This article provides a sample solution essay on Quantitative Methods of Inquiry.
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Quantitative Methods of Inquiry
Quantitative methods of research emphasize on measurements of objects and statistical, mathematical, or numerical data (Flick, 2014). The data may be collected through questionnaires, polls, surveys, or the manipulation of pre-existing statistical data with the help of computational techniques (Blackstone, 2018). Quantitative research is focused on gathering numerical data and generalizing it across groups of people or when explaining a particular phenomenon (Flick, 2014). It emerged to quantify data. This method is specific in its surveying and experimentation because it is built upon existing theories. Quantitative research method begins with a statement and involves the formation of strategies of inquiry like experimental and surveys, and proceeds to the collection of data on the predetermined instruments, which yields statistical data (Flick, 2014).
There are various methods of quantitative inquiry. The main quantitative methods of inquiry are: correlational, descriptive, quasi-experimental, and experimental. The methods differ in the degree of control the researcher has on the experimental variables. Below is the description of the mentioned quantitative methods of inquiry.
Descriptive design: this type of quantitative inquiry seeks to describe the most current status of a phenomenon. In this case, the researcher needs not to begin with a hypothesis, but in case, one is developed after the collection of data. The collection of data is more observational (Blackstone, 2018).
Correlational design: this method of quantitative design seeks to explore the relationship between the variables with the use of statistical analysis. In this method, it does not aim for the cause and effect. Thus, it is more observational with relationship to data collection.
Experimental designs: this is also called true experimentation. It makes use of the scientific methods to establish the cause and effect, but not for the one being manipulated, which is the independent variable in this case. In this design, the dependent and the independent variable are collected and analyzed for the relationship (Mertens, 2014).
Quasi-experimental design: this is also called the casual-comparative design. In this method of inquiry, there is an attempt to establish a cause-effect relationship between two or more variables. In this case, the researcher doesn’t assign groups and does not manipulate the independent variable. There is the identification of the control, groups, and exposure to variables. There is a comparison of results from the groups that are not exposed to the variables (Nardi, 2018).
From the data, the mean, mode, and median of the discharges in all states in 2014 are 604175.2258, 49564, and 393002, respectively. The SPSS table is as below:
Statistics |
||
year2014 |
||
N |
Valid |
31 |
Missing |
2 |
|
Mean |
604175.2258 |
|
Median |
393002.0000 |
|
Mode |
49564.00a |
|
a. Multiple modes exist. The smallest value is shown |
The next question requires that the number of discharges in the years 2010, 2012, and 2015 in all states are compared. In this case, the mean will be of great help during the comparison. The result from the SPSS table is as below:
Statistics |
||||
|
The year 2010 |
The year 2012 |
The year 2015 |
|
N |
Valid |
31 |
29 |
32 |
Missing |
2 |
4 |
1 |
|
Mean |
756629.1290 |
643490.9310 |
593000.3750 |
|
Median |
478025.0000 |
473461.0000 |
387108.0000 |
|
Mode |
52965.00a |
52180.00a |
52755.00a |
|
Std. Deviation |
860411.47379 |
632443.48421 |
613848.00780 |
|
Minimum |
52965.00 |
52180.00 |
52755.00 |
|
Maximum |
3970921.00 |
2670520.00 |
2817621.00 |
|
Sum |
23455503.00 |
18661237.00 |
18976012.00 |
|
a. Multiple modes exist. The smallest value is shown |
From the table, it can be seen that there were more discharges in all the states in the year 2010 as compared to 2012 and 2015. The mean of the discharges for the years 2010, 2012, and 2015 are 756629.1290, 643490.9310, and 593000.3750, respectively. However, comparing 2012 and 2015, there were more discharges in 2012 (643490.9310) than in the year 2015(593000.3750). The maximum values also support this for the three years, where the maximum value in 2010 is higher for the three years. The maximum value for the year 2012 is even higher than that of 2015. The maximum discharges for the years 2010, 2012, and 2015 are 3970921, 2870520, and 2817621, respectively. Significantly, there are fewer discharges in the year 2015 than in the year 2010. This is supported by the sum of the discharges, which are 23455503 (2010) and 18976012 (2015).
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Lastly, the number of discharges for the year 2011 for different states. The following results are obtained from SPSS:
Statistics |
||||||
|
North-western |
Southwestern |
Central |
North-eastern |
South-eastern |
|
N |
Valid |
5 |
6 |
7 |
8 |
5 |
Missing |
28 |
27 |
26 |
25 |
28 |
|
Mean |
518367.6000 |
889662.6667 |
477005.0000 |
755602.2500 |
1134405.0000 |
|
Median |
634750.0000 |
287955.5000 |
389828.0000 |
600633.5000 |
1073083.0000 |
|
Minimum |
134824.00 |
206720.00 |
33757.00 |
52214.00 |
293325.00 |
|
Maximum |
801982.00 |
3933239.00 |
1269145.00 |
2578680.00 |
2656249.00 |
|
Sum |
2591838.00 |
5337976.00 |
3339035.00 |
6044818.00 |
5672025.00 |
From the table, the discharges varied asper the region. The highest discharge was experienced in the Northeastern (6044818) region, followed by southeastern (5672025), southwestern (5337976, and central (3339035). The least discharge was experienced in the northwestern region (2591838) of the country.
Summary
Various techniques can be used in quantitative research. Quantitative methods put more emphasis on the measurements of objects and statistical, mathematical, or numerical data. There are three broad classifications of the quantitative method of research: casual, experimental, and descriptive. As seen in this paper, SPSS can be used in quantitative research, where it can assist in the statistical analysis of the data. The results obtained from SPSS can, after that, be analyzed and inferences made.
One of the determinants in healthcare deliverance is the knowledge, beliefs, attitudes, and experiences of healthcare professionals. This helps in determining both the procedures and the behavior of health care professionals while treating and evaluating patients. While treating patients, there can be a need for data analysis about a particular disease affecting a population. In this case, the nurses are required to have skills in data analysis, including the appropriate quantitative methods of inquiry to use. This is why all medical practitioners are required to have a vast knowledge of data analysis, which will help them in such scenarios.
References
Blackstone, A. (2018). Principles of sociological inquiry: Qualitative and quantitative methods.
Flick, U. (2014). The SAGE handbook of qualitative data analysis. London, UK: SAGE. doi:10.4135/9781446282243
Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Sage publications.
Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods. Routledge.
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