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Emipically Validated Treatment (ETV)/Empirically Supported Treatment (EST)
Research integrated into practice
Correlation
●Negative 1 to 0 to positive 1
●0= No correlation
●-1 and +1 = Both are perfect correlation
●Positive Correlation - When X goes up, Y goes up (ex. When you study more, GPA goes up)
●Negative Correlation - When X goes up, Y goes down (ex. The more you brush your teeth, the less you will have cavities)
True Experiment
●Two or more groups used
●Random Sampling
●Random assignment
●Systematic Sampling- Every nth person can also be used
●Quasi-experimental- Doesn't ensure causality, groups not random or researchers can't control the independent variables (IV)
●Experimental groups get the Independent Variables (IV)/experimental variables
●Control Groups do not receive the IV
●Dependent Variable (DV) - Outcome Data (ex. Do eating carrots raise IQ? Carrots = IV, DV = Measured IQ)
●Null Hypothesis - No significant difference between Control and Experimental groups
●Experimental/alternative hypothesis- There is a significant difference (carrots DO raise IQ)
●Type I alpha error - Researcher rejects null hypothesis that IS true (i.e. truly no significant difference, but research claims difference in experimental group- carrots don't raise IQ, but research claims they do)
●Type II beta error - Researcher accepts null when it should have been rejected (i.e. Research claims no difference, but there is - Research claims no significance between carrots and raised IQ, but there is a difference)
●Significance Level - 0.05 or less
●N=1 - Single subject design or case study
●Demand Characteristics - Evident when research subjects have cues about desired/undesired behavior, Can make research inaccurate.
●Obtrusive/reactive measure - Subjects know they are being observed (ex. Observer presence)
●Internal Validity - high when experimental has few flaws and findings are accurate. Low when researcher didn't measure what they thought they measured.
●External Validity - high when results can be generalized to other settings
●Confounding Extraneous Variables- other factor that causes change (ranch dressing carrots dipped in raised IQ)
●t Test - Parametric test comparing two means
●ANOVA/analysis of variance - 2 or more means to compare, provides F variables
●F test - Will tell you if significant differences are present
●MANOVA/multivariant analysis of variance- Use when more than one DV.
●Factorial analysis of variance- when investigating more than one IV
●Chi Quare or Kruskal-Wallace- Non-parametric tests for when a population is not necessarily normal
●Ex post facto/causal comparative design - researcher looking at after the fact data
●Descriptive Statistics - Describe central tendency like mean, median, mode, range, quartiles, variance, standard deviation
●Statistical Analyses - correlation coefficients, t tests, ANOVAs, analysis of covariance, Chi square, Kruskal-Wallace, etc.
●Cohort Studies - Exam groups of people with things in common
●Longitudinal Research - Relies on observation or data over a period of time
●Cross-sectional data - observations or data from a given point in time.
●Formative evaluation - Takes place during treatment or program
●Summative/Outcome Evaluation - takes place at the end of treatment or program
●Between Groups Design - Different subjects in different groups
●Within Groups Repeated Measures Design - Same subjects for control conditions and at a different time for experimental conditions
●Cauusal Comparative Design -
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