Study your flashcards anywhere!

Download the official Cram app for free >

  • Shuffle
    Toggle On
    Toggle Off
  • Alphabetize
    Toggle On
    Toggle Off
  • Front First
    Toggle On
    Toggle Off
  • Both Sides
    Toggle On
    Toggle Off
  • Read
    Toggle On
    Toggle Off

How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key


Play button


Play button




Click to flip

65 Cards in this Set

  • Front
  • Back
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/double-blind 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, cross-sectional, cross-sequential
single-subject design
single subject, at least one baseline and one tx phase. Types: AB (baseline-tx), 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 double-blind 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 cross-sectional designs
cross-sequential design
-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)
major threat to single-subject 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)
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
z-score (standard score)
-how many standard deviations a given raw score is from the mean -z-score 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 z-scores
-mean of 50 and sd of 10 -z-score of +1 equals t-score 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
two-tailed vs. one-tailed 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 -t-test and ANOVA -assumptions: normal distribution, homogeneity of variance, independence of observations (most imp.)
nonparametric test
used for nominal or ordinal data -chi-square, Mann-Whitney U -NO assumptions about distributions -less powerful than parametric tests
-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
One-way ANOVA
-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)
Wilcoxon Matched-Pairs Test
compare two correlated groups on a DV w/RANK ORDERED data (like t-test for correlated samples)
Kruskal-Wallis Test
compare two or more independent groups on DV w/RANK ORDERED data (like One-way 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 pre-set alpha level and degrees of freedom for statistical test
F ratio (ANOVA)
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
ANOVA summary table
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)
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 (rank-order 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?
one-way 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 observed-only once after having been exposed to some treatment
what is the problem with on shot studies
has internal and external validity problems
describe the one-group pretest-posttest
1 group is assembled and protested then exposed and then post-tested
describe the static-group 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 control-group design
1 group is formed, pre-tested, exposed to treatment, and then post-tested, ANOTHER group is pre-tested and then post-tested 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