Data%20preparation%20for%20use%20in%20SEM

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Data preparation for use in SEM

• Ned Kock

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Data in table format
Each column corresponds to a manifest variable.
Some groups of columns correspond to a latent
variable.
Each row often contains the answers from one
subject under a particular condition, and is also
known as a case.
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Missing values

• A missing value is an empty cell in a data table.
• Missing values are a fact of life in many areas
  of research, including behavioral research.
• In terms of behavioral research, missing values
  may be present when
• Respondents do not answer one or more questions
  in a questionnaire.
• A researcher empties a data cell when a
  respondent answers a question with non-usable
  data e.g., by responding with a 0 (zero) when
  asked for his or her age.

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Examples of missing values
Datasets with missing values are a common
occurrence in behavioral research, as well as
other types of research.
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Percentage of missing data
A simple Excel formula can be used to calculate
the percentage of missing data for a manifest
variable.
How much is too much? A recent Monte Carlo
simulation suggests that as much as 30 may be
okay. More than that can lead to
problems. Supporting source Kock, N. (2014).
Single missing data imputation in PLS-SEM.
Laredo, TX ScriptWarp Systems.
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Dealing with missing values

• A first step is to make an effort to ensure that
  no more than 30 of the data is missing in each
  column of a data table.
• The above can be accomplished by employing data
  collection techniques that minimize missing data
  e.g., targeted questionnaires and interviews.
• Then the remaining missing cells can be filled
  using one of the several imputation methods, such
  as
• Arithmetic Mean Imputation
• Multiple Regression Imputation
• Hierarchical Regression Imputation
• Stochastic Multiple Regression Imputation
• Stochastic Hierarchical Regression Imputation

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Missing data imputation with WarpPLS
Main menu gt Settings gt View or change missing
data imputation settings
Using deletion, listwise or pairwise, to deal
with missing data Researchers have
traditionally used deletion methods, often
listwise and pairwise deletion, to deal with
missing data. A report by the American
Psychological Association Task Force on
Statistical Inference stated that these
techniques are among the worst
methods available for practical
applications. Supporting source Kock, N.
(2014). Single missing data imputation in
PLS-SEM. Laredo, TX ScriptWarp Systems.
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Missing data imputation performance
Main menu gt Settings gt View or change missing
data imputation settings
Results from a Monte Carlo simulation Multiple
Regression Imputation yielded the least biased
mean path coefficient estimates, followed by
Arithmetic Mean Imputation. With respect to mean
loading estimates, Arithmetic Mean Imputation
yielded the least biased results, followed by
Stochastic Hierarchical Regression Imputation and
Hierarchical Regression Imputation. Supporting
source Kock, N. (2014). Single missing data
imputation in PLS-SEM. Laredo, TX ScriptWarp
Systems.
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Replacing missing values with SPSS
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Creating source data file for WarpPLS

• Source data files contain the data used in a
  WarpPLS analysis.
• They are often referred to as raw data files.
• Source data files should be prepared as follows
• They should be .xls or .xlsx files (Excel), or
  plain text files with the names of the variables
  first followed by each data case in the same
  order as the variables listed (missing data
  points do not have to be imputed a-priori).
• If text files, variable names and numeric data
  should be separated from each other by tabs.
• If text files, the suffix of the data file should
  be designated as .txt.

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Using Excel to create a .txt file
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Important tips

• One file format that usually works well for a
  .txt file, and that is widely available is the
  ASCII tab-delimited format.
• If you are using Excel to create a .txt file,
  save the Excel-formatted file first, and create
  the .txt file with a different name.
• With Excel, have only one worksheet with the raw
  data.
• You can also create .txt tab-delimited files
  using SPSS, in which case it is important to
  instruct SPSS to write the variable names into
  the .txt file.
• The above is done by default when you use Excel.

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Reading raw data file in WarpPLS
File import wizard
Viewing and accepting data
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Acknowledgements
Adapted text, illustrations, and ideas from the
following sources were used in the preparation of
the preceding set of slides

1. Kock, N. (2015). WarpPLS 5.0 User Manual. Laredo,
   TX ScriptWarp Systems.
2. Kline, R.B. (1998), Principles and Practice of
   Structural Equation Modeling, The Guilford Press,
   New York, NY.
3. MS Excel, SPSS, and WarpPLS software
   applications.
4. Rencher, A.C. (1998), Multivariate Statistical
   Inference and Applications, John Wiley Sons,
   New York, NY.
5. SPSS web site www.spss.com.
6. WarpPLS software.

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