Screening

Question 1

Ungrouped Data = ?

Answer 1

multiple regression, canonical correlation, factor analysis, or structural equation modeling

Question 2

Grouped Data = ?

Answer 2

analysis of covariance, multivariate analysis of variance or covariance, profile analysis, discriminant analysis, or multilevel modeling

Question 3

What can Values be?

Answer 3

Out of range
plausible values
coding accuracy (frequencies table)

Question 4

Missing Data?

Answer 4

suspect it-test it

Question 5

Missing Data how to test?

Answer 5

use a dummy variable and test between group diff – anything less than 5% missing yields same result from any method of recovery depending on size of data set.

Question 6

Types of missing data

Answer 6

1) MCAR (Missing completely at random – i.e. unpredictable, independent of other variables in study)
2) MAR (missing at random, ‘ignorable non-response’ – predictable from other variables – e.g. patients might miss questionnaire)
3) MNAR (missing not at random or ‘non-ignorable’ – missingness is related to the variable – serious bias of results, e.g. patients less likely to complete qu. Because of score)

Question 7

Types of tests for missing data?

Answer 7

1) SPSS MVA (missing value analysis) – finds patterns and replaces values. T-test produced to test if missingness is related to any other variable on cases with >5% missing data. This tests MCAR, MAR & MNAR. ‘Little MCAR test’ = if not sig assume MCAR; if sig and missingness is relted to other IVs (not DV) then MAR. If sig with DV, then MNAR.

Question 8

how to deal with missing data ?

Answer 8

1) Omission / Deleting (still report) – Ok if (<5%) with MAR/MCAR & dataset mod to large. Problematic if small, experimental design, or MNAR.
2) Prior knowledge – Impute or fill in using expert prior knowledge. Could use dichotomous downgrade (high vs low).
3) Impute Mean Substitution – Easy, conservative, but reduces variance in data set (because the mean is closer to itself than to the missing value it replaces, and the correlation the variable has with other variables is reduced because of the reduction in variance)
4) Impute Regression – More sophisticated. Other IVs used to write regression equation for variable with missing data (DV). Sometimes repeated regressions, using predicted values from REG1 as new DV. Reduces variance (values closer to the mean) and inflates relationship (scores fit better than they should) between IVs. Relies on good relationship between DV and potential IVs. Can use SPSS MVA for this.
5) Expectation Maximisation (EM) – Possible for MCAR/MAR – EM assumes normal distribution and makes missing data correlation matrix for the partially missing data. It bases inferences about missing values on the likelihood under that distribution. 1) finsing conditional expectation given all data and estimates of parameters 2) maximum likelihood – values inputted iteratively. Biased as does not add error to imputed data (biased standard error).
6) Multiple Imputation – No assumptions about randomness. Complex in SPSS. Can be applied to longitudinal data and time-series and retains sampling variability.

Question 9

once dealt with missing data, what good to do?

Answer 9

Contrast different methods – do not base method on outcome

Question 10

types of outliers?

Answer 10

univariate & multivariate

Question 11

what/how to test for univariate outliers?

Answer 11

1) Standardise variable – absolute values z = > 3.29 (.1% sample)
2) Histograms / boxplots
3) Outlier decision should be independent of results
4) Tackling univariate first should limit multivariate

Question 12

what/how to test for multiivariate outliers?

Answer 12

1) Best with formal stats methods
2) Mahalanobis Distance (MD) = distance of a case centroid (intersection of variable means)
3) MD tested using chi sq distribution (X2) with conservative alpha (p

Question 13

what is Mahalanobis Distance ?

Answer 13

1) distance of a case centroid (intersection of variable means)

Question 14

what is leverage ?

Answer 14

1) Leverage = similar to MD but cant use same sig tests with X2 as on diff scale. Leverage is how far out (but can still be in line)

Question 15

what is discrepancy ?

Answer 15

1) Discrepancy = is how out of line.

Question 16

what is influence? and which is corresponding stat?

Answer 16

1) Influence = The product of Leverage and Discrepancy - It assesses change in regression coefficients when a case is deleted; cases with influence scores larger than 1.00 are suspected of being outliers – using Cook’s distance.

Question 17

why identify multiivariate outliers?

Answer 17

1) Why are cases extreme? 1. is case part of sample 2. If intending to modify scores, which scores to modify 3. Provides indication as to which results do not genrealise.

Question 18

how to properly describe multiivariate outliers?

Answer 18

1) Create a dummy variable for outliers and non-outliers
2) Dummy variable for outliers is then used as DV in logistic regression to determine which IVs best predict group membership.
3) Check M & SDs of outlier in variables identified.

Question 19

how to deal with outliers?

Answer 19

1) Omit with caution (if not in pop)
2) Transform variable
3) Windsorize / nearest neighbour – reduce or increase score based on next (surrounding) values
4) Trim a priori a % of cases from upper & lower tails of dist
5) After dealing with univariate you still have multivariate in large N best to omit.
6) Compare with and without outliers output for effect of omission
7) Report any remedies or changes
8) Non-parametric tests are less sensitive (possible solution)

Question 20

Important point of Grouped data normality and e.g. of one analysis type?

Answer 20

Grouped Data – e.g. anova – norm is sampling dist of variable means – central limit theorem

Question 21

Important point of UN Grouped data normality and e.g. of one analysis type?

Answer 21

Ungrouped Data – e.g. Regression – multivariate impractical, analysis of individual variables and residuals through norm, linearity, homoscedasticity

Question 22

what is test of distribution ?

Answer 22

Kolmogorov-Smirnoff

Question 23

what is skewness ?

Answer 23

1) degree of symmetry (positive / negative / normal = 0) – spss freq table

Question 24

what is kurtosis ?

Answer 24

– Peakedness or flatness (Leptokurtic (positive) / Platykurtic (negative) / Normal = value of 0 – spss freq table

Question 25

simple way of testing distribution ?

Answer 25

Can test using z scores diff from 0

Question 26

Normality can be assumed if …..

Answer 26

1) Curran et al. (1996) suggest normality can be assumed if skewness values are not greater than abs val 2 and kurtosis values are not greater than abs val 7.

Question 27

plots for normality?

Answer 27

1) Plots = Histogram (with normal overlay) // normal probability plot (straight line with dots plots observed against expected) // Detrended prob plot (similar but plots deviation)

Question 28

how to address normality issues?

Answer 28

1) Transform (log for skewed / sq rt / inverse)
2) Windsorize / Trim
3) Check after completion / examine impact on analysis
4) Use non-para if recalcitrant

Question 29

what is linearity?

Answer 29

Assumption variables have linear (straight line) relations - for Pearson corr and regression.

Question 30

how to assess linearity ?

Answer 30

1) Assessed using bivariate scatterplots – should be oval-shaped
1) Some variables may inherently have non-linear

Question 31

types of linear reatonships? + alternative if non-linear?

Answer 31

1) Alernatives if non-linear = (recode in to dummy variables somehow).
2) Types of relationship – Quadratic, cubic,

Question 32

what is Homoscedasticity?

Answer 32

Variables variance roughly equal

Question 33

How to assess Homoscedasticity Grouped?

Answer 33

1) Grouped = Var(DV) = Var(all levels of discrete IV) - assessed Levene’s Test

Question 34

How to assess Homoscedasticity UN Grouped?

Answer 34

1) Un Grouped = Inspect scatterplots – non-normaility in one or both of variables

Question 35

what is Multi-collinearity ?

Answer 35

• Multi-collinearity – IV too correlated with each other (e.g. >.90)

Question 36

Factors involved with multi-collinearity ?

Answer 36

1) Correlation Matrix
2) Tolerance / VIF (accepted values I forgot)
3) Can also use that MACRO I doenloaded for SPSS
4) Remove or combine