USING MULTIVARIATE STATISTICS (5TH EDITION) BY. BARBARA G. TABACHNICK, LINDA S. FIDELL PDF. Just how can? Do you assume that you don't. Author: Barbara G. Tabachnick Pages: Publication Date Release Date: ISBN: Product Group:Book Free eBooks [PDF. Editorial Reviews. About the Author. Barbara Tabachnick is Professor Emerita of Psychology at Using Multivariate Statistics 6th Edition, Kindle Edition. by.
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USING. MULTIVARIATE. STATISTICS. SECOND EDITION. Barbara G. Tabachnick. Linda S. Fidell. California State University, Northridge. HarperCollins . Request PDF on ResearchGate | On Jan 1, , Barbara G. Tabachnick and others published Fidell Using Multivariate Statistics. PDF | On Jan 1, , Keith F. Widaman and others published Book Review: Using Multivariate Statistics Barbara G. Tabachnick and Linda S.
In standard analysis, each variable is assigned only its unique variance accounted for. Statistical power. Significance of. Power of. These are not reciprocals!! Social scientists typically expect. Software exists to estimate power for a given test for a given set of data.
Preview this item Preview this item. Using multivariate statistics Author: Harlow, Essex: Always learning. Subjects Multivariate analysis. More like this Similar Items. Allow this favorite library to be seen by others Keep this favorite library private. Find a copy in the library Finding libraries that hold this item Electronic books Additional Physical Format: Print version: Document, Internet resource Document Type: Reviews User-contributed reviews Add a review and share your thoughts with other readers.
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Fidell Using Multivariate Statistics, Fourth Edition. ISBN Go over it in class. Barbara G. Hypothesis testing: 1-sample z tests of difference of means - pp.
Classrooms nested within teaching methods, children nested within classroooms.
Comparison tests - pp. Eta - p. It tries to deal with the problem of inflation of eta with more IVs but sum of etas does not add to total system variance of the DV and may add to more than 1.
Pearsonian Correlation - pp. Bivariate Regression - pp. Chi-square analysis - p. The first variable contains one or more components which also appears in similar meaning in the second variable. This is most likely to happen when the variables are scales or indexes composed of multiple items. What is the main cause of attenuation of correlation? Explain Table 4. Table 4. You want not significant 2-tail p values. If you fail this test, data are not missing at random. Warning: SPSS uses this by default Best to pick a grouping variable thought to correlate highly with the variable with missing cases.
SPSS does not have this option.
Replaces missing values with the mean for the entire series. Replaces missing values with the mean of valid surrounding values. The span of nearby points is the number of valid values above and below the missing value used to compute the mean. Replaces missing values with the median of valid surrounding values. The span of nearby points is the number of valid values above and below the missing value used to compute the median.
Replaces missing values using a linear interpolation. The last valid value before the missing value and the first valid value after the missing value are used for the interpolation.
If the first or last case in the series has a missing value, the missing value is not replaced. Replaces missing values with the linear trend for that point.
The existing series is regressed on an index variable scaled 1 to n. Missing values are replaced with their predicted values. Use other variables to get a regression prediction of the missing values of one variable.
This only works if there are other IVs which do in fact predict the variable with missing cases. Also, regression imputation works too well: the real values might show more noise or even be outside the existing range. Maximum likelihood estimation MLE rather than regression estimates of the missing values. This is now the standard method for dealing with missing data! It is preferred to regression because it handles nonlinearities and makes fewer data assumptions, as will be discussed later in the course.
Use logistic regression in an iterative process to estimate m new data sets. Run your analysis on all m datasets. Report averaged coefficients over the m runs.
What other methods are discussed by Tabachnick for handling missing data pp. In SPSS correlation module, ask of pairwise deletion rather than the usual listwise. Coefficients will be calculated for all available bivariate data even if a case has missing data for variables not part of the pair being correlated at the moment.
This involves mathematical problems and is not recommended unless you have a large sample with only a few missing cases, then you can use this pairwise correlation matrix as input for factor analysis or other procedures. It may be this dummy will itself have explanatory power for your DV.
If a variable is thought relevant to a DV but has a lot of missing cases, this procedure is better than just dropping the variable. Run separate analyses and see if your outcomes are the same for data with imputed values vs. If there is a difference, you need to examine why.
If you cannot explain it, report both sets of results. What are the four main reasons for outliers? Wrongly coded data: scan your dataset for extreme and out-of-range values 2. SAV file has already defined missing values. The outlier case is not part of the intended population ex. Remove the case with replacement. The variable simply has a non-normal distribution with extreme values.
There are transforms to normalize data, or the case may simply be recoded to a more moderate value. Sometimes extreme cases need to be modeled separately.
Differentiate a univariate from a multivariate outlier. What criteria are used for each? Boxplots and normal probability plots are a graphical way to spot univariate outliers.
Multivariate: an extreme combination, like juvenile with a high income. Mahalanobis distance is the most common measure used for multivariate outliers. How do you screen for outliers in SPSS? You will get the five most extreme high and low cases. You can also click on Plots and get boxplots or normal probability plots if you prefer a graphical method. Analyze, Descriptive Statistics, Frequencies, Check for dichotomies with more than split.
You can also ask for histograms if you prefer a graphical method.
Analyze, Regression, Linear. Note there are a variety of Influence measures you could also check in the Influence panel. SPSS will add columns at the end of your dataset showing these coefficients. The larger the coefficient, the more it is an outlier with respect to the set of IVs not the DV. Therefore you may wish the DV not to be a variable of interest for the substantive analysis.
Is outlier if leverage extent case affects the prediction is DfFit is amount regression beta will change if case is dropped. Other than eyeballing, what is one way to analyze outliers? What do you do about outliers besides analyzing them? For multivariate normality? Needed for parametric procedures in the regression family. Central Limit Theorem says not to worry if you have grouped data ex. Frequency distributions. Just eyeball graphically, looking for bell-shaped curve..
Normal probability plots: Another graphical method: if normal, will form degree plot line horizontal line for detrended normal P-P plots.
Based on functions of skewness and kurtosis, Mardia's PK should be less than 3 to assume the assumption of multivariate normality is met. How do you test for linearity? SPSS supports 1st, 2nd, 3rd, 4th, and 5th degree polynomials. How do you test for homogeneity of variances? Homogeneity of variances is homoscedasticity for grouped data. If the Levene statistic is significant at the. The Levene test is robust in the face of departures from normality.
Tabachnick discusses F-max test for homogeneity of variances: if the ratio of the largest to smallest group in Anova is or less, then if the ratio of the largest to smallest group variance F-max is 10 or less, homogeneity of variances is not a problem. When group sizes are unequal, F-max may need to be as little as 3. Homogeneity of variance-covariance matrices is multivariate homogeneity of variances. The researcher wants this test not to be significant, so as to accept the null hypothesis that the groups do not differ.
This test is very sensitive to meeting also the assumption of multivariate normality.
What is the purpose of data transformations? To normalize data. See Figure 4. Inverse reciprocal transforms are stronger than logarithmic, which are stronger than roots. To correct left negative skew, first subtract all values from the highest value plus 1, then apply square root, inverse, or logarithmic transforms. Values of P greater than 1 powers correct left skew.
For right skew, decreasing P decreases right skew. Too great reduction of P will overcorrect and cause left skew. When the best P is found, further refinements can be made by adjusting C. For right skew, for instance, subtracting C will decrease skew. Logs vs. What is multicollinearity? What is singularity? If the redundancy is total, one has singularity, which may prevent algorithms from computing any answer.
High but not complete redundancy will still mean that the standard errors of the coefficients of the IVs are unreliable for purposes of comparing which IV is more important than another. Multicollinearity is less important in factor analysis, where there is no DV, but even there it can cause sub-optimization.
Adding crossproduct interaction terms and power terms sometimes introduces multicollinearity. What is tolerance in the context of multicollinearity?