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65 Cards in this Set
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independent variable

input variable, variable that is manipulated by researcher, IVs effect the DVs


dependent variable

output variable, measured not manipulated (d=describe), changes as result of manipulations of IV


internal validity, definition

allows researcher to say there is a causal relationship between IVs and DVs


threats to internal validity

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


most powerful method of controlling threats to internal validity

random assignment of subjects, ensures equivalency of extraneous factors, "great equalizer"


Other methods of controlling threats to internal validity

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


external validity, definition

generalizability of results


threats to external validity

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


how control for threats to external validity

random selection naturalistic/field research single/doubleblind designs control order effects with counter balancing


correlational design

variables not manipulated, no causal relationship assumed only degree of relationship


developmental research

assess variables as function of dev over time i.e. aging on IQ scores. Types: longitudinal, crosssectional, crosssequential


singlesubject design

single subject, at least one baseline and one tx phase. Types: AB (baselinetx), reversal, and ABAB (mult baseline)


experimenter expectancy

a.k.a. Rosenthal effect or Pygmalion effect change in behavior result of experimenter expectances rather than IV overcome with doubleblind techniques


random assignment vs. random selection/sampling

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).


cohort effects

observed differences between age groups may have to do with experience rather than age. problem with crosssectional designs


crosssequential design

combines longitudinal and crosssectional designs. samples of diff age groups assessed on two or more occasions. control cohort effects, less time consuming than longitudinal (help w/drop out)


major threat to singlesubject designs

much variablility in target behavior, difficult to establish reliable baseline


ordinal data, def and ex.

order of categories but not HOW much more/less ranks, likert scales


interval data vs. ratio data

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


negatively skewed distribution

easy test few scores fall at low end (tail on left/negative end w/lump on right)


positively skewed distribution

hard test few scores fall at high end (tail on right/positive end w/lump on left)


variance

measure of variability of disribution average of squared differences of each score from mean equal to the square of the standard deviation (S2)


standard deviation

square root of variance (s) expected deviation from mean of a score chosen at random


zscore (standard score)

how many standard deviations a given raw score is from the mean zscore distributions have sd of 1 and mean of 0


linear transformation

when transformation of scores does not change distribution shape, i.e. raw scores to zscores


tscores

mean of 50 and sd of 10 zscore of +1 equals tscore of 60


nonlinear transformation

converting scores will change shape of distribution, i.e. raw scores to percentile ranks


standard deviation curve stats (for normal distribution)

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%


sampling error

difference between sample mean and population mean (one type) statistic (sample value) vs. parameter (population value)


standard error of the mean

expected difference between sample mean and population mean, s.d/square root of N, inverse relationship bt sample size and std. error of mean


twotailed vs. onetailed hypothesis

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


Type I error

rejecting a true null hypothesis
if two groups are the same and the researcher thinks they are different that is also 

Type II error

accepting a false null hypothesis and
found NO difference when there in fact IS one  

parametric test

used for interval and ratio data ttest and ANOVA assumptions: normal distribution, homogeneity of variance, independence of observations (most imp.)


nonparametric test

used for nominal or ordinal data chisquare, MannWhitney U NO assumptions about distributions less powerful than parametric tests


ttest

compare two means (t for two) one sample: sample mean to known pop mean (df=N1) 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


Oneway ANOVA

one IV and 2+ groups/levels statistic is F, ratio of between/within group variance does not indicate which means are diff (posthoc tests)


Wilcoxon MatchedPairs Test

compare two correlated groups on a DV w/RANK ORDERED data (like ttest for correlated samples)


KruskalWallis Test

compare two or more independent groups on DV w/RANK ORDERED data (like Oneway ANOVA)


critical value

determine whether or not to reject null hypothesis (table)  if obtained value exceeds critical value, reject null value to use depends on preset alpha level and degrees of freedom for statistical test


F ratio (ANOVA)

comparison of betweengroup variance (tx variance) and withingroup variance (error variance) desire between group variance to be large (effect of tx) and withingroup error to be small


ANOVA summary table

Sum of Squares: variability of set of data (between, within) DF: between = k(# groups)  1, within = Nk Mean Squares: Sum of Squares/DF (illustrates in table the fratio)


relationship between correlation and causality

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


Pearson r (PPM)

calculates the relationship between two variables most commonly used correlation coefficient in psychology


What factors affect the Pearson r?

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


Spearman's Rho (rankorder corelation)

correlate two variables ordinally ranked (compare two judges rankings on same set of observations)


regression analysis can be used as a substitute for what?

oneway ANOVA


canonical correlation

used with multiple criterion and multiple predictor variables


discriminant function analysis

used to predict criterion group membership, not a criterion score (like multiple regression)


differential validity

when each predictor has different correlation with each criterion variable


what does mortality mean

mortality refers to the differential loss of participants between experimental and control groups


describe the one shot study

a single group is observedonly once after having been exposed to some treatment


what is the problem with on shot studies

has internal and external validity problems


describe the onegroup pretestposttest

1 group is assembled and protested then exposed and then posttested


describe the staticgroup comparison

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


describe the nonequivalent controlgroup design

1 group is formed, pretested, exposed to treatment, and then posttested, ANOTHER group is pretested and then posttested no treatment


what is a weakness in the nonequivalent study

no randomization/


describe a distibution

it is the frequency count of attributes that fall into different categories for normal level of measurement


what are the four characteristics that are usually described in the measurement levels of a distribution

central tendency variability skewness and kurtosis


what are the three common measures of central tendency

median; mean; mode


how do you calculate median

it is the middle number if there are 11 numbers it is the 6th number


How do you calculate mode

this is the most frequently occurring number


How do you calculate mean

add up all the values and divide by the number of items


define degrees of freedom

pertains to the subjects in use


when is a z ratio used instead of a t

when there are more then 30 ppl
