research meth final
Terms
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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/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)
- 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
- 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
- t-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
- t-test
- -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