Week 12 - Missing Data Analysis

Question 1

List wise deletion

Answer 1

Dropping participant from the analysis who don’t have complete scores on all the variables in the model

Need nonmissing scores on all variables

It reduces the sample size and makes it harder to find a significant effect
Lower power, waste data and exacerbate bias ( especially when data is nonignorable)

Question 2

Balanced vs unbalanced data

Answer 2

Balanced design - same number of cells in the analysis

- make computation easier

Question 3

Why are scores missing initially?

Answer 3

Participant factor - mortality, attrition ( in longitudinal design)
Experimenter factor - clerical error, malfunction
Balance between not coercing people into giving answers and making it to easy to respond

Question 4

What are old approaches of missing data

Answer 4

List wise deletion
Pairwise deletion
Mean substitution ( mean imputation)
Regression imputation
Last value carried forward

Question 5

Pairwise deletion

Answer 5

Only available for correlation and factor analysis

Use all cases available for each pair of variables

Question 6

Regression Imputation

Answer 6

Replace missing data with predicted score from regression based on all available cases

Standard error too small

Question 7

Last value carried forward

Answer 7

No longer valid

Approach to longitudinal design

Attrition (drop out) lose data point ( if drop out of the third wave, 2nd wave score will replace third wave)

Intention to treat analysis

Question 8

Problems with old approaches

Answer 8

Underestimating error variance
SE too small
CI too narrow
Type 1 error too high

Question 9

Previous approach to missingness

Answer 9

Lessen the impact of missingness (nuisance factor)

Question 10

Rubin and little approach to missingness

Answer 10

Estimate missingness statistically

Mechanism of missingness is important

Question 11

Types of missingness

Answer 11

Ignorable and nonignorable

Ignorable - fewer constraints on type of analysis, reduced bias however still have problems with power (problems with precision)

Non ignorable - listwise deletion will lead to problems with bias and precision

Question 12

Three Types of missing data

Answer 12

MCAR - Missing completely at random
MAR - Missing at random
MNAR - Missing not at random

Question 13

MCAR

Answer 13

Ignorable

Probability of it being missing on a given variable is not conditional on itself or on other variables in the data set

Cause of missingness completely outside of data

Question 14

MAR

Answer 14

Ignorable

Probability of being missing on given variable not conditional on itself but IS conditional on other variables in the data set

eg. older people less likely to respond to question on sex

Question 15

MNAR

Answer 15

Non-ignorable

Probability of being missing on given variable is conditional on itself , missingness predicted what would have been said

eg. embarrassed to answer question because of what it would have been (often effect the outcome variable)

Can lead to big bias

Problematic when trying to estimate population prevalence of behaviour or state (people who are too sick, too drunk are missing from the analysis because of what they would have answered)

Question 16

Approaches to Missing Data

Answer 16

Listwise deletion (MNAR)
Multiple Imputation 
Direct maximum likelihood (Modelling approach)

Question 17

Listwise deletion for MNAR

Answer 17

Can be used however if high proportion is missing will lead to low power
Bias unacceptable for MAR and MNAR
- Will under or over estimate the regression weights

Question 18

Direct Maximum Likelihood

Answer 18

Use all available data and the modelling is built into the procedure

Statistical way to deal with missingness

Used in SEM and mixed effects regression

Can be used on MAR

Question 19

Multiple Imputation

Answer 19

Fill in missing data with values that include extra random variance

Overcome barriers of using the sample mean or other variance (Impoverished scores that will lead to type 1 error - underestimation)

Can us for MAR

Question 20

Steps to Multiple Imputation

Answer 20

1) Do regression imputation where missing scores are replaced with predicted scores from the regression on all available cases (Imputation model)
2) Add random error to the imputed score
3) repeat the process on seperate times to make (m) of these data sets
4) run desired stats on each of the (m) data sets
5) Take the model parameters of interest (M and b-weight) average them and use the SE to calculate the statistical test

Question 21

Can you tell which type of missingness is present?

Answer 21

Can only test MCAR
- if planning to use listwise deletion

No way of telling for MAR and MNAR
- Have to use logical and theoretical understanding

Question 22

What is the best way to deal with missing data?

Answer 22

Good clear items that have been piloted
Follow up on non-responders
Mandatory questioning

if missingness below .5 approach does not matter
-0 unlikely to make difference between parameter estimates