# Why do senior executives feel more comfortable relying on quantitative data than qualitative data? How might a qualitative research company lessen the senior-level executive’s skepticism?

Question
Describing quantitative data
Why do senior executives feel more comfortable relying on quantitative data than qualitative data? How might a qualitative research company lessen the senior-level executive’s skepticism?

2021-02-19
Step 1
Quantitative data:
-Quantitative data is the data received as a measure of a performance usually expressed in numbers.
-It is the data received from a systematical empirical investigation of a phenomena.
Qualitative data:
-Qualitative data is the information about the scenario or the environment. These are information which cannot be actually measured.
-These are data gathered by the scientific observation of a phenomena without a numerical record of information.
-It can be subjected to human bias or misconception.
Step 2
Reason why senior executives feel more comfortable relying on quantitative data than qualitative data:
-The senior executives present in a company are comfortable relying more on the quantitative data because, the chance of human errors are smaller in the quantitative data compared to qualitative data.
-Qualitative data can be subjected to bias as it is the nature of human beings. Quantitative data is measured with precision with the help of a well developed system.
-Hence, the senior executives who are bound to make important business decisions rely more on the quantitative data.
Step 3
Some of the ways a qualitative research company might lessen the senior-level executive’s skepticism:
-The methodology or the combination of methodologies chosen must be justified with proper illustrations.
-The methodology can be executed in a natural setting rather than a controlled environment.
-The data analysis process must be carefully structured and executed.
-The data can be compared across multiple sources and in a different context.

### Relevant Questions

Discuss the differences between quantitative and qualitative data, as well as the advantages and disadvantages of each. As a part of your response, describe one type of quantitative data.
Determine whether the data described below is quantitative of qualitative variable.
A. The data are quantitative because they don't count nor measure anything.
B. The data are quantitative because they consist of counts or measurements.
C. The data are qualitative because they don't count nor measure anything.
D. The data are qualitative because they consist of counts or measurements.
Show whether the following is quantitative or qualitative data
i) Gender of students at a college
ii) Weight of babies at a hospital
iii) Colour of sweets in a box
iv) Number of students in a class
v) Colour of sweets in a box
What is the relationship between a frequency or relativefrequency distribution of a quantitative data set and that of a qualitative data set?
In a study, you ask the subjects their gender. Is this data qualitative or quantitative?
In a study, you ask the subjects their age in years. Is this data qualitative or quantitative?
What is the difference between quantitative and qualitative data.
The difference between the qualitative and quantitative data with examples
What is distinguish between qualitative and quantitative data?

A random sample of $$n_1 = 14$$ winter days in Denver gave a sample mean pollution index $$x_1 = 43$$.
Previous studies show that $$\sigma_1 = 19$$.
For Englewood (a suburb of Denver), a random sample of $$n_2 = 12$$ winter days gave a sample mean pollution index of $$x_2 = 37$$.
Previous studies show that $$\sigma_2 = 13$$.
Assume the pollution index is normally distributed in both Englewood and Denver.
(a) State the null and alternate hypotheses.
$$H_0:\mu_1=\mu_2.\mu_1>\mu_2$$
$$H_0:\mu_1<\mu_2.\mu_1=\mu_2$$
$$H_0:\mu_1=\mu_2.\mu_1<\mu_2$$
$$H_0:\mu_1=\mu_2.\mu_1\neq\mu_2$$
(b) What sampling distribution will you use? What assumptions are you making? NKS The Student's t. We assume that both population distributions are approximately normal with known standard deviations.
The standard normal. We assume that both population distributions are approximately normal with unknown standard deviations.
The standard normal. We assume that both population distributions are approximately normal with known standard deviations.
The Student's t. We assume that both population distributions are approximately normal with unknown standard deviations.
(c) What is the value of the sample test statistic? Compute the corresponding z or t value as appropriate.
(Test the difference $$\mu_1 - \mu_2$$. Round your answer to two decimal places.) NKS (d) Find (or estimate) the P-value. (Round your answer to four decimal places.)
(e) Based on your answers in parts (i)−(iii), will you reject or fail to reject the null hypothesis? Are the data statistically significant at level \alpha?
At the $$\alpha = 0.01$$ level, we fail to reject the null hypothesis and conclude the data are not statistically significant.
At the $$\alpha = 0.01$$ level, we reject the null hypothesis and conclude the data are statistically significant.
At the $$\alpha = 0.01$$ level, we fail to reject the null hypothesis and conclude the data are statistically significant.
At the $$\alpha = 0.01$$ level, we reject the null hypothesis and conclude the data are not statistically significant.
(f) Interpret your conclusion in the context of the application.
Reject the null hypothesis, there is insufficient evidence that there is a difference in mean pollution index for Englewood and Denver.
Reject the null hypothesis, there is sufficient evidence that there is a difference in mean pollution index for Englewood and Denver.
Fail to reject the null hypothesis, there is insufficient evidence that there is a difference in mean pollution index for Englewood and Denver.
Fail to reject the null hypothesis, there is sufficient evidence that there is a difference in mean pollution index for Englewood and Denver. (g) Find a 99% confidence interval for
$$\mu_1 - \mu_2$$.