D/RS 1013

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D/RS 1013

• Data Screening/Cleaning/ Preparation for Analyses

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Data entered in computer

• assuming reasonable care was taken
• scanner probably most "error free"
• checking physical forms against file
• verifying any recoding or score calculations
• "list cases"(mac) or "case summaries (windows)

3
Data screening

• descriptives look for out of range values
• check values against original forms
• correct data in file

4
Missing data

• respondents will not answer all questions on a
  survey
• what to do about items where data is missing?
• several options to consider/ways to address

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Missing data (cont.)

• single variable - is systematic bias present in
  the kinds of people who fail to answer an item?
• if the amount of missing data is small don't
  really need to worry
• use pairwise deletion
• pairwise can cause problems

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Missing data (cont.)

• drop subject's data completely
• if missing data on unimportant variable don't
  analyze
• if a reasonable guess can be made based on other
  available variables, do it
• numerical variable - use average

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Missing data (cont.)

• correlation between answered and unanswered
  questions
• regression equation to predict values on one
  variable based on others for which we have data
• new variable that flags whether they answered
  question or not
• analyze for possible differences on some other
  variable.

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Outliers

• exert influence on the mean
• inflate variance of the sample
• identify - look at a graph or run explore
  requesting outliers
• rule out some kind of data problem
• can dump and not use
• compromise is to move outlier
• residual analysis and detecting multivariate
  outliers when we move on to multiple regression
  (e.g. Mahalanobis Dist.)

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Normality

• assessing univariate normality
• look at graph
• skew and kurtosis values
• can test significance
• divide by standard error
• result is a z score

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Normality (cont.)

• tells us whether skew/kurtosis is significantly
  different than "0
• does not necessarily mean it is a problem
• Kline's (1998) recommendations skewness values gt
  3 and kurtosis gt 10
• If seriously violated transforming is an option

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Linearity of relationship

• relationship between variables reasonably
  summarized by straight line
• check scatterplot
• may be curvilinear

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Homoscedasticity

• assumption that variation in one variable is
  constant across range of another variable
• check scatterplot

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Homoscedasticity