Module 2 Session 06 by e51r8Xi

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									Data processing and
          exporting
       Module 2 Session 6




                            1
Overview
   The next slide again shows the data
    management cycle.
   Data have been entered (and checked) in Epi
    Info. We are now ready to undertake data
    analysis. Do we remain in Epi Info, or move to
    another software?
   This session explores both:
     simple data analysis in Epi Info and

     exporting data – so that analysis can be
       undertaken in another software if required.   2
 Data management cycle
                   Design           Enumerators collect
                 questionnaire        data in the field


            Design
            survey                           Manual checking,
Conception                                     editing etc.
      Reporting of results


                                          Data entered
We are now        Data                   onto computer
ready to         analysis
move on to the
data analysis
stage
                      Computer data management                  3
Contents

   Using Epi Info to merge data from
    separate files
   Simple summaries, tables and graphs
    in Epi Info
   Exporting data from Epi Info to use
    with other programmes



                                          4
Learning Objectives

At the end of this session participants will be able to:

   merge data from separate data files
   use Epi-Info to produce tables, graphs and
    summary statistics and interpret the results
   export data from Epi Info for reading into another
    software package.



                                                         5
Merging Data
   There are three common types of merging:

       Top to Bottom – Adding records – Merge

       Side to Side – Adding variables – Relate

       Table Lookup – Data at different levels - Relate



                                                           6
Top to Bottom Merge
   Used when data files      ID   Var1   Var2   var3
                              01   24     54     62
    have the same
                              02   32     54     14
    variables but different
                              03   54     24     35
    records
   Used to combine data
    entered by different
    data entry staff          ID   Var1   Var2   var3
   For example: A enters     04   35     45     12
    records 1 to 10, B        05   64     74     25
    enters records 11 to 20   06   54     54     65

                                                        7
Side to Side Merge

    ID   Health1   Health2          ID   Educ1   Educ2
    01     02        03             01    34      45
    02     04        05             02    71      55
    03     14        24             03    62      34


   Used when data files have same records but different
    variables
   Each file should have key field(s) to ensure correct
    merging
   For example: Person A enters Health data, Person B
    enters Education data
                                                           8
Table Lookup Merge
HHID    ID   GENDER   AGE
10001   01     01     51     HHID    WALL   ROOF   FLOOR
10001   02     02     48     10001    02     03     01
10001   03     01     20     10002    03     03     01
10002   01     02     84     10003    04     05     01
10003   01     01     56
10003   02     02     51

   Used for data at different levels
   For example: Household data and data on
    individuals within the household
   Household ID must appear in Individuals dataset
                                                           9
Activity 2
   The first part of the practical takes you
    through the process of a side-to-side merge
   An example of the Table Lookup merge
    appears in Activity 4
   The top-to-bottom merge is a much simpler
    and more intuitive process so is not included
    in this practical


                                                    10
Data Checking with Analysis
   Initial data analyses can be part of the data
    checking process
   Useful to check on spellings and ranges –
    e.g. are all ages feasible?
   Useful to have ability to produce simple
    tables and charts from the data entry
    package
   Corrections then made in the same package

                                                    11
Data Analysis in Epi Info
   The Analyze Data utility in Epi Info produces
    tables, graphs and summary statistics.
   The relevant commands are:
     Frequencies : 1-way tables

     Tables : cross-tabulations (2-way tables)

     Means: mean values

     Graph : graphs and charts

     Summarize: summary statistics

                                                    12
Frequencies (FREQ command)
   is used for 1-way tables




                               13
Tables
   is used for cross-tabulations




                                    14
Means
   produces mean, median, minimum,
    maximum, quartiles, standard deviation.




                                              15
Graph
   Offers a wide choice of graphs




                                     16
Example Bar Chart




                    17
Labelling values
   When data have been entered as numeric
    codes the graphs do not give much
    information
   To label the value we first define a new text
    variable
   Then we recode the existing numeric variable
    into the new variable


                                                18
Define and Recode




                    19
Revised Bar Chart




                    20
Activity 4
   For your dataset, generate:
       Frequencies for the categorical variables
       Means for the continuous variables
       Cross-tabulations
       Graphs
   The results are copied to a Word file to be
    used in a report (sessions 10 & 11)


                                                    21
Moving out of Epi Info
   The Write (Export) command within Analyze
    Data exports data from Epi Info.




                                                22
Options on the Export
   When exporting to Excel the options are Excel
    3.0 or Excel 4.0
   These are earlier versions of Excel – only one
    worksheet per file – but they can be read by the
    later versions of Excel
   You can choose which variables to export
   Note Epi-Info gives no indication that the export
    has been done – re-running the command will
    append the same data to the worksheet!
                                                        23
Variable and Value Labels
   Variable labels in
    Epi-Info are the
    prompts or
    questions
   Value labels can
    only be done by
    recoding into a
    different variable


                            24
Labels in Exported file
   In resulting Excel file variable names are
    used as column headings
   Text fields come across too




                                                 25
Vlookup Function in Excel
   Alternatively export only the numeric codes
    and use Vlookup in Excel for labels
   Codes are stored in a separate worksheet
   =vlookup(C2, Codes!$A$2:$B$8, 2, FALSE)
   Advantage is that codes are synchronised
    with labels



                                                  26
Vlookup Example




                  27
Activity 6 (Optional)
   Export some of the data you have been
    working on into Excel
   Try to use the vlookup function to label the
    coded variables
   For more information about the Vlookup
    function see Chapter 5 – Multi-level Data of
    SSC Introduction to data handling in Excel –
    SADC version

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