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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