demonstration projects final evaluation dataset july 28, 2011 special diabetes program for indians...
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Demonstration ProjectsFinal Evaluation Dataset
July 28, 2011
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Final Evaluation Dataset
Contents of the CD Database design and documentation Using Excel for basic functions Cautions when using Excel More advanced statistics Importing Excel data into SPSS/SAS Where to find help
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Contents of CD: Data
All data that were collected using the SDPI Demonstration Projects original data collection forms for all eligible participants
Up to 7/31/2009 for original participants
Up to 7/31/2010 for transition participants
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Contents of CD: Instruction Manual
Located on the CD and in the meeting binder Contents:
Subdirectory Structure Participant-Level Datasets and Documentation Grantee-Level Datasets and Documentation Analysis Conventions for Using Final
Evaluation Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Database Design and Documentation
Raw datasets
Constructed datasets
Data dictionaries and Variable indexes
Scale variable documentation
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Subdirectory Structure
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Raw Datasets Separate Excel dataset for each participant-level
and grantee-level data collection form Some forms contain multiple tabs in Excel file Contain responses to the original questions Variables were usually named using a combination
of form name and question number Examples
Participant’s gender on B1 form = B1DQ1 Provider’s professional background on AD1 form = AD1DQ1
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Raw Datasets Variables went through a thorough cleaning and
quality assurance process Checked for missing data Checked for outliers (out-of-range data) Checked for inconsistencies among variables Checked for inconsistencies in skip patterns
See the Instruction Manual for more specific information about the cleaning procedures used for each of the raw datasets.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Raw Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Raw Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Raw Datasets
See the Instruction Manual for more specific information about the contents of each of the raw datasets.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets Only exist for participant-level data
Constructed datasets should be sufficient for most data analysis on participant-level data
Contain frequently used variables
Contain renamed, derived, and scale variables created from raw datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets – Renamed Variables
Meaningful variable names indicate variable content
Examples B1DQ3_1, F1DQ3_1, and A1DQ3_1 have been
renamed “Weight” M1DQ1_1 has been renamed “FBG”
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets – Derived Variables
Re-categorized and re-coded variables, as well as some other variables created based on variables from the raw datasets Example
Emp4Cat is a derived variable for participant employment status that was re-classified into 4 categories based upon each participant’s response to B2DQ62, F2DQ41, and A2DQ54
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets – Scale Variables
Scale variables that were created based upon several items from the raw datasets Examples
The Alcohol Use Disorders Identification Test (AUDIT) Scales
Audit, Audit1, Audit2
Healthy and Unhealthy Diet Scales HealDiet, UnHeDiet
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Constructed Datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Unique Identifiers
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Variable Indexes
List of all the variable names and labels Found in the Data Dictionary subdirectory Available for only the following constructed
participant-level datasets: dpbaseline.xls dpfollowup.xls dpannual.xls dpmidyear.xls
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Data Dictionaries
Excel files corresponding to all datasets Found in the Data Dictionary subdirectories
for both the participant-level and grantee-level data
Some data dictionaries contain multiple tabs: I2.D, I3.D, AD1.D, I1.D, R3.D
Differ for raw versus constructed datasets
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Columns in Data Dictionaries for Both
Raw and Constructed Datasets Variable Name
- Raw: combination of form name and question #
- Constructed: meaningful, indicating content
Label (provides meaning of each variable)- Raw: actual question wording
- Constructed: includes “baseline only,” etc.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Columns in Data Dictionaries for Both
Raw and Constructed Datasets Options (value labels – meaning of each
numeric value for categorical variables)Example:
1 = Strongly Agree
2 = Agree
3 = Neither Agree nor Disagree
4 = Disagree
5 = Strongly Disagree
Type (data type of each variable)Examples: Numeric, Text, Date
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Additional Columns in Data Dictionaries for Constructed
Datasets Input Variable
- Variable(s) that was/were used to create each variable in the constructed dataset
Comments- Algorithm for how the variable was created
- Cautions to note when using a variable in data analysis
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Additional Columns in Data Dictionaries for Constructed
Datasets *Citation- Reference(s) for each scale variable
*Cronbach’s α- Cronbach’s alpha coefficient indicating the internal
consistency of the items used to create each scale variable
* Columns are only applicable for baseline datasets that have scale variables. Because follow-up and annual datasets have the same sets of scale variables as the baseline dataset, users should refer to the baseline data dictionary for
this information.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Scale Variable Documentation
Only applies to participant-level constructed datasets that include scale variables
Brief summaries of how each scale variable was created and the psychometric properties of each scale
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Recommendations for Using the Final Evaluation Datasets in Data
Analysis Use constructed datasets when available, before
going to the raw datasets folder
Use existing scale and derived variables in the constructed datasets, if possible (especially socio-demographic variables)
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Recommendations for Using the Final Evaluation Datasets in Data
Analysis Check for outliers that are out of normal ranges
(especially with regard to major outcome variables)
Check for timeline compliance of the measurements (especially at baseline)
Check for potential inconsistencies among variables
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Recommendations for Using the Final Evaluation Datasets in Data
Analysis When using an ordered categorical variable as a
continuous variable, check the linearity assumption
When using a dichotomous variable that was created from more than two levels originally, make sure that you are not losing important information by doing so
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Using Excel for Basic Functions Merging Data from two Excel Files Computing Descriptive Statistics
Count (N) Minimum Maximum Mean (average)
Using Selection Criteria Creating Bar Graphs
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Importing a Worksheet from another Excel file
Let’s say that you would like to compare data from baseline to data from follow-
up. In order to do this in Excel, you would need to merge the data from two
Excel files into one Excel file.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Open first Excel file; Right-click on the worksheet tab; Choose “Insert”
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose Worksheet; Click OK
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Right-click on “Sheet1” and choose Rename to change the name of the
worksheet
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Copy and paste in the data from the other Excel file into the newly
renamed worksheet
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Computing Descriptive Statistics
First, add one more worksheet to the newly combined Excel file using the procedures above
Calculate count (n), minimum, maximum, and mean (average) for a variable of interest (e.g., BMI) at both baseline and follow-up using all of your participants
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
In the new worksheet, add appropriate row and column
headers
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Add in the appropriate formulas to compute the descriptive statistics Count (n)
=COUNT(dpbaseline!AJ2:dpbaseline!AJ76)
=COUNT(dpfollowup!U2:dpfollowup!U38)
Minimum =MIN(dpbaseline!AJ2:dpbaseline!AJ76)
=MIN(dpfollowup!U2:dpfollowup!U38)
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Add in the appropriate formulas to compute the descriptive statistics Maximum
=MAX(dpbaseline!AJ2:dpbaseline!AJ76)
=MAX(dpfollowup!U2:dpfollowup!U38)
Mean (average) =AVERAGE(dpbaseline!AJ2:dpbaseline!AJ76)
=AVERAGE(dpfollowup!U2:dpfollowup!U38)
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check that resulting values make sense
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Using Selection Criteria to Compute Means by Gender
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Merge Cells for Gender Categories
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After merging gender category cells…
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Average IF function=AVERAGEIF (Range, Criteria, Average_range)
– Range - the group of cells the function is to search.
– Criteria - this value is compared with the data in the Range. If a match is found, then the corresponding data in the Average_range is averaged. Actual data or the cell reference to the data can be entered for this argument.
– Average_range (optional) - the data in this range of cells is averaged when matches are found between the Range and Criteria arguments. If the Average_range argument is omitted, the data matched in the Range argument is averaged instead.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Add in the appropriate formulas to compute the mean by gender and
timepoint=AVERAGEIF(dpbaseline!K2:dpbaseline!K76,1,dpbaseline!AJ2:dpbaseline!AJ76)
=AVERAGEIF(dpfollowup!F2:dpfollowup!F38,1,dpfollowup!U2:dpfollowup!U38)
=AVERAGEIF(dpbaseline!K2:dpbaseline!K76,0,dpbaseline!AJ2:dpbaseline!AJ76)
=AVERAGEIF(dpfollowup!F2:dpfollowup!F38,0,dpfollowup!U2:dpfollowup!U38)
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check that resulting values make sense
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Create Bar Graph:Highlight data cells; Use “Insert”
menu
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Review Graph
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Adjust Y-Axis Range
Double-click on y-axis to get
the dialogue box
to appear
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Delete Legend:Click on legend and press delete
key
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Delete Grid Lines
Double-click on
grid lines to get the dialogue box
to appear
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Change Fill Style
Double-click on one bar to get
the dialogue box
to appear
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Label Y-Axis:Click on graph border; Use “Layout”
menu
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Add Graph Title:Click on graph border; Use “Layout”
menu
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Review Final Graph; Can copy and paste graph into Word
document
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Cautions when using Excel
Data from the same participants are not really merged – comparisons between baseline and follow-up are based on data with different sample sizes
Missing data issues Filtering issues Copying and pasting formulas Graphing options
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
More Advanced Statistics Inferential Statistics
Example question: Is a change in a particular measurement (e.g., BMI) from baseline to follow-up in a particular sample statistically significant?
Techniques: Paired t-test, ANOVA, Regression, etc.
Specialized statistical software SPSS, SAS, Stata, etc.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Importing Excel into SPSS
Two methods
Menu-driven
Syntax
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Click File → Open → Data
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose “Excel” from Files of type;Browse to Excel file you want to
import; Click Open
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check Options; Check Worksheet name;
Check Worksheet range; Click OK
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check that all variables were imported; Check variable formats
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check that all cases were imported
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
SPSS Import Syntax
GET DATA /TYPE=XLS /FILE='C:\Users\dilled\Desktop\dpbaseline.xls‘ /SHEET=name 'dpbaseline‘ /CELLRANGE=full /READNAMES=on /ASSUMEDSTRWIDTH=32767.EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Importing Excel into SAS
Multiple methods
Import wizard
Proc import
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Click File → Import Data
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose “Microsoft Excel Workbook (*.xls, *.xlsx)”; Click Next
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Browse to the desired Excel file
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose worksheet from Excel file to import;
Click Options
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check options
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Click Next
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose library and SAS database name;
Click Next
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Choose name of syntax file to save (optional);Click Finish
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Check that all cases & all variables were imported; Check variable formats
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
PROC IMPORTproc import OUT= work.dp_baseline
DATAFILE= "C:\Users\dilled\Desktop\dpbaseline.xls"
DBMS=EXCEL REPLACE;
SHEET="dpbaseline";
GETNAMES=YES;
MIXED=YES;
USEDATE=YES;
SCANTIME=YES;
run;
proc contents data=dp_baseline;
run;
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Where to find help… For help with advanced statistical analyses, consider
contacting:
Local university
IHS service unit
Tribal planning office
SPECIAL DIABETES PROGRAM FOR INDIANS Diabetes Prevention Program Initiative: Year 1 Meeting 2
Questions?
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