Quantitative and Qualitative Data Analysis: Describing the Type of Data Used

Qualitative and Quantitative Data Analysis
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Section 1: List of Variables
The data collected from 300 study participants covered 15 major variables that had varying levels of measurements.
Gender is binary and the possible values that can be used to label a participant is either male or female.
Race is a nominal data that categorizes the participants into distinct categories without a natural order. It is collected using the values, “African Americans,” “Whites,” Asian American,” “Native Americans,” “Hispanics,” and “Others.” Bayoudh, Miclet, and Delhay (2007) describe nominal data as a data type that lack natural ordering.
Age is a continuous variable and is collected in numerical values that can range from zero to infinity. Volkovs et al. (2014) describes continuous data as the type of data that can be expressed in a combination of whole numbers and decimal numbers.
Salary is a continuous variable and is collected in numerical values that can range from zero to infinity.
Education is ordinal data type that codes for the level of education attained by a study participant using values “high school,” “professional,” “college” and “Masters” Ordinal data describes qualitative data made up of categories with a natural ordering (Jakobsson & Westergren, 2005).
Height is a continuous data collected using values ranging from zero to infinity.
Weight is continuous data collected using value ranging from zero to infinity.
Body Mass Index (BMI) is a continuous data collected using value ranging from zero to infinity.
Diabetes status is a binary data type that can be described using the values “yes” or “no.”
Allergy status is a binary data type that can be described using the values “yes” or “no.”
Family history of diabetes (family hx diabetes) status is binary data type that can be described using the values “yes” or “no.”
Family history of allergies (family hx allergies) status binary data type that can be described using the values “yes” or “no.”
“To feel depressed during winter” is a discrete variable in the sense that the participant can only give a whole number among the specified options (1, 2, 3, 4, or 5). Discrete data type is a continuous data type that uses only whole numbers (Wilson, 2014)
“To exercise during summer” discrete variable in the sense that the participant can only give a whole number among the specified options (1, 2, 3, 4, or 5)
“To overeat when stressed out” discrete variable in the sense that the participant can only give a whole number among the specified options (1, 2, 3, 4, or 5).
Section 2: Descriptive Statistics
Age
Of the 300 participants, 53 percent of them were female translating to 159 of the participants. Male participants were 47 percent, which translates to 47 percent of the participants.
Gender

Count

Percentage

female

159

53.00%

male

141

47.00%

Grand Total

300

100.00%

Race
Majority of the study participants were White at 54.67 percent. The percentage represents 164 out of the 300 participants. Native Americans was the least represented race at 3 percent. African American made up 11.67 percent of the participants while Hispanic made up 18.33 percent of the data.
Race

Count

Percentage

African American

35

11.67%

Asian American

21

7.00%

Hispanic

55

18.33%

Native American

9

3.00%

other

16

5.33%

white

164

54.67%

Grand Total

300

100.00%

Age
The mean age of the study participants was 50.401 years (SD= 20.019), with 50.095 being the median age. The modal age was 18. Age of the participants ranged from 18 to 90.5 years.
age

Mean

50.401

Standard Error

1.155807023

Median

50.095

Mode

18

Standard Deviation

20.01916487

Sample Variance

400.7669622

Kurtosis

-1.13492229

Skewness

0.084737663

Range

72.5

Minimum

18

Maximum

90.5

Sum

15120.3

Count

300



Salary
The mean salary of the study participants was US$ 54,498.02 (SD= 28293.782), with US$ 50,012 being the median salary. The modal salary was US$ 15,000. The salaries of the participants ranged from US$ 10,123 to US$ 117,878.
Salary

Mean

54498.02

Standard Error

1669.915366

Median

50012

Mode

15000

Standard Deviation

28923.78258

Sample Variance

836585198.7

Kurtosis

-0.91327143

Skewness

0.406850746

Range

107755

Minimum

10123

Maximum

117878

Sum

16349406

Count

300



Education
Majority of the study participants were college educated (33.67 %), closely followed by participants with Master’s education (30%). Only 46 participants reported finishing their education at high school level (15.33%), with 21 percent reporting that they acquired professional education.
Education Level

Count

Percentage

college

101

33.67%

high school

46

15.33%

masters

90

30.00%

professional

63

21.00%

Grand Total
...