Interpreting Studies Quantitative and Qualitative Studies

INTERPRET RESEARCH STUDIES

Interpret Qualitative and Quantitative Research Studies

Research has become necessary to find information about things that have not been established truthfully after valid reasoning and experience. When an already available source of information is present, it becomes easier for a researcher to build rational results that are both valid and reliable. This paper aims at finding the difference between continuous and categorical variables, nominal data, and interval data. The next section highlights qualitative and quantitative studies and how they were carried out to understand both the categories of research studies better.

Part 1

Difference between continuous versus categorical variables

Continuous variables have an infinite numeric value between any two points of the values or numbers (Minitab, n.a.). For instance, a continuous variable can be a date or time between two days, such as a delivery parcel arrives on a specific day. On the other hand, the variables that do not have a logical order and are finite in number are called categorical variables. They can be categorized into different groups. For instance, gender, payment method, or ethnic group are some examples of categorical variables.

Difference between nominal data versus interval data

Nominal data is when the labeling of variables is done without prescribing them any quantitative value (Corporate Finance Institute, n.a.). It is considered the simplest type of measurement scale as it cannot be measured or cannot be put in an order. Interval data is when each value of the number is placed on a definite value on of the scale at equal intervals (Question Pro, n.a.). It is measured quantitatively with definite numbers since the value is assumed to be equal and standardized.

Part 2

Qualitative article

From the annotated bibliography, the selected article for the qualitative research study was presented by Courtin & Knapp (2015). The qualitative research technique was the review of literature presented by various authors before on the same issue. 11,736 articles were included in this study and the period of the review of this comprehensive literature spanned from July to September 2013. However, it was mentioned in the details of the study that out of these articles, 128 items from 15 countries was extracted for scoping review purpose. The two concepts, social isolation and loneliness, were focused on in these selected studies so that their effect on old age and mental and physical health in old age could be analyzed deeply.

The study results indicated that the previous literature that was included for studying loneliness and social isolation on the old age health revealed that the studies were mainly directed towards the US. Also, the studies were largely highlighting loneliness rather than social isolation. It was noted that after the effects of loneliness and somewhat the impacts of social isolation, the effects only resulted in cardiovascular diseases or depression. It was deducted that vast research was needed to extend the topic's research to other countries, rather than only focusing on the US population. Also, by keeping aside the bias of effects in cardiovascular problems and depression, the results of these two concepts should be studied otherwise.

The method used for collecting data in this study was based on the five-stage methodological framework put forward by Arksey and O'Malley in 2005. The main steps in this framework included determining the research question, selecting the studies relevant to the research question and the issue at hand, charting the data from the selected studies, and summarizing the results extracted from the previous literature. The keywords were recognized that were needed to find the relevant literature for the stated research questions. Three categories of keyword search were made: population target group, issue, and health outcome. The keywords for the population or target group were aged, aging, old people, older people, old age, elderly people, etc. The keywords entered for the issue category were isolation, loneliness, solitude, and social isolation. The keywords entered for the category of health outcome involved physical health, mental health, mental disorder, depression, etc. The papers' inclusion was also based on the criteria that they were written in the English language, and all formats of research designs were included. The studies that were excluded from these criteria were based outside Europe and the US, did not cater to the older population, did not mention physical and mental health, or did not narrate any loneliness or social isolation matter. The data analysis was done systematically since it was framed into a chart in which the studies from the years they were published and the number of those studies was mentioned. The highest number of studies was taken from the years 2013, 2012, 2011, and 2010, and the least number of studies was from the year 2003. Hence, the most recent data was included. An in-depth analysis was done in another table that showed variables, the number of studies that showed that variable, and the percentage of studies related to that variable. Another table was presented to show what variables were authored the most by the writers concerning that measure in what year the study was published.

Further analysis was done in the form of another table in which detailed mentioning of all the outcomes and the keywords was mentioned in one column. The related number of studies for that keyword was described in another column. A final table was given for analysis purposes that showed an overview of health outcomes' characteristics due to depression and cardiovascular health.

Quantitative article

No clear categorization of the study in the annotated bibliography was seen as a quantitative study since most of them were the literature review of the past studies. Many of them did not present full articles; hence, a quantitative study on aging is selected for this paper. The selected study was presented by Bahramnezhad et al. (2017), which was a cross-sectional study. The elderly people's age was 60 years and above, and the total number of participants was 201. The sampling was done from Iran in the year 2014 with a continuous and consecutive sampling method. The questionnaire used for this purpose was related to the quality of life; the data collection was done from Lubben social network scale and LEIPAD questionnaire. The identified variables were social network dimension and quality of life since their relationship was to be assessed. This study aimed to check whether there was a relationship between social networking and older people's quality of life. The variable named as social network dimension had factors like family, neighbors, and friends. This variable was collectively known as the categorical variable. The other variable, quality of life, which included physical functioning, self0care, anxiety, depression, mental health, social performance, etc., is also known as a categorical variable. The reason for categorizing both these variables as categorical is that they are not definite variables and could not be indicated with numerical values. They can be determined in groups, and thus, their respective factors were grouped for defining one variable. Moreover, they can be defined as interval data since they were given quantitative values from the statistical results such as correlation coefficient, Pearson coefficient, mean scores, etc. Each factor from the specific variable was given the specific value gained after running statistical tests on them so that the relationship between the two variables and their corresponding factors could be analyzed.

The data analysis was done using SPSS 16, descriptive statistics, independent t-sample tests, ANOVA, and Pearson coefficient. These tests showed that all the quality of life factors had a strong relationship with social networking attributes, except social performance. It was due to the reason that its Pearson coefficient value was low as compared to the other factors of that variable. The lowest relation was revealed between the quality of life and the social network in neighbors' form.