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65 Cards in this Set
- Front
- Back
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independent variable
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input variable, variable that is manipulated by researcher, IVs effect the DVs
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dependent variable
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output variable, measured not manipulated (d=describe), changes as result of manipulations of IV
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internal validity, definition
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allows researcher to say there is a causal relationship between IVs and DVs
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threats to internal validity
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may cause relationship instead of IV 1. history 2. maturation, 3. testing, 4. instrumentation, 5. statistical regression, 6. selection, 7. differences in dropouts & non, 8. experimenter bias
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most powerful method of controlling threats to internal validity
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random assignment of subjects, ensures equivalency of extraneous factors, "great equalizer"
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Other methods of controlling threats to internal validity
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1. matching on ex var, 2. blocking - studying ex var as a IV, 3. including only homogenous subj on ex var, 4. ANCOVA - mathematical adj to equalize on ex var
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external validity, definition
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generalizability of results
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threats to external validity
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-selection effects -testing effects -history effects (tx doesn't gen beyond setting/time conducted) -demand characteristics (cues in setting) -Hawthorne Effects -order effects in repeated measures
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how control for threats to external validity
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random selection -naturalistic/field research -single/double-blind designs -control order effects with counter balancing
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correlational design
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variables not manipulated, no causal relationship assumed only degree of relationship
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developmental research
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assess variables as function of dev over time i.e. aging on IQ scores. Types: longitudinal, cross-sectional, cross-sequential
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single-subject design
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single subject, at least one baseline and one tx phase. Types: AB (baseline-tx), reversal, and ABAB (mult baseline)
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experimenter expectancy
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a.k.a. Rosenthal effect or Pygmalion effect -change in behavior result of experimenter expectances rather than IV -overcome with double-blind techniques
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random assignment vs. random selection/sampling
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Random selection is method of selecting subjects for study (=chance of participation), random assignment happens after subjects have been selected (=chance of assignment to groups).
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cohort effects
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observed differences between age groups may have to do with experience rather than age. problem with cross-sectional designs
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cross-sequential design
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-combines longitudinal and cross-sectional designs. -samples of diff age groups assessed on two or more occasions. -control cohort effects, less time consuming than longitudinal (help w/drop out)
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major threat to single-subject designs
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much variablility in target behavior, difficult to establish reliable baseline
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ordinal data, def and ex.
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order of categories but not HOW much more/less -ranks, likert scales
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interval data vs. ratio data
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interval: equal distances but NO absolute zero point (complete absence of attribute), ex temp, IQ ratio: same as ordinal but with absolute zero, can mult & divide
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negatively skewed distribution
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easy test few scores fall at low end (tail on left/negative end w/lump on right)
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positively skewed distribution
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hard test few scores fall at high end (tail on right/positive end w/lump on left)
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variance
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measure of variability of disribution -average of squared differences of each score from mean -equal to the square of the standard deviation (S2)
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standard deviation
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square root of variance (s) -expected deviation from mean of a score chosen at random
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z-score (standard score)
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-how many standard deviations a given raw score is from the mean -z-score distributions have sd of 1 and mean of 0
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linear transformation
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when transformation of scores does not change distribution shape, i.e. raw scores to z-scores
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t-scores
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-mean of 50 and sd of 10 -z-score of +1 equals t-score of 60
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nonlinear transformation
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converting scores will change shape of distribution, i.e. raw scores to percentile ranks
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standard deviation curve stats (for normal distribution)
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68% fall between +-1z or sd, 95% fall between +-2z or sd, +-1z or sd equivalent to PR of 84/16 or top/bottom 16%, +2z or sd equivalent to 98th PR/top 2%
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sampling error
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difference between sample mean and population mean (one type) statistic (sample value) vs. parameter (population value)
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standard error of the mean
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expected difference between sample mean and population mean, s.d/square root of N, inverse relationship bt sample size and std. error of mean
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two-tailed vs. one-tailed hypothesis
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two tailed states a mean is different from another mean but do not know in which direcction -one tailed states mean is either > or < another mean
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Type I error
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rejecting a true null hypothesis
if two groups are the same and the researcher thinks they are different that is also |
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Type II error
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accepting a false null hypothesis and
-found NO difference when there in fact IS one - |
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parametric test
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used for interval and ratio data -t-test and ANOVA -assumptions: normal distribution, homogeneity of variance, independence of observations (most imp.)
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nonparametric test
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used for nominal or ordinal data -chi-square, Mann-Whitney U -NO assumptions about distributions -less powerful than parametric tests
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t-test
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-compare two means (t for two) -one sample: sample mean to known pop mean (df=N-1) -independent sample: means from two independent samples (df=N (total # subj in study) -2) -correlated samples: means of two correlated samples (before/after) (df=N (# pairs
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One-way ANOVA
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-one IV and 2+ groups/levels -statistic is F, ratio of between/within group variance -does not indicate which means are diff (post-hoc tests)
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Wilcoxon Matched-Pairs Test
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compare two correlated groups on a DV w/RANK ORDERED data (like t-test for correlated samples)
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Kruskal-Wallis Test
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compare two or more independent groups on DV w/RANK ORDERED data (like One-way ANOVA)
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critical value
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determine whether or not to reject null hypothesis (table) - if obtained value exceeds critical value, reject null -value to use depends on pre-set alpha level and degrees of freedom for statistical test
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F ratio (ANOVA)
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comparison of between-group variance (tx variance) and within-group variance (error variance) -desire between group variance to be large (effect of tx) and within-group error to be small
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ANOVA summary table
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Sum of Squares: variability of set of data (between, within) DF: between = k(# groups) - 1, within = N-k Mean Squares: Sum of Squares/DF (illustrates in table the f-ratio)
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relationship between correlation and causality
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correlation is a necessary but not sufficient condition of causality, correlation does not guarantee causality but if causal link is established then they must be correlated
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Pearson r (PPM)
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calculates the relationship between two variables -most commonly used correlation coefficient in psychology
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What factors affect the Pearson r?
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1. linearity: assumes linear rel (not curvilinear) 2. homoscedasticity: assumes = dispersion of scores (not heteroscedasticity) 3. range of scores: wider range will yeild more accurate correlation
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Spearman's Rho (rank-order corelation)
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correlate two variables ordinally ranked (compare two judges rankings on same set of observations)
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regression analysis can be used as a substitute for what?
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one-way ANOVA
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canonical correlation
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used with multiple criterion and multiple predictor variables
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discriminant function analysis
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used to predict criterion group membership, not a criterion score (like multiple regression)
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differential validity
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when each predictor has different correlation with each criterion variable
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what does mortality mean
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mortality refers to the differential loss of participants between experimental and control groups
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describe the one shot study
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a single group is observed-only once after having been exposed to some treatment
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what is the problem with on shot studies
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has internal and external validity problems
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describe the one-group pretest-posttest
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1 group is assembled and protested then exposed and then post-tested
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describe the static-group comparison
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1 group is exposed to experimental treatment and then compared to another group that has not had treatment, no attempt is made to pretest the groups
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describe the nonequivalent control-group design
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1 group is formed, pre-tested, exposed to treatment, and then post-tested, ANOTHER group is pre-tested and then post-tested no treatment
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what is a weakness in the nonequivalent study
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no randomization/
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describe a distibution
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it is the frequency count of attributes that fall into different categories for normal level of measurement
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what are the four characteristics that are usually described in the measurement levels of a distribution
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central tendency variability skewness and kurtosis
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what are the three common measures of central tendency
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median; mean; mode
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how do you calculate median
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it is the middle number if there are 11 numbers it is the 6th number
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How do you calculate mode
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this is the most frequently occurring number
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How do you calculate mean
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add up all the values and divide by the number of items
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define degrees of freedom
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pertains to the subjects in use
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when is a z ratio used instead of a t
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when there are more then 30 ppl
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