quantitative analysis research question

Assignment # (add # here) Quantitative Analysis

Student Name

University of Maryland University College

HMGT400

Professor:

Date

Introduction

A short introduction not more than one or two paragraphs

Method

Hypothesis:

The main research question is that:

Does the nature of a hospital being a critical or non-critical access hospital influence parameters such as the average length-of-stay? For this research question the hypothesis is:

There is no significant difference on average length-of-stay between critical and non-critical access hospitals.

Data

The primary source of data for this research is Minnesota Hospitals Admissions by Care Unit of the Hospital, Fiscal Year 2012 (Health Care Cost Information System, n.d.). The Minnesota Health Care Cost Information System (HCCIS) releases this data set to provide accurate and reliable information about the financial, utilization, and service characteristics of hospitals in Minnesota.

More specifically, this data set provides information on hospital characteristics, such as: affiliations, counties, critical access hospital status, number of available beds, admissions to acute care units, managed and non-managed care admissions, and the average length-of-stay. This data set included 145 observations for 25 different variables for all hospitals in the state of Minnesota, USA, for the fiscal year 2012.

Materials and Method

Variable

The variables in play to answer this research question is the average length-of-stay. The other variable used was CAH which was meant to categorize the hospitals into critical and non-critical access hospitals (See Table 1).

Table 1. List of Variables Used for the Statistical Analysis

Source: Department of Health Minnesota, 2012

Method

To formulate an answer to this question and to test this hypothesis the study uses a descriptive quantitative research method. Based on the nature of our data, the most appropriate statistical test would be an independent sample t-test since these two samples are numerical and have no relationship. The data source provides parameters such as the average length-of-stay and a categorical variable. These samples are categorized into critical and non-critical access hospitals with critical access hospitals being represented by “Yes” and non-critical access hospitals being represented by “No” in the CAH column.

Data analysis process

The next step is the data analysis process. The analysis of statistical data required systematic tools and processes to be conducted. The statistical package used for our analysis was RStudio. We were able to create the codes that would execute an unpaired independent sample t-test. “One of the most common tests in statistics is the t-test, used to determine whether the means of two groups are equal. The assumption for the test is that both groups are sampled from normal distributions with equal variances. The null hypothesis is that the two means are equal, and the alternative is that they are not” (Spector, 2014).

Results

The analysis focused on the associations between the average length-of-stay and the status of a hospital as a critical access hospital. Figure 1 shows that the average length-of-stay, the distributions have a tail on the right side that most probably correspond to a set of outliers. After we removed the outliers from the data set, the final fit to the remaining data yielded an almost normal distribution for both variables. Therefore, the use of the unpaired t-test for equality of means is justified.

Of the 145 hospitals providing acute care in the state of Minnesota, 78 (53.8%) were designated as CAHs. The mean for acute care admissions for CAHs was lower (550±535), when compared with admissions for non-CAHs (7895±9690). See Table 2 for more details.

Table 2. Descriptive Analysis of LOS in Critical Access and Non-Critical Access Hospitals

Source: Authors findings using Mn data, 2012

After applying a two-sample t-test to our data, we found that the mean for acute care admissions in CAHs differs significantly (p<0.0000) from the mean for non-CAHs. It is important to notice that p-values less than the significance level (0.05) for the variables of interest are evidence against our null hypothesis stating that there is no difference in between the parameters for CAHs and non-CAHs.

Box-plots were generated to visually represent the data. Figure 2 shows you are more likely to have fewer acute care admissions in CAHs when compared to non-CAHs. Similarly, Figure 2b shows that patients in non-CAHs are more likely to have longer stays than their counterpart CAHs.

Discussion

According to data analysis, there is enough evidence to support our research hypothesis that non-CAHs are likely to have more longer average lengths of stay of patients compared to CAHs. Nevertheless, it is important to notice that this correlation does not imply causation. Our study found that the average LOS for CAHs in Minnesota is lower than for non-CAHs.

In this study, CAHs showed reductions in long term services, compared to non-CAH facilities that affected the number of patients treated at these facilities that contributed to overall admissions and, thus, length-of-stay.

Limitation: As in any other study, our report is also subject to limitations. First, unpaired t-test relies on the assumption that the data from the two samples are both normally distributed. Second, further analysis needs to be done on the relationship between critical care and length-of-stay for patients and age, as well as the many other predictor variables that could potentially present a relationship with our response variable.

Conclusion and Policy Recommendations

Write a paragraph and conclude your findings and add one or two policy recommendation

References

Critical Access Hospitals - Rural Health Information Hub. (n.d.). Retrieved September 23, 2018, from https://www.ruralhealthinfo.org/topics/critical-access-hospitals#location-requirements

Spector, R. (2014). Using t-tests in R. University of California, Berkeley. Department of statistics. Retrieved September 23, 2018 from https://statistics.berkeley.edu/computing/r-t-tests