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62 Cards in this Set
- Front
- Back
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Def: cluster analysis *
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Random selection of naturally occurring groups, rather than individuals
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Def: analog study
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Assessing a phenomenon under conditions that resemble the phenomenon in the field
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Def: a cross-sequential research design
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A combination of longitudinal and cross-sectional designs
Subjects divided into age groups Assessed on dependent variable repeatedly over time |
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Developmental research designs
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Longitudinal
Cross-sectional Cross-sequential |
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Describe a matching design
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Grouping subjects similar on an extraneous variable and then assigning members of the group to each treatment condition
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Describe stratified random sampling
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Random sampling of sub-groups of a population
eg children, teens, young adults, etc |
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Describe multiple baseline study *
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Single subject
Application of treatment across different baselines (behaviors, settings, individuals) Used when reversal is not possible or is unethical |
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Describe a one-group time-series design
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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 |
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Formula: variance (s squared)
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(sum of (X - mean)squared) / n
population denominator n sample denominator n-1 |
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Formula: z score
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(X - mean) / standard deviation
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T score attributes
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mean = 50
sd = 10 |
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Stanine attributes
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Divides score range into equal ninths
Mean = 5 SD = 2 |
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Formula: standard error of the mean *
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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 |
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Chi square requirements *
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Independent observations
Mutually exclusive categories Frequency, not percentage data used |
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Formula: ANOVA mean square
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mean square = sum of squares / df
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Use: phi coefficient
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Two dichotomous variables
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Use: point-biserial coefficient *
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One interval or ratio variable
One naturally dichotomous variable (2 categories) |
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Use: biserial coefficient *
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One interval or ratio variable
One artifically dichotomous variable (2 categories) (eg scores above, scores below) |
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Use: contingency coefficient
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Two nominally scaled variables, each with more than 2 categories
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Use: canonical correlation
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Multiple predictors and
multiple criterion values |
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Use: Spearman's rho
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Both predictor and criterion variables are ranked
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Def: coefficient of determination
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Pearson r squared
% of variability accounted for in the correlation |
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In an ANOVA, what does within group variance measure
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Random variance
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Which has the smallest variance?
population samples individual samples mean population samples |
Mean population samples
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When are non-parametric tests used?
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When normality can't be assumed
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When homogeneity of variance is compromised, the best way to assure result robustness is...?
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To keep sample size equal
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Use: eta correlation
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With non-linear, continuous variables
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Distinguish the use of: t-test, one way ANOVA, factorial ANOVA, MANOVA and ANCOVA *
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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 |
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Def: internal validity
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Study that permits the conclusion that there is a causal effect between the independent and dependent variable
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Threats to internal validity
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History - an external event
Maturation Test learning Changes in instrumentation Statistical regression Subject characteristics Systematic differences between stickers and dropouts Experimenter bias |
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Methods for controlling threats to internal validity
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Randomization
Matching Blocking Hold extraneous variables constant ANCOVA |
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Def: matching
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Grouping subjects by status on extraneous variable and then randomly assigning from within groups
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Def: blocking
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Treating an extraneous variable like another independent variable
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Def: time series design
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Multiple pre-tests
Treatment Multiple post-tests History is a threat to internal validity |
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Bias in longitudinal studies
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Tendency to underestimate age related change, esp decrements
Drop outs tend to be poorer performers Practice effects on measures |
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Bias in cross sectional studies
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Over estimation of effects due to aging
Cohort effects Experience |
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Def: Type II error (beta) *
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retaining a false null hypothesis
failing to detect a true effect |
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Techniques to increase the validity coefficient
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Increase the range of scores
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Def: shrinkage *
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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.
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def: power *
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ability to detect a treatment effect
p (rejecting a false null hypothesis) p (not making a type II error) 1 - beta |
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factors affecting power
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sample size - larger
alpha - larger one tailed test magnitude of the population difference - larger |
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assumptions of parametric tests
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normal distribution of the dependent variable
homogeneity of variance independence of observations - most critical |
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Def: F statistic
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In an ANOVA, the ratio of between group variance over within group variance
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Common non-parametric tests
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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 |
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ANOVA post-hoc tests
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Scheffe provides greatest protection against a type I error, but increases probability of a type II error
Tukey most appropriate for pairwise comparisons |
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Calculation of CHI-square expected frequencies
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simple case = subjects / cells
complex case = column total * row total / total N |
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Assumptions of Pearson r
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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 |
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Use: discriminant function analysis
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scores are combined to determine group assignment
in contrast to multiple regression in which multiple variables are combined to predict a score |
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Def: differential validity
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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 |
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Use: structural equation modeling
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testing causal models based on multiple variables
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Techniques of structural equation modeling *
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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 |
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Use: trend analysis
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determination of shape of the relationship between variables: eg linear, quadratic, cubic, quartic...
yield the significance of the trend |
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Def: sampling distribution
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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 |
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Central limit theorem *
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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 |
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Rosenthal effect
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aka experimenter expectancy effect
unintentional effect experimenter exerts towards making the results come out right |
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experiment-wise error rate
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probability of making at least 1 type I error when multiple comparisons are made in a single experiment
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heteroscedasticity
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unequal variability of y scores at different values of x
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Effect on t test when comparison groups are highly correlated
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Within group variability is suppressed, giving an artificially high t value
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Threats to external validity *
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Interaction between selection and treatment would create problems in generalization
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Use: tetrachoric coefficient
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2 artificially dichotomous variables
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Use: paired t test
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Analysis of means when groups are not independent (eg twin studies or repeated measures)
df = # of pairs - 1 |
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ANOVA vs multiple regression
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ANOVA uses categorical independent variables only
Multiple regression can use either categorical or continuous variables |
