- Author scored a 10 on the exam - A total overview of all obligatory topics for MMSR; based on the key glossary terms of the chapters from Hair and the video clips, I have made an overview per topic. Every topic has some theoretical background listed, whereafter the analysis process is depicted. F...
MMSR
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Overview of multivariate methods.........................................................................................................2
Examining your data...............................................................................................................................3
Rules...................................................................................................................................................3
Figures................................................................................................................................................4
Exploratory and confirmatory factor analysis.........................................................................................6
Rules...................................................................................................................................................7
Figures................................................................................................................................................8
(M)AN(C)OVA.......................................................................................................................................11
Rules.................................................................................................................................................12
Figures..............................................................................................................................................13
Multiple and logistic regression analysis..............................................................................................17
Rules.................................................................................................................................................18
Figures..............................................................................................................................................19
Partial Least Squares Structural Equation Modeling (PLS-SEM)............................................................23
Rules.................................................................................................................................................24
Figures..............................................................................................................................................24
,Overview of multivariate methods
Multivariate analysis = any simultaneous analysis of more than two variables.
,Examining your data
Imputation method = process of estimating the missing data of an observation based on valid values
of the other variables. Possibilities:
Cold-deck imputation: from data outside your database
EM: maximum likelihood (MAR)
Hot deck imputation: from existing observation deemed similar
Mean substitution: substituting by means
Multiple imputation: MAR
Regression imputation: calculating it based on regression models
In case you are not going to replace the data with values, two options are possible:
Complete case approach: handling missing data based on complete cases, cases with no
missing data. Also known as the listwise deletion approach.
All-available approach: handling missing data based on all available data, also known as
pairwise approach.
Positive kurtosis means a steep line, negatively kurtosis is a flatter line. Positively skewed is many
observations on the left, negatively skewed is many observations on the right. The threshold values
for kurtosis and skewness are -3 to 3 (without dividing it by the standard error!).
Always consider both the practical and substantive impact of your missing data.
Rules
How much missing data is too much?
o Over 10%
o Under 10% is acceptable, but assess the MAR/MCAR
When is a value an outlier?
o For small samples a standard score of 2.5
o For large samples a standard score of 4
o If standard scores are not given, use the threshold values with standard deviations
Normality
o Above 200 sample size, normality is often okay
o Skewness and kurtosis values between -3 and 3.
, Figures
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