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60 Cards in this Set
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1/12a standardized measure of the strength of relationship between two variables when one of the two variables is dichotomous. The biserial correlation coefficient is used when one variable is a continuous dichotomy (e.g., has an underlying continuum between the categories). |
Biserial correlation |
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a correlation between two variables. |
Bivariate correlation |
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a correlation between two variables.Bivariate correlationthe proportion of variance in one variable explained by a second variable. It is Pearson's correlation coefficient squared. |
Coefficient of determination |
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a measure of the 'average' relationship between two variables. It is the average cross-product deviation (i.e., the cross-product divided by one less than the number of observations). |
Covariance |
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a non-parametric correlation coefficient similar to Spearman's correlation coefficient, but should be used in preference for a small data set with a large number of tied ranks.Kendall's taua measure of the relationship between two variables while 'controlling' the effect of one or more additional variables on both. |
Partial correlation |
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Pearson's product-moment correlation coefficient, to give it its full name, is a standardized measure of the strength of relationship between two variables. It can take any value from −1 (as one variable changes, the other changes in the opposite direction by the same amount), through 0 (as one variable changes the other doesn't change at all), to +1 (as one variable changes, the other changes in the same direction by the same amount). |
Pearson's correlation coefficient |
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a standardized measure of the strength of relationship between two variables when one of the two variables is dichotomous. The point-biserial correlation coefficient is used when the dichotomy is a discrete, or true, dichotomy (i.e., one for which there is no underlying continuum between the categories). An example of this is pregnancy: you can be either pregnant or not, there is no in between. |
Point-biserial correlation |
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a measure of the relationship between two variables while 'controlling' the effect that one or more additional variables has on one of those variables. If we call our variables x and y, it gives us a measure of the variance in y that x alone shares. |
Semi-partial correlation |
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a standardized measure of the strength of relationship between two variables that does not rely on the assumptions of a parametric test. It is Pearson's correlation coefficient performed on data that have been converted into ranked scores. |
Spearman's correlation coefficient |
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the process of converting a variable into a standard unit of measurement. The unit of measurement typically used is standard deviation units (see also z-scores). Standardization allows us to compare data when different units of measurement have been used (we could compare weight measured in kilograms to height measured in inches). |
standardisation |
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a measure of the loss of predictive power or shrinkage in regression. The adjusted R² tells us how much variance in the outcome would be accounted for if the model had been derived from the population from which the sample was taken. |
Adjusted R |
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unstandardized regression coefficient. Indicates the strength of relationship between a given predictor, i, of many and an outcome in the units of measurement of the predictor. It is the change in the outcome associated with a unit change in the predictor. |
bi--(unstandardised beta weight..wtf is a beta weight...who makes these names) |
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standardized regression coefficient. Indicates the strength of relationship between a given predictor, i, of many and an outcome in a standardized form. It is the change in the outcome (in standard deviations) associated with a one standard deviation change in the predictor. |
βi (standardsied beta weights) |
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a way of recoding a categorical variable with more than two categories into a series of variables all of which are dichotomous and can take on values of only 0 or 1. There are seven basic steps to create such variables: (1) count the number of groups you want to recode and subtract 1; (2) create as many new variables as the value you calculated in step 1 (these are your dummy variables); (3) choose one of your groups as a baseline (i.e., a group against which all other groups should be compared, such as a control group); (4) assign that baseline group values of 0 for all of your dummy variables; (5) for your first dummy variable, assign the value 1 to the first group that you want to compare against the baseline group (assign all other groups 0 for this variable); (6) for the second dummy variable assign the value 1 to the second group that you want to compare against the baseline group (assign all other groups 0 for this variable); (7) repeat this process until you run out of dummy variables. |
Dummy variables |
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a test for serial correlations between errors in regression models. Specifically, it tests whether adjacent residuals are correlated, which is useful in assessing the assumption of independent errors. The test statistic can vary between 0 and 4, with a value of 2 meaning that the residuals are uncorrelated. A value greater than 2 indicates a negative correlation between adjacent residuals, whereas a value below 2 indicates a positive correlation. The size of the Durbin-Watson statistic depends upon the number of predictors in the model and the number of observations. For accuracy, look up the exact acceptable values in Durbin and Watson's (1951) original paper. As a very conservative rule of thumb, values less than 1 or greater than 3 are definitely cause for concern; however, values closer to 2 may still be problematic depending on the sample and model. |
Durbin-watson |
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a test statistic with a known probability distribution (the F-distribution). It is the ratio of the average variability in the data that a given model can explain to the average variability unexplained by that same model. It is used to test the overall fit of the model in simple regression and multiple regression, and to test for overall differences between group means in experiments. |
F ratio |
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an index of how well a model fits the data from which it was generated. It's usually based on how well the data predicted by the model correspond to the data that were actually collected. |
goodness of fit |
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the opposite of homoscedasticity. This occurs when the residuals at each level of the predictor variables(s) have unequal variances. Put another way, at each point along any predictor variable, the spread of residuals is different. |
Heteroscedasticity |
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a method of multiple regression in which the order in which predictors are entered into the regression model is determined by the researcher based on previous research: variables already known to be predictors are entered first, new variables are entered subsequently. |
Hierarchical regression |
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an assumption in regression analysis that the residuals at each level of the predictor variable(s) have similar variances. Put another way, at each point along any predictor variable, the spread of residuals should be fairly constant. |
homoscedasticity |
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for any two observations in regression the residuals should be uncorrelated (or independent). |
indepenent errors |
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these measure the influence of a case by examining the distance of cases from the mean(s) of the predictor variable(s). One needs to look for the cases with the highest values. It is not easy to establish a cut-off point at which to worry, although Barnett and Lewis (1978) have produced a table of critical values dependent on the number of predictors and the sample size. From their work it is clear that even with large samples (N = 500) and five predictors, values above 25 are cause for concern. In smaller samples (N = 100) and with fewer predictors (namely three) values greater than 15 are problematic, and in very small samples (N = 30) with only two predictors values greater than 11 should be examined. However, for more specific advice, refer to Barnett and Lewis's (1978) table. |
Mahalanobis distances |
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a measure of average variability. For every sum of squares (which measure the total variability) it is possible to create mean squares by dividing by the number of things used to calculate the sum of squares (or some function of it). |
mean of squares |
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a measure of the total amount of variability for which a model can account. It is the difference between the total sum of squares and the residual sum of squares. |
Model sum of squares |
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a situation in which two or more variables are very closely linearly related. |
Multicollinearity |
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a variable whose values we are trying to predict from one or more predictor variables. |
Outcome variable ....or DV |
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a variable that is used to try to predict values of another variable known as an outcome variable. |
predictor variable ...or IV |
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The difference between the value a model predicts and the value observed in the data on which the model is based. Basically, an error. When the residual is calculated for each observation in a data set the resulting collection is referred to as the residuals. |
residiual |
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the loss of predictive power of a regression model if the model had been derived from the population from which the sample was taken, rather than the sample itself. |
shrinkage (what happens when you read the adjusted R) |
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Student's t is a test statistic with a known probability distribution (the t-distribution). In the context of regression it is used to test whether a regression coefficient b is significantly different from zero; in the context of experimental work it is used to test whether the differences between two means are significantly different from zero. See also paired-samples t-test and Independent t-test. |
t-statistic |
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tolerance statistics measure multicollinearity and are simply the reciprocal of the variance inflation factor (1/VIF). Values below 0.1 indicate serious problems, although Menard (1995) suggests that values below 0.2 are worthy of concern. |
tolerence |
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a measure of the total variability within a set of observations. It is the total squared deviance between each observation and the overall mean of all observations. |
total sum of squares |
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measure of multicollinearity. The VIF indicates whether a predictor has a strong linear relationship with the other predictor(s). Myers (1990) suggests that a value of 10 is a good value at which to worry. Bowerman and O'Connell (1990) suggest that if the average VIF is greater than 1, then multicollinearity may be biasing the regression model.Variance inflation factor (VIF)Nice work!You just studied 49 terms!Start over |
VIF variance inflation factor |
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a test using the t-statistic that establishes whether two means collected from independent samples differ significantly |
independent t test |
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a test using the t-statistic that establishes whether two means collected from the same sample (or related observations) differ significantly. |
pair samples, repeated t test |
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if we were to take several pairs of samples from a population and calculate their means, then we could also calculate the difference between their means. If we plotted these differences between sample means as a frequency distribution, we would have the sampling distribution of differences. The standard deviation of this sampling distribution is the standard error of differences. As such it is a measure of the variability of differences between sample means. |
Standard error of differences |
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grand mean centring means the transformation of a variable by taking each score and subtracting the mean of all scores (for that variable) from it (cf. Group mean centring). |
grand mean centering |
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the effect of a predictor variable on an outcome variable when a mediator is present in the model (cf. indirect effect) |
Direct effect |
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the effect of a predictor variable on an outcome variable through a mediator (cf. direct effect) |
indirect eeffect |
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the combined effect of two or more predictor variables on an outcome variable. It can be used to gauge moderation. |
Interaction effect |
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occurs when the relationship between a predictor variable and an outcome variable can be completely explained by their relationships with a third variable. For example, taking a dog to work reduces work stress. This relationship is mediated by positive mood if (1) having a dog at work increases positive mood; (2) positive mood reduces work stress; and (3) the relationship between having a dog at work and work stress is reduced to zero (or at least weakened) when positive mood is included in the model. |
meditation |
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a variable that reduces the size and/or direction of the relationship between a predictor variable and an outcome variable (ideally to zero) and is associated statistically with both. |
mediator |
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occurs when the relationship between two variables changes as a function of a third variable. For example, the relationship between watching horror films (predictor) and feeling scared at bedtime (outcome) might increase as a function of how vivid an imagination a person has (moderator). |
Moderation |
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a variable that changes the size and/or direction of the relationship between two other variables |
moderator |
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a statistical procedure that uses the F-ratio to test the overall fit of a linear model. In experimental research this linear model tends to be defined in terms of group means, and the resulting .................. is therefore an overall test of whether group means differ. |
Analysis of variance ANOVA |
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a version of the F-ratio designed to be accurate when the assumption of homogeneity of variance has been violated. |
Brown-Forsythe F ( i reckon this might be an exam question) |
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an effect size measure that is the ratio of the model sum of squares to the total sum of squares. So, in essence, the coefficient of determination by another name. It doesn't have an awful lot going for it: not only is it biased, but it typically measures the overall effect of an ANOVA, and effect sizes are more easily interpreted when they reflect specific comparisons (e.g., the difference between two means). |
Eta squared (η²) |
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the probability of making a Type I error in any family of tests when the null hypothesis is true in each case. The 'family of tests' can be loosely defined as a set of tests conducted on the same data set and addressing the same empirical question. |
family wise error |
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means perpendicular (at right angles) to something. It tends to be equated to independence in statistics because of the connotation that perpendicular linear models in geometric space are completely independent (one is not influenced by the other). |
Orthogonal |
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a set of comparisons between group means that were not thought of before data were collected. Typically these tests involve comparing the means of all combinations of pairs of groups. To compensate for the number of tests conducted, each test uses a strict criterion for significance. As such, they tend to have less power than planned contrasts. They are usually used for exploratory work for which no firm hypotheses were available on which to base planned contrasts. |
post hoc test |
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a statistical procedure that uses the F-ratio to test the overall fit of a linear model, controlling for the effect that one or more covariates have on the outcome variable. In experimental research this linear model tends to be defined in terms of group means, and the resulting ANOVA is therefore an overall test of whether group means differ after the variance in the outcome variable explained by any covariates has been removed. |
covariance |
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a statistical procedure that uses the F-ratio to test the overall fit of a linear model, controlling for the effect that one or more covariates have on the outcome variable. In experimental research this linear model tends to be defined in terms of group means, and the resulting ............. is therefore an overall test of whether group means differ after the variance in the outcome variable explained by any covariates has been removed. |
Analysis of covariance |
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a variable that has a relationship with (in terms of covariance), or has the potential to be related to, the outcome variable we've measured. |
covariate |
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an assumption of analysis of covariance. This is the assumption that the relationship between the covariate and outcome variable is constant across different treatment levels. So, if we had three treatment conditions, if there's a positive relationship between the covariate and the outcome in one group, we assume that there is a similar-sized positive relationship between the covariate and outcome in the other two groups too. |
Homogeneity of regression slopes |
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the phenomenon that people of the opposite gender (or the same, depending on your sexual orientation) appear much more attractive after a few alcoholic drinks. |
Beer-goggles effect |
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an analysis of variance involving two or more independent variables or predictors. |
factorial anova |
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an experimental design incorporating two or more predictors (or independent variables) at least one of which has been manipulated using different participants (or whatever entities are being tested) and at least one of which has been manipulated using the same participants (or entities). Also known as a split-plot design because Fisher developed ANOVA for analysing agricultural data involving 'plots' of land containing crops |
Mixed design |
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an experimental design incorporating two or more predictors (or independent variables) all of which have been manipulated using the same participants (or whatever entities are being tested). |
Related factorial design |
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a test of the assumption of sphericity. If this test is significant then the assumption of sphericity has not been met and an appropriate correction must be applied to the degrees of freedom of the F-ratio in repeated-measures ANOVA. The test works by comparing the variance-covariance matrix of the data to an identity matrix; if the variance-covariance matrix is a scalar multiple of an identity matrix then sphericity is met. |
Mauchly's test |
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an analysis of variance conducted on any design in which the independent variable (predictor) or variables (predictors) have all been measured using the same participants in all conditions. |
repeated measures anova |
