1 / 13

D/RS 1013

D/RS 1013. Data Screening/Cleaning/ Preparation for Analyses. 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).

agrata
Download Presentation

D/RS 1013

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. D/RS 1013 Data Screening/Cleaning/ Preparation for Analyses

  2. 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

  5. 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

  6. 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

  7. 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.

  8. 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.)

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

  10. 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 > 3 and kurtosis > 10 • If seriously violated transforming is an option

  11. Linearity of relationship • relationship between variables reasonably summarized by straight line • check scatterplot • may be curvilinear

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

  13. Homoscedasticity

More Related