The role of qualitative and quantitative research methods

¶ … new knowledge on which decisions and actions can be founded (Hughes & Hayhoe 2007). Here, new knowledge is created through the collection and analysis of observations in a systematic way. Further, research concerns itself with the articulation of truths (generalized) from given instances. For instance, research on reader behavior looks at the behavior of readers in general and not a particular reader's behavior in isolation.

Basically, qualitative research concerns itself with clarifying human experience. Below are examples in which case qualitative research comes in handy as far as human experience is concerned.

Example 1: A study of how human beings relate with each other from the ages of 18 to 30.

Example 2: A study of how culture impacts on the performance of school going children.

Example 3: A study of the relationship between monetary compensation and employee motivation

Example 4: A study of the relationship between upbringing and child delinquency

Example 5: A study of how gay tendencies impact on an individual's interaction with members of the opposite sex.

Question 3

The process in this case (in relation to question 2) would be conduction of interviews which are categorized as a type of non-numerical data on which qualitative methods rely on. Therefore, interviews can be used as the source data while seeking to identify the meaning of experiences humans undergo.

Question 4

While inductive reasoning is mainly used in qualitative research, quantitative research makes use of deductive reasoning (Kumar 2011). That is, in quantitative research, the focus generally zeroes in on the specific rather than the general. However, in qualitative research, larger theories are inferred to by the use of small observations.

Question 6

In the research question given in question 5, two variable types can be identified. These include the dependent and the independent variables. According to Hughes & Hayhoe (2007), the independent variable includes the intervention to be tested while the dependent variable essentially comprises of the result to be measured. In the research question outlined in question 5, the independent variable is caring for a pet. On the other hand, we have the dependent variable being the lifespan of a person over 65 to 75. According to McBurney and White (2009), the effects of the independent variable are reflected or mirrored on the dependent variable. In our case, caring for a pet which is the dependent variable can be said to be a measure of the behavior of the subject.

Question 7

The approach that takes into consideration the idea from all its perspectives by exploring literature extensively is known as literature review. Literature review is considered a preliminary task that helps individuals to familiarize themselves with the existing body of knowledge regarding an issue at hand (Kumar 2011).

Question 8

Basically, the null hypothesis provides a framework under which the study inferences can be reported. When it comes to errors, two kinds of the same can occur (Kumar 2011). The first error known as Type I error denotes a situation where there is rejection of a null hypothesis despite being true while the second error known as Type II error takes place when there is an acceptance of a null hypothesis despite the same being false. According to Hughes & Hayhoe (2007), a null hypothesis can be rejected or accepted on the basis of its p value.

Question 9

For the research question given in question 5, the hypothesis set to be disapproved will constitute the null hypothesis. Hence the null hypothesis (H0) in this case will be:

"Caring for a cat pet does not prolong life of a person over 65 to 75 living alone in the Northwestern region of the U.S.A."

In the event that the null hypothesis is disapproved after the conduction of investigations, then it shall be necessary to construct another null hypothesis.

Question 10

When it comes to either accepting or rejecting the hull hypothesis, the p value will come in handy. In this case, should the p value happen to be higher than or equal to 0.1 (which is essentially the significant level), then I will not reject the null hypothesis. On the other hand, if the p value happens to be below 0.1, this will form the basis for my rejection of the null hypothesis.