of infer.)
Frequencies/frequency distribution -how often something occurs -graph used to depict the count
N,n number of participants
a (alpha) level of significance
p probability value, liklihood of making a type I error
t and F t- critical value in a t-test F- critical value ANOVA
r and r2 r-correlation coefficient r2-variance in correlational study
ES effect size-magnitude or strength of a stat
s2 variance of a sample
sd or s or omega standard deviation of a sample
Median when is it applicable? distribution is skewed,less sensitive to extreme scores
(+) (-) distributions where do the scores fall? what are they called? (+)-mean, median mode - -mode median mean measures of central tendency
Variability spread of scores
Most common measure of variability standard deviation
% in a normal distribution 34.1, 13.6, 2.1 (both sides)
Positive skewed, and negative skewed positive tail in the back, negative tail in the front
statistical significance probability that the result of the study could have occurred by chance
0.05 p is what? level of significance standard, 5% risk not a true difference, 95% true
T-test broad Measures difference between: 2 groups at a single time or one group at two time point
Dependent t test Differences within a group @ two time points on a single dependent variable, differences between a group on two different dep. variables
Independent t-test between-difference in mean scores between two groups, unrelated to each other, at one point, single dep. variable
df degrees of freedom or number of values in the study free to vary based on the # of participants in the study
one way ANOVA compare differences between 3 or more groups a single dep. measure similar to ind. t-test
Repeated Measures ANOVA Compare the differences of one group 3 or more times (dep. t -test)
Mixed model ANOVA Both between group and within group, differences simultaneously providing an interaction effect, provide separate results- between group differences and within groups diff(main)
Analysis of covariance ANCOVA Compare differences between and/or within groups while statistically controlling a variable (covariable)
ANOVA more than two means compared
Correlations degree at which two variables flucuate together
p<0.05 p>0.05 interaction effect exists no interaction effect performed similiar
r ranges what does it mean if + or - 0-1.0, +- related in the same direction speed and distance -- - related in opposite direction, speed and time- faster speed less time
PPMC Inferential stat that examines the strength of relationship between two continous variables
Spearman Same as PPMC but compares variables that are rank ordered
Odds ratio OR= AD/BC-odds of presence/absence of one variable is associated with the presence/absence of another variable
Linear regression identify predictors of continuous outcome measure (ROM- numbers mean something)
Logistic regression identify predictors of categorical outcome measure (only two outcomes)
Factors that have the greatest impact on a particular outcome predictors
Total variance r2 (correlation squared) 100- r2 is the variance unaccounted for variance is always smaller than correlation
CI reliability- range of outcomes expected
Difference between clinical and stat. significance stat. is based on sample size- likely to find a difference with a large sample size, could have a small mean difference (1.5) but that is not clinically significant
Cohen's d effect size measure, difference between two groups in standard deviation measure
R is effect size in relationship studies, what does it mean if r value is 0.87 vs. 0.2 0.87 greater effect, stronger relationship 0.20 smaller effect, lesser relationship
Created by: kmackdaddy
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