DHSC9035 Data Collection for Applied Research Project Study Descriptiona Study purpose: To examine whether preventive care has an effect on developing Type II diabetes. Participants: 30 adults (18 – 65 years) who visit an urban medical clinic routinely Study Design: Quantitative descriptive Inclusion criteria:   Adults ages 18-65 HgA1c levels tested within the past 12 months Exclusion criteria:  Children < 18 years and adults > 65 years of age  Individuals who have not had their HgA1c levels tested within the past 12 months Methods: Participants were consecutively sampled who visited an urban medical clinic. The admitting nurse reviewed the patient charts to determine whether the patients had their HgA1c levels tested within the past year. Those meeting the inclusion criteria were asked if they would be willing to participate in a brief survey. Upon agreement, they were asked to sign an informed consent which was reviewed by the nurse. Data collection: Upon signing the Informed Consent, participants completed the demographic section and the 5-point Likert scale questions on the Preventive Care Survey.* Upon completion of the brief survey, the admitting nurse completed the bottom portion of the survey instrument, recording the participants' most recent height, weight, BP, and HgA1c levels, measured within the past 12 months. a Mock study and survey created by Dr. Helen Salisbury, PhD Preventive Care Surveya Demographics 1) What is your age? 2) What is your sex? 3) Please indicate your income range: < $20,000 $20,000-29,999 $30,000-39,999 $40,000-49,999 $50,000-59,999 $60,000-69,999 $70,000-79,999 $80,000-89,999 $90,000-99,999 > $100,000 Prefer not to answer 4) Please select your highest educational level attained: High School Community College Bachelor's Master's Doctorate 5) Race (select all that apply) White/Caucasian Black/African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander Other (please specify) Prefer not to answer 6) Have you accessed preventive services that are available through your provider? Yes No 7) If "no", please rank order the following reasons you have not taken advantage of the preventive services available (if N/A, leave blank). No insurance Not covered by my insurance Don't feel it's necessary Takes too much time Can't get out of work for appointments Physician doesn't emphasize this/think it's necessary a Mock study and survey created by Dr. Helen Salisbury, PhD Preventive Care Please select the degree to which you agree or disagree with the following statements. My healthcare provider: Strongly disagree Disagree Neither agree nor disagree Somewhat Agree Strongly Agree 8) recommends annual preventive visits 9) recommends biometric lab tests annually (e.g., lipid profile, HbA1c) 10) recommends imaging screenings at appropriate intervals 11) encourages me to exercise for 30 minutes 2-3 times per week 12) recommends I drink alcohol moderately 13) recommends I stop smoking or don't start 14) recommends I wear protective equipment when engaging in sports 15) recommends I follow a specific diet (e.g., Atkins, Weight Watchers, Mediterranean) 16) recommends I eat less red meat 17) recommends I consume more fresh fruit and vegetables Thank you for completing this survey! Please do not write below the line. ______________________________________________________________________ a Mock study and survey created by Dr. Helen Salisbury, PhD To be completed by medical staff only: HbA1c (latest test result within the past 12 months): ______% Height (in inches): ______ Weight (in pounds): ______ SBP: ______ DBP: ______ a Mock study and survey created by Dr. Helen Salisbury, PhD Preventive Care & HbA1c Variable View Worksheet (A1) Complete this SPSS® Variable View worksheet, ensuring every cell contains the appropriate alphanumeric response setting(s). Like a puzzle, some information is provided for you already. You must fill in the remainder. The recommendation is that you complete this worksheet prior to populating the Variable View tab in the assignment SPSS® dataset (PrevCare_HbA1c.sav). Here are the SPSS® Variable View column headings, with brief definitions/explanations:  Name (variable name). These have been provided for you and should not be changed.  Type (e.g., numeric, date, string)  Width* (The default is 8. Adjust as appropriate.)  Decimals (The default is 2. Decimals are only needed if the collected data points should logically be carried out to decimals.)  Label (Keep labels brief but informative. Labels assist with interpreting your output.)  Values (Used with nominal and ordinal variables. Nominal variables will have numbers assigned to the categories and ordinal variables will have numbers assigned to the rankings.)  Missing (Defines the numeric values that should be considered missing [e.g., -99] or out of range [e.g., -98])  Columns* (The default is 8. Adjust as appropriate.)  Align (Right is the default. You can adjust to your preferences, although consistency is advised.)  Measure (Scale is for interval/ratio variables; Ordinal for ordinal; Nominal for nominal. String variables ("text") are automatically defined as nominal.)  Role (The default is Input. You can retain this setting for all of your variables.) * Width vs. Columns: "Width" refers to the size of the variable. This should be set based on the largest size data point you are likely to obtain for a given variable. If your responses can range from "1" to "150", for example, your width would be "3". If your responses can range from "1.25" to "150.99", then you width would be "6". Decimals count as characters. Columns are not the same as Width, although they can be equal. Columns will determine how much of the variable name and accompanying data will be visible on the screen. For ease in "reading" your dataset, always be sure the full name of the variable is visible in Data View. Avoid presenting your dataset in a "squashed" manner as this is difficult to read. Consistency is recommended across your variables, though. Name Type Width Decimals Label Values Missing Columns Align Measure Role ID Numeri 2 0 Preventive Care Survey Participant ID # None None 8 Right Nominal Input c Age Scale Sex Income 0 = Male 1 = Female Numeri -99 c Income_a Participant income: Prefers not to answer Educ Race_a Race_f 0 = Not checked 1 = Checked 8 1 0 Race (White/Caucasian) 8 Input Right Nominal Input RaceOthr String Name Type Other participant race (text) Width Decimals Label Values Missing Columns Align Measure Role RaceNoAn Acc_Prev NoPC_a NoPC_f PC08 - Numeri PC17 c PCTotal HbA1c 0 1 = Strongly disagree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree Total Preventive Care Score (Max = 50) 8 Input Right Scale TypeII Name Type 1 0 Width Decimals Height 0 = No diagnosis 1 = Pre-diabetes 2 = Type II diabetes Label Values Missing Participant height (in inches) Weight 6 2 Columns Align 8 None -999 SBP DBP Numeri c Diastolic blood pressure (in mm Hg) Right Measure Role DHSC9035 Data Collection for Applied Research Project SPSS Instructions for SPSS Part I – Purchase, Installation, and Dataset Preparation Steps to Creating the Codebook (i.e., data dictionary) 1. Complete the Variable View Worksheet (Preventive Care & HbA1c Worksheet.docx) in preparation for completing the codebook in the SPSS® data file (PrevCare_HbA1c.sav) 2. Once the Worksheet is complete, open the SPSS ® data file and click on the “Variable View” tab to show the variable list. The Variable View is the codebook view in SPSS®. 3. Now complete the rows in the codebook using the completed Worksheet as a guide. Steps to Locating and Assigning the Missing Data Code to All Missing Data 1. Click on the “Data View” tab in the SPSS® data file. 2. Review the data to locate and assign the missing data code (-99) for all missing data. Missing Data © 2015 – A.T. Still University – Last Updated: July 19 2014 1 SPSS Instructions for SPSS Part I – Purchase, Installation, and Dataset Preparation DHSC9035 Data Collection for Applied Research Project 3. Now that all the missing data have been assigned the missing data code (i.e., -99), there should be no blank cells in your data sheet. Then, be certain to add "-.99" (with no quotation marks) as one of the “Missing” values in your codebook (Variable View) for that variable. Otherwise, when you analyze your data, SPSS® will incorrectly treat "-99" as a valid entry. For example, if you discover a blank cell for the variable “Age”, you would replace the missing value with “-99” and then add “-99” to the “Missing” column in the “Age” row within the codebook (Variable View). © 2015 – A.T. Still University – Last Updated: July 19 2014 2 Preventive Care & HbA1c Variable View Worksheet (A1) Complete this SPSS® Variable View worksheet, ensuring every cell contains the appropriate alphanumeric response setting(s). Like a puzzle, some information is provided for you already. You must fill in the remainder. The recommendation is that you complete this worksheet prior to populating the Variable View tab in the assignment SPSS® dataset (PrevCare_HbA1c.sav). Here are the SPSS® Variable View column headings, with brief definitions/explanations:  Name (variable name). These have been provided for you and should not be changed.  Type (e.g., numeric, date, string)  Width* (The default is 8. Adjust as appropriate.)  Decimals (The default is 2. Decimals are only needed if the collected data points should logically be carried out to decimals.)  Label (Keep labels brief but informative. Labels assist with interpreting your output.)  Values (Used with nominal and ordinal variables. Nominal variables will have numbers assigned to the categories and ordinal variables will have numbers assigned to the rankings.)  Missing (Defines the numeric values that should be considered missing [e.g., -99] or out of range [e.g., -98])  Columns* (The default is 8. Adjust as appropriate.)  Align (Right is the default. You can adjust to your preferences, although consistency is advised.)  Measure (Scale is for interval/ratio variables; Ordinal for ordinal; Nominal for nominal. String variables ("text") are automatically defined as nominal.)  Role (The default is Input. You can retain this setting for all of your variables.) * Width vs. Columns: "Width" refers to the size of the variable. This should be set based on the largest size data point you are likely to obtain for a given variable. If your responses can range from "1" to "150", for example, your width would be "3". If your responses can range from "1.25" to "150.99", then you width would be "6". Decimals count as characters. Columns are not the same as Width, although they can be equal. Columns will determine how much of the variable name and accompanying data will be visible on the screen. For ease in "reading" your dataset, always be sure the full name of the variable is visible in Data View. Avoid presenting your dataset in a "squashed" manner as this is difficult to read. Consistency is recommended across your variables, though. Name Type Width Decimals Label Values Missing Columns Align Measure Role ID Numeric 2 0 Preventive Care Survey Participant ID # None None 8 Right Nominal Input Age Numeric 8 2 N/A None None 8 Right Scale Input Sex Numeric 8 2 N/A 0 = Male 1 = Female None 8 Right Unknown Input Income Numeric 8 2 N/A None -99 8 Right Unknown Input Income_a Numeric 8 2 Participant income: Prefers not to answer 0 = Not checked 1 = Checked None 8 Right Unknown Input Educ Numeric 8 2 N/A None None 8 Right Unknown Input Race_a - Numeric 1 0 Race (White/Caucasian) None None 8 Right Nominal Input 8 2 8 0 Other participant race (“please specify”) None None 8 Right Unknown Input Race_f RaceOthr String Name Type Width Decimals Label Values Missing Columns Align Measure Role RaceNoAn Numeric 8 2 N/A None None 8 Right Unknown Input Acc_Prev Numeric 8 2 N/A None None 8 Right Unknown Input NoPC_a - Numeric 8 2 N/A None None 8 Right Unknown Input Numeric 8 0 N/A None 8 Right Unknown Input PCTotal Numeric 8 2 Total Preventive Care Score (Max = 50) 1 = Strongly disagree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree None None 8 Right Scale Input HbA1c Numeric 8 2 N/A None None 8 Right Unknown Input TypeII Numeric 1 0 N/A 0 = No diagnosis 1 = Pre-diabetes 2 = Type II diabetes None 8 Right Unknown Input NoPC_f PC08 PC17 Name Type Width Decimals Label Values Missing Columns Align Measure Role Height Numeric 8 2 Participant height (in inches) None None 8 Right Unknown Input Weight Numeric 6 2 N/A None -999 8 Right Unknown Input SBP Numeric 8 2 N/A None None 8 Right Unknown Input DBP Numeric 8 2 Diastolic blood pressure (in mm Hg) None None 8 Right Unknown Input
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