Descriptive Statistics Analysis

The provided assignment content discusses various concepts related to research methodology, including descriptive statistics, measurement scales, survey design, data validation, and non-response bias. The content highlights the importance of understanding the research problem, establishing detailed data requirements, identifying constructs, and selecting appropriate measurement scales. Additionally, it emphasizes the need for careful consideration when designing surveys, ensuring that questions are relevant to the research objective and do not contain filler questions. Furthermore, the content touches upon the importance of data validation, non-response bias, and addressing missing data.

1
2
3
4
5a)

The descriptive statistics and the histogram of the blood donation shows that the mean value of
the donated blood is 3.88. That is, on an average, 3.88 times blood has been donated by the total
population of study with the deviation of 2.315.

5b)
The descriptive statistics and graph of the data set for the gender has been brought together and it
was identified that the 47.06% of the individuals are male.

6a)
The descriptive statistics shows that the maximum score achieved by the student in the group is
20 and the mean score achieved is 16.23 with the standard deviation of 2.308. From the data it
can be stated that the overall proficiency is near to constant with the entire classroom.

6b)
The descriptive statistics and bar chart shows that the 68.2% of the students were looking at the
keyboard and the rest were not looking at the keyboard.

7a)
The descriptive statistics and histogram calculated here a show that the maximum units achieved
was 15 and the average value was 12.08 with the standard deviation of 2.148.

7b)
The descriptive statistics of the degree shows that the 30 were bachelor’s degree, 5 were from
Masters and the rest, that is, 2 were from the doctorate.

Short Answer
1. A concrete variable can be directly observed whereas an abstract construct must be indirectly
measured. An example of a concrete variable is a person's age or income. An example of an
abstract construct is a person's personality or intelligence.
2. The researcher can use the nominal scale, ordinal scale, as well as the interval scale. However,
the ratio scale cannot be used in this case. Given that the zero F temperature is not a true zero,
the scale can never be a ratio scale. For example, it will be erroneous to say that 64°F is twice as
warm as 32°F. To see the fallacy of this argument, simply convert these two temperature points
into degree Celsius and you will find that the argument falls apart.
3. Designing measurement scales requires (1) understanding the research problem, (2)
establishing detailed data requirements, (3) identifying and developing constructs, and (4)
selecting the appropriate measurement scale.
4. Descriptors used to express the two ends of the scale should be true bipolar ends of the scale.
The scale is not valid if bipolar ends lack the expression of extreme intensity associated with end
poles. The scale design might not allow for significant magnitudes to exist between two pole
descriptions.
5. If the construct under study is multidimensional, then these unique dimensions have to be
captured using a multiple-item scale. Also, multiple-item scales generally have more reliability
and validity compared to those of single-item scales.
6. The advantage is that the unstructured allows the researchers with more to explore the subject
under study. It also allows the respondents to bring in whatever seems helpful to the study. The
disadvantage is that it is time consuming and few respondents would spend time on this.
7. The questions that don’t answer the research objective or the questions that do not flow with
the content can be termed as bad questions. Asking filler questions more than the actual can be
time waster for respondents and the whole research.
8. A transition phrase can be used to prepare for answering personal questions and it can be used
to indicate that the task of completing the survey is almost over.
9. Response bias refers to the cognitive bias among the researchers while conducting the
research. Common method variance is the variance that is attributable to the measurement
method rather than to the constructs the measures are assumed to represent or equivalently as
systematic error variance shared among variables.
10. It helps in ensuring that the survey has been properly designed.
11. Data validation ensures that the clean, correct and useful data are brought in for the research.

12. Respondents are re-contacted if the researcher is sure that there something else respondents
can share or there are some information that might be wrongly filled.
13. Using the dummy data can be one of the methods. If the missing data are few, then the
researchers can get back to the respondents to fill those.
14. One-way tabulation helps in determining the degree of non-response, local blunders, locale
outliers, and calculating summary statistics.