{"id":56,"date":"2021-07-08T12:04:12","date_gmt":"2021-07-08T19:04:12","guid":{"rendered":"https:\/\/blogs.ubc.ca\/rhsrnbcresearchsupport\/?p=56"},"modified":"2021-07-08T12:12:37","modified_gmt":"2021-07-08T19:12:37","slug":"quantitative-research-methodologies-used-in-rural-health-research","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/rhsrnbcresearchsupport\/2021\/07\/08\/quantitative-research-methodologies-used-in-rural-health-research\/","title":{"rendered":"Quantitative research methodologies used in rural health research"},"content":{"rendered":"

Photo by \u4e01\u4ea6\u7136 on Unsplash<\/p>\n

By Sila Rogan and Elyse Tsang<\/p>\n

What is quantitative research?<\/h1>\n

Numbers are the hallmark of quantitative research. Quantitative research is characterized by collecting and analyzing numerical data generated through various statistical, computational, or mathematical methods. These quantitative findings can determine associations or causality between variables and can also be used to draw inferences about a wider population [1]. <\/strong>Broadly speaking, quantitative research design is categorized as descriptive or experimental.<\/p>\n

Descriptive quantitative research<\/em><\/h4>\n

The purpose of descriptive study is to describe a population through its characteristics, trends, and frequencies to name a few factors [2]. Descriptive research involves the observation and measurement of uncontrolled variables to determine the \u201cwhat\u201d component of a research topic, rather than the \u201cwhy\u201d.<\/p>\n

Experimental quantitative research<\/em><\/h4>\n

In experimental studies, researchers manipulate independent variables and measure their effects on dependent variables. In this way, experimental research aims to establish causality [1].<\/p>\n\n\n\n
Click here for more on descriptive<\/a> and experimental<\/a> quantitative research design.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Statistical analysis<\/h1>\n

Statistical analysis is an important tool that is performed to make meaningful interpretations of quantitative data. It helps us discover trends, patterns, and relationships in our findings which ultimately enables us to draw valid conclusions from our research.<\/p>\n

Descriptive quantitative methods<\/em><\/h4>\n

Descriptive statistics uses numerical summaries, such as central tendency, variability, and distribution, generated through various statistical tools to describe and understand a sample [3,4]. This type of quantitative analysis tends to be the most valuable in a rural health research context, as numerical summaries are useful for gathering political and public support for improving rural health services as well as developing more effective health interventions [5].<\/p>\n

Inferential quantitative methods<\/em><\/h4>\n

Inferential statistics establishes quantitative associations between two or more variables of interest and often involves drawing conclusions from a subset of the population before generalizing them to a larger population [6].<\/p>\n

For the most part, experimental studies and inferential statistics are not the best fit for rural health research as rural health is generally influenced by several dynamic external variables like socio-economic factors and geography which are not accounted for in an experimental setting. Furthermore, rural populations are incredibly diverse, so generalizations can be inaccurate<\/p>\n

Application of quantitative methods in rural health research<\/h1>\n

Study designs<\/h2>\n

Cross-sectional studies<\/h3>\n

Cross-sectional studies are conducted by collecting data from a population at a single moment in time [7]. The population under investigation is selected based on inclusion and exclusion criteria according to the study\u2019s goal. In this way, cross-sectional studies are considered descriptive as researchers do not manipulate the exposure or treatment variables. Common rural health applications for cross-sectional study design include population-based surveys and chart audits [7].<\/p>\n

Cohort studies<\/h3>\n

Unlike cross-sectional studies, cohort studies follow a population with a defining characteristic over an extended period of time\u2014typically for many years [8]. They are particularly effective at identifying risk factors for disease. Cohort studies may be prospective (carried out in the future) or retrospective (the examination of pre-existing data) in nature [9]. In terms of rural health, cohort studies are useful for making comparisons between rural and urban populations or comparing different rural populations to one another.<\/p>\n

\"\"<\/p>\n

Data analysis tools<\/h2>\n

Rural health indexes<\/h3>\n

Quantitative methods can be used to generate rural health indexes. These are measurement tools based on quantitative data analysis that can function as a proxy for various features of the health of rural communities [5]. They are useful for health services planning and provide a standardized way of comparing health characteristics across diverse communities [5]. Generally, these indexes must be accompanied by some form of qualitative data to contextualize the index within all the factors that influence the health of a particular community. That is, researchers should caution against using rural health indexes as an exclusive measure to define the health of a rural community.<\/p>\n

Examples of rural health indexes<\/em><\/h4>\n

The Rural Birth Index<\/a> (RBI)<\/p>\n

Using data on remoteness and population characteristics of rural communities, the RBI is an objective measure of need for maternity services and can theoretically be used to predict an appropriate and sustainable level of these maternity services in rural communities.<\/p>\n

General Practice Rurality Index<\/a><\/p>\n

This index can be used to measure how rural a physician\u2019s practice is, and accounts for several factors including remoteness from the nearest specialist, remoteness from an advanced referral centre, and presence of an acute care hospital.<\/p>\n

Geography-based tools<\/h3>\n

Geography has a large influence on the health of rural communities and rural health services planning. A combination of geographic and quantitative research methods can be used to develop important metrics and statistics for rural health. For example, RHSRNbc\u2019s catchment project<\/a> uses a geographic information system (ArcGIS) to map rural community catchments that are within a one-hour drive to the nearest hospital.<\/p>\n

Considerations when using quantitative methods in a rural health research setting<\/h1>\n