ONLY FOR ZEEK THE GREEK

profilerrmdushka
20160818025413preventive_care_hba1c_worksheet_a1.zip

20160818025357preventive_care_hba1c_study_description_survey_instrument.pdf

a Mock study and survey created by Dr. Helen Salisbury, PhD

DHSC9035 Data Collection for Applied Research Project

Study Description a

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 Survey a

Demographics

1) What is your age? 2) What is your sex? 3) Please indicate your income range:

< $20,000 $60,000-69,999

$20,000-29,999 $70,000-79,999

$30,000-39,999 $80,000-89,999

$40,000-49,999 $90,000-99,999

$50,000-59,999 > $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: ______

20160818025357preventive_care_hba1c_worksheet_a1_3_.docx

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

Scale

Sex

0 = Male

1 = Female

Income

Numeric

-99

Income_a

Participant income: Prefers not to answer

0 = Not checked

1 = Checked

Educ

8

Input

Race_a - Race_f

1

0

Race (White/Caucasian)

8

Right

Nominal

Input

RaceOthr

String

Other participant race (text)

Name

Type

Width

Decimals

Label

Values

Missing

Columns

Align

Measure

Role

RaceNoAn

Acc_Prev

NoPC_a - NoPC_f

PC08 - PC17

Numeric

0

1 = Strongly disagree

2 = Disagree

3 = Neither agree nor disagree

4 = Agree

5 = Strongly agree

8

Input

PCTotal

Total Preventive Care Score (Max = 50)

Right

Scale

HbA1c

TypeII

1

0

0 = No diagnosis

1 = Pre-diabetes

2 = Type II diabetes

Name

Type

Width

Decimals

Label

Values

Missing

Columns

Align

Measure

Role

Height

Participant height (in inches)

8

Weight

6

2

None

-999

SBP

DBP

Numeric

Diastolic blood pressure

(in mm Hg)

Right

20160818025402spss_part_i_instructions.pdf

© 2015 – A.T. Still University – Last Updated: July 19 2014

1

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

2

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).

20160818025410preventive_care_hba1c_worksheet_a1.docx

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 - Race_f

Numeric

1

8

0

2

Race (White/Caucasian)

None

None

8

Right

Nominal

Input

RaceOthr

String

8

0

Other participant race (“please specify”)

None

None

8

Right

Unknown

Input

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 - NoPC_f

Numeric

8

2

N/A

None

None

8

Right

Unknown

Input

PC08 - PC17

Numeric

8

0

N/A

1 = Strongly disagree

2 = Disagree

3 = Neither agree nor disagree

4 = Agree

5 = Strongly agree

None

8

Right

Unknown

Input

PCTotal

Numeric

8

2

Total Preventive Care Score (Max = 50)

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

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

20160818025413preventive_care_hba1c_worksheet_a1.docx

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 - Race_f

Numeric

1

8

0

2

Race (White/Caucasian)

None

None

8

Right

Nominal

Input

RaceOthr

String

8

0

Other participant race (“please specify”)

None

None

8

Right

Unknown

Input

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 - NoPC_f

Numeric

8

2

N/A

None

None

8

Right

Unknown

Input

PC08 - PC17

Numeric

8

0

N/A

1 = Strongly disagree

2 = Disagree

3 = Neither agree nor disagree

4 = Agree

5 = Strongly agree

None

8

Right

Unknown

Input

PCTotal

Numeric

8

2

Total Preventive Care Score (Max = 50)

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

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