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4 Preparing Raw Data

Almost by definition, raw data does not need to be prepared for
analysis. However, computer programs rarely communicate with each
other without some form of translation of data format, and getting
data out of datasets maintained in popular statistical packages such
as SAS or SPSS and into Mx is no exception. In this section we
briefly describe SAS and SPSS scripts that output data into a file
suitable for Mx to read.
Mx has two main ways to read individual scores. First, and most
straightforward, is `rectangular' format, with one case per line, with variables
separated by one or more spaces. A case is a collection of possibly
correlated observations, such as several variables assessed on an
individual, or on both members of a twin pair, or on a whole family.
Because family members correlate, it is necessary to consider the
whole family as a `case'. Separate cases are assumed to be
uncorrelated, which is important for statistical purposes. Certain
new methods available in programs such as Sudaan, SAS proc mixed, and
Stata make it possible to account for some correlation between
different cases, usually when data are grouped, e.g., subjects in the
same school. These methods can prove useful for running standard
statistical analyses at the individual level (multiple regression,
survival analysis) by taking into account the covariation between
family members. However, they do not help with the preparation of
data for modeling genetic and environmental factors which is the
primary objective here.
The default code that Mx recognises as indicating missing data is a
dot ``.`

' which is the same as SAS and SPSS. A sample SAS script
to produce rectangular data is shown in Appendix . Mx's
missing command can be used to declare a different string as the
missing value, and it is important to note that this is a string and
not a numeric value, as 1.0 and 1.00 will be considered to be
different.
The second main format for raw data that Mx accepts is variable
length, or `vl'.

** Next:** 5 Summary
** Up:** 2 Data Preparation
** Previous:** 5 Using PRELIS with
** Index**
Jeff Lessem
2002-03-21