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62 Cards in this Set

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Def: cluster analysis *
Random selection of naturally occurring groups, rather than individuals
Def: analog study
Assessing a phenomenon under conditions that resemble the phenomenon in the field
Def: a cross-sequential research design
A combination of longitudinal and cross-sectional designs

Subjects divided into age groups
Assessed on dependent variable repeatedly over time
Developmental research designs
Longitudinal
Cross-sectional
Cross-sequential
Describe a matching design
Grouping subjects similar on an extraneous variable and then assigning members of the group to each treatment condition
Describe stratified random sampling
Random sampling of sub-groups of a population

eg children, teens, young adults, etc
Describe multiple baseline study *
Single subject
Application of treatment across different baselines (behaviors, settings, individuals)
Used when reversal is not possible or is unethical
Describe a one-group time-series design
Multiple pre-tests, followed by treatment, followed by multiple post-tests

Controls for maturation, testing and regression effects

Vulnerable to history, or a simultaneously occurring event
Formula: variance (s squared)
(sum of (X - mean)squared) / n

population denominator n
sample denominator n-1
Formula: z score
(X - mean) / standard deviation
T score attributes
mean = 50
sd = 10
Stanine attributes
Divides score range into equal ninths
Mean = 5
SD = 2
Formula: standard error of the mean *
SE = standard deviation / square root of N

also is SD of the sampling distribution of means

the expected difference between the sample mean and the population mean
Chi square requirements *
Independent observations
Mutually exclusive categories
Frequency, not percentage data used
Formula: ANOVA mean square
mean square = sum of squares / df
Use: phi coefficient
Two dichotomous variables
Use: point-biserial coefficient *
One interval or ratio variable
One naturally dichotomous variable (2 categories)
Use: biserial coefficient *
One interval or ratio variable
One artifically dichotomous variable (2 categories)
(eg scores above, scores below)
Use: contingency coefficient
Two nominally scaled variables, each with more than 2 categories
Use: canonical correlation
Multiple predictors and
multiple criterion values
Use: Spearman's rho
Both predictor and criterion variables are ranked
Def: coefficient of determination
Pearson r squared
% of variability accounted for in the correlation
In an ANOVA, what does within group variance measure
Random variance
Which has the smallest variance?

population samples
individual samples
mean population samples
Mean population samples
When are non-parametric tests used?
When normality can't be assumed
When homogeneity of variance is compromised, the best way to assure result robustness is...?
To keep sample size equal
Use: eta correlation
With non-linear, continuous variables
Distinguish the use of: t-test, one way ANOVA, factorial ANOVA, MANOVA and ANCOVA *
t-test: pair of means

One way ANOVA:
1 independent variable; 2 groups

Factorial ANOVA:
>1 independent variable
permits analysis of interaction effects

MANOVA
>1 dependent variables
minimizes p(Type I error)

ANCOVA
to control for the presence of an extraneous variable
Def: internal validity
Study that permits the conclusion that there is a causal effect between the independent and dependent variable
Threats to internal validity
History - an external event
Maturation
Test learning
Changes in instrumentation
Statistical regression
Subject characteristics
Systematic differences between stickers and dropouts
Experimenter bias
Methods for controlling threats to internal validity
Randomization
Matching
Blocking
Hold extraneous variables constant
ANCOVA
Def: matching
Grouping subjects by status on extraneous variable and then randomly assigning from within groups
Def: blocking
Treating an extraneous variable like another independent variable
Def: time series design
Multiple pre-tests
Treatment
Multiple post-tests

History is a threat to internal validity
Bias in longitudinal studies
Tendency to underestimate age related change, esp decrements

Drop outs tend to be poorer performers

Practice effects on measures
Bias in cross sectional studies
Over estimation of effects due to aging

Cohort effects
Experience
Def: Type II error (beta) *
retaining a false null hypothesis

failing to detect a true effect
Techniques to increase the validity coefficient
Increase the range of scores
Def: shrinkage *
Occurs when predictors are DEVELOPED on one sample and then VALIDATED on another. The correlation coefficient for the second sample is likely to be lower.
def: power *
ability to detect a treatment effect
p (rejecting a false null hypothesis)
p (not making a type II error)
1 - beta
factors affecting power
sample size - larger
alpha - larger
one tailed test
magnitude of the population difference - larger
assumptions of parametric tests
normal distribution of the dependent variable

homogeneity of variance

independence of observations - most critical
Def: F statistic
In an ANOVA, the ratio of between group variance over within group variance
Common non-parametric tests
Chi-square - frequencies of nominal data

Mann-Whitney U - non-parametric equivalent of a t-test; 2 independent groups - nominal scores

Wilcoxon Matched-Pairs test - non-parametric equivalent of a t-test for correlated scores

Kruskal-Wallis test - non-parametric alternative to a one-way ANOVA
ANOVA post-hoc tests
Scheffe provides greatest protection against a type I error, but increases probability of a type II error

Tukey most appropriate for pairwise comparisons
Calculation of CHI-square expected frequencies
simple case = subjects / cells

complex case = column total * row total / total N
Assumptions of Pearson r
linear relationship between variables

homoscedasticity - equal variability on y throughout the x range

r is highest when using the full range of scores on both variables
Use: discriminant function analysis
scores are combined to determine group assignment

in contrast to multiple regression in which multiple variables are combined to predict a score
Def: differential validity
in discriminant analysis, each predictor has a high correlation with a single category criterion and a low correlation with the other category criteria

IQ has low differential validity
Use: structural equation modeling
testing causal models based on multiple variables
Techniques of structural equation modeling *
Path analysis - one way causal relationship with observed values

LISREL - one or two way causal analysis with both observed and infered variables

helps sort out the contributions of true score and error variance
Use: trend analysis
determination of shape of the relationship between variables: eg linear, quadratic, cubic, quartic...

yield the significance of the trend
Def: sampling distribution
a distribution of the values of a statistic (eg the mean) with each value computed from same-sized samples drawn with replacement from the population

has less variability than the population distribution
Central limit theorem *
1. As sample size increases the shape of the sampling distribution of means approaches a normal shape - even if the distribution of scores is not normal
2.The mean of the sampling distribution of means is equal to the mean of the population
Rosenthal effect
aka experimenter expectancy effect

unintentional effect experimenter exerts towards making the results come out right
experiment-wise error rate
probability of making at least 1 type I error when multiple comparisons are made in a single experiment
heteroscedasticity
unequal variability of y scores at different values of x
Effect on t test when comparison groups are highly correlated
Within group variability is suppressed, giving an artificially high t value
Threats to external validity *
Interaction between selection and treatment would create problems in generalization
Use: tetrachoric coefficient
2 artificially dichotomous variables
Use: paired t test
Analysis of means when groups are not independent (eg twin studies or repeated measures)

df = # of pairs - 1
ANOVA vs multiple regression
ANOVA uses categorical independent variables only

Multiple regression can use either categorical or continuous variables