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Quantitative Methods in Scientific Inquiries

Descriptive Research

The first type of quantitative methods is descriptive research which aims at describing the current condition of a variable identified by the researcher. Usually, these studies are conducted to gather and systematize information about a certain phenomenon (Boeren, 2018). Prior to the beginning of the research, scientists do not form a hypothesis, developing the educated guess after collecting the first set of data. With close analysis and synthesis of the information, researchers derive important conclusions for further studies (Boeren, 2018). In the process, a great extent of selectivity and careful measurement of variables is used to finalize a set of numerical records.

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Correlational Research

The second type of quantitative methods frequently used in clinical setting is correlational research. The central objective of the study is to identify the extent of a relationship (if any) between variables on the basis of statistical data (Clankie & Mima, 2012). To accomplish this, researchers look closely at associations among a number of facts and seek interpretations explaining their relationships. Correlational research is frequently utilized to determine trends and patterns in data. Yet, it is important not to confuse established correlation with the causation of the observed patterns (Clankie & Mima, 2012). Correlational research is more observational in nature and does not have capacity to establish cause-effect.

Causal comparative

Quasi-experimental Research

Unlike correlational study, quasi-experimental research tries to identify cause-effect relationships among two and more determined variables. On the scale of quantitative inquiries, this method falls between correlational research and experiment, possessing elements of both. The scientist identifies the independent variable as in the experiment but does not manipulate it, only measuring its effects on the dependent variable (Noyes et al., 2019). Another distinct characteristic from a true experiment is sampling of the groups for the study. In causal comparative study, the researcher does not randomly assign groups but uses pre-existing or naturally formed sets (Noyes et al., 2019). To control the research, quasi-experimental study uses control groups without any treatment.

Experimental Research

The last method in quantitative inquiries is experimental research, simply known as true experiment. This type of study utilizes scientific method and aims at establishing the cause-effect relationship among two and more variables identified for the study (Smcilquham, 2012). Unlike in the quasi-experimental method, the researcher imposes deliberate control over all the variables except one – the independent variable. The independent variable is then manipulated; its effect on the dependent variables is measured (Smcilquham, 2012). Participants are assigned randomly to avoid personal bias, and control groups are used as in the causal comparative research.

Discharges by State in 2014


As per 2014, the arithmetic mean was roughly 604,175.22580645, the closest number for discharges from Wisconsin. This statistical measure appears relatively accurate considering the quantitative estimate of the numerical distribution on the scale. Unfortunately, the quantitative data presented does not account for geographical specialties of the location, disregarding the sizes and overall population of the states.


As per 2014, the median was 393, 002, the number of discharges from Arkansas. It is important to consider that some states did not submit the data for review; therefore, the calculated statistical measure may not be fully accurate. Another factor to take into account is that the median found differs drastically from the lowest and highest numbers of discharges per state, raising an alarming question for the reasoning behind such statistical variations. Yet, the nature of the quantitative nature does not allow for qualitative content analysis and deriving of the phenomenological themes for the establishment of the cause-effect relationship (Schreier, 2013).


The data presented does not have an arithmetic mode with the precise reoccurring number. Yet, if rounded to hundreds of thousands, the most frequent number was 300, 000, repeating 4 times among the states that presented the data for review.

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Comparison of Number of Discharges in 2010, 2012, and 2015

Statistically speaking, the number of discharges in 2010, 2012, and 2015 did not change dramatically. The first interesting tendency which is worth to note is that the rate of discharges in some states, like AR, AZ, or GA, gradually fell, starting from 2010. While, in some states, like FL, HI, or MA, increased, up till 2015. The numerical data does not show surprising fluctuations, presenting stable, gradual drops or rises of the number of discharges based on the individual states. More patterns arise when assessing the geographical distribution rather than evaluating the picture overall. Based on the quantitative estimate, the number of discharges in 2010 was higher than in 2015 with the exception of some states, like NV or UT. Differences in the number of discharges over the course of past five years and standard deviated alternations should not be considered statistically crucial.

Comparison of Number of Discharges in 2011

In terms of northwestern states, there was the same number of discharges in WA and WY (648,079). According to Healthcare Cost and Utilization Project (2017), OR had 372,203, while MN fell in between the five states with 597,645 people discharged in 2011. No data is available for ID. CA was the leader of southwestern states with 3,933,239, followed by AZ with 801,982. NV, UT, and NM had estimated the same rate: 295,081; 274,576; 209,073 respectively (HCUP, 2017). TX and OK did not provide data for analysis. Many central states also miss from the statistical data: there is no information for KN, IL, and ND. As highlighted by HCUP (2017), the highest number of discharges is in MI – 1,269,145, while the lowest is in SD – 104,101. MN and WI had similar rates as per 2011: 597,645 and 628,134.

Based on the analysis of data from HCUP (2017), similar to MI in the center, NC and GA have 1,111,961 and 1,073,083 discharges documented. FL leads the ranking of southeastern states with 2,656,249 incidents. SC has less than half of the discharges in NC and GA: 537,407; while WV constitutes only 1/8 of the FL rate: 293,325. VI did not provide data for analysis. Finally, in the northeastern states, MA has 849,997 cases, while WV constitutes 293,325 incidents. VT has the lowest number of discharges – 52,214, and NJ has the highest – 1,069,663 (HCUP, 2017). The rest of the states fall in the middle of the standard distribution.


Quantitative inquiries include four main methods: 1) descriptive; 2) correlational; 3) causal comparative; 4) experimental. As per 2014, the arithmetic mean of discharges per state was roughly 604,175.22580645, while the median was calculated as 393, 002. The data did not have an arithmetic mode, yet the most reoccurring number was 300,000 based on the information presented in the database. Over the course of past five years (2010-2015), the data did not show crucial statistic alternations and fluctuations, demonstrating a stable gradual drop in some states, like AR, AZ, or GA. On the contrary, FL, HI, or MA slightly increased their number of discharges. Quantitative estimate suggests that the number of discharges was higher in 2010 with exceptions spotted in NV and UT. Obvious leaders in terms of discharges as per 2011 were FL, CA, MN, NC, and GA. The lowest number of incidents was spotted in VT.


Boeren, E. (2018). The methodological underdog: A review of quantitative research in the key adult education journals. Adult Education Quarterly, 68(1), 63–79. Web.

Clankie, S., & Mima, T. (2012). A brief comparison of qualitative and quantitative research methods. Web.

Healthcare Cost and Utilization Project. (2017). HCUP state inpatient databases (SID) file composition – number of discharges by year. Web.

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Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Ö., & Shakibazadeh, E. (2019). Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: Clarifying the purposes, designs and outlining some methods. BMJ global health, 4(1), 1-14. Web.

Schreier, M. (2013). Qualitative content analysis. The SAGE Handbook of Qualitative Data Analysis, 170-183. Web.

Smcilquham. (2012). Quantitative vs qualitative research. Web.

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