Quantitative Research for Chaplains and Health Care Professionals

A Primer

Kevin J. Flannelly, Laura T. Flannelly, Katherine R.B. Jankowski

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9780367583255
• eBook ISBN: 9780429788758
• Edition: 1
• Topic: Medicine
• Subtopic: Health Care Delivery

This book takes readers from very basic research concepts, such as 'causality' and 'variables', to the application of different types of statistical analyses. The first two chapters introduce the scientific method and causality, and assess the degree to which the major types of research designs used in health care studies allow researchers to make causal inferences. The book concludes with a detailed description of the seven critical factors that must be controlled to draw causal inferences from experimental studies. The rest of the book covers levels of measurement, i.e. nominal, ordinal, interval, and ratio scales; operational definitions; risk factors, independent and dependent variables, and other kinds of variables; how to calculate and interpret measures of central tendency and variability; the normal curve; commonly used measures of association and what they mean; criteria that have been suggested for inferring causality from nonexperimental research; and different types of t -tests.

This book provides fundamental and practical knowledge about research methodology that is essential for health care chaplains, and students and professionals in other health care fields and the social sciences.

The chapters in this book were originally published as articles in the Journal of Health Care Chaplaincy.

Scientific Method and Its Application to Chaplaincy

KEVIN J. FLANNELLY and KATHERINE R. B. JANKOWSKI

The American philosopher Charles Sanders Peirce (1877) said there were four ways of knowing, or “fixing belief,” as he called it (Kerlinger, 1973). The first is the method of tenacity in which people hold something to be true because they know it is true, and nothing can convince them that it is not true. The second is the method of authority, in which, as the name implies, people accept a proposition to be true because a powerful institution or other authority claims it is true. Peirce called the third way of knowing the a priori method because a proposition is accepted as being a self-evident truth. It also has been called the method of intuition, because one concludes a proposition is true on the basis of intuition; that is, it appears to be reasonable or logical. Peirce said some propositions are accepted without question or analysis (a priori) because they agree with reason, although they do not necessarily agree with experience (Kerlinger, 1973; Peirce, 1877).

The last method of “fixing belief” is the “method of science” or “scientific method.” Scientific method, according to Peirce (1877), is predicated on the premise that: “There are real things, whose characters are entirely independent of our opinions about them …” (p. 10). Peirce did not think “fixing belief” using scientific method was reserved for scientists, and he thought that everyone should, and they often do, use scientific method in their everyday lives. Kerlinger (1973) similarly endorsed the general application of scientific method, saying it is “a special systematized form of all reflective thinking and inquiry” (p. 11).

Underwood (1957) said the purpose of scientific method was to describe and understand nature, and he thought the commonly made distinction between “pure” and “applied” science was a false dichotomy. Instead, he saw pure and applied science as ends of a continuum, in which the place of a research project along “this continuum is defined by the attitude of the research[er]” (Underwood, 1957, p. 9). A study that was recently published in the Journal of Health Care Chaplaincy (JHCC) illustrates the relative nature of this continuum (Gaudette & Jankowski, 2013). The study examined the association between spiritual beliefs and practices and general anxiety in a sample of palliative care patients. On the one hand, the study can be viewed as an example of applied research because the study’s findings that spiritual beliefs and practices are associated with lower anxiety can be used to develop chaplain interventions to reduce patient anxiety. On the other hand, the study can be viewed as an example of pure (or basic) research because the findings contribute to our fundamental knowledge of the relationship between spirituality and mental health, which has theoretical as well as practical implications.

This issue of JHCC introduces the first article in the Journal’s new section on Research Methodology. The section will feature an on-going series of articles about the application of scientific method in chaplaincy and related healthcare research. The first article describes different research designs and the degree to which one can draw causal inferences from their findings. However, research design is only one component of a research study that determines its quality, and the series will cover many other aspects of the research process. This series of articles on research aims to provide a quick reference for chaplains to prepare them to understand the research literature, to be active participants in research, and to identify and engage in evidence-based chaplaincy practices.

REFERENCES

• Gaudette, H., & Jankowski, K. R. (2013). Spiritual coping and anxiety in palliative care patients: A pilot study. Journal of Health Care Chaplaincy, 19(4), 131–139.
• Kerlinger, F. N. (1973). Foundations of behavorial research (2nd ed.). New York, NY: Holt, Rhinehart & Winston.
• Peirce, C. S. (1877). The fixation of belief. Popular Science Monthly, November, 1–15.
• Underwood, B. J. (1957). Psychological research. New York, NY: Appleton Century Crofts.

Research Designs and Making Causal Inferences from Health Care Studies

KEVIN J. FLANNELLY and KATHERINE R. B. JANKOWSKI

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.

Traditionally, healthcare research has been divided into two categories: observational research and experimental research, or simply experimentation

TABLE 1 Research Designs in Descending Order of Their Strength of Evidence

Experimental Research (Experiments-Randomized Control Trials)

Quasi-Experimental Research

Observational Research

Analytic Studies

Prospective Cohort

Retrospective Cohort

Case-Control

Survey Studies

Correlational (cross-sectional and longitudinal)

Descriptive (cross-sectional)

Case Studies

(Dawson-Saunders & Trapp, 1994; Mausner & Kramer, 1985). Observational research is a broad category that includes several different types of research designs, including surveys, case studies, and specific types of epidemiological study designs that will be discussed later (Dawson-Saunders & Trapp, 1994). A third type of research design is also recognized in health care and the social sciences (Cook & Campbell, 1979; Kleinbaum, Kupper, & Morgenstern, 1982; Macnee & McCabe, 2008), although it is ignored in many textbooks on health research: that is, quasi-experimental research (see Table 1).

OBSERVATIONAL RESEARCH

Observational research provides a fertile ground for thought. This type of research often yields evidence that supports questioning commonly accepted beliefs, and it can provide new insights and new ways of thinking about causes and effects. It is exceptionally helpful in the development of new theory and study of new fields of inquiry. Observational research has the potential to be done quickly, with uncomplicated designs, and minimal monetary investment.

Survey studies collect information, or data, from individuals using questionnaires or face-to-face interviews. Interviews are typically used when it is important to delve more deeply into issues or individual experiences than can be done using standard questionnaires (Dawson-Saunders & Trapp, 1994). The purpose of the survey dictates the kinds of questions it asks. Social surveys usually collect information about people’s attitudes and opinions about social issues, whereas health surveys collect information about height, weight, blood pressure, symptoms of disease, and so forth. Almost all survey studies collect information about the attributes or characteristics of the individuals that they survey, such as age, gender, and marital status. Naturally, these characteristics vary from person to person. A survey of U.S. adults, for example, may question people anywhere from 18 to 100 years of age. The same participants may be males or females, and they may be married, or unmarried. In scientific language, these attributes are called variables because the attributes vary along some dimension. Indeed, the scientific term for anything that varies along a dimension is a variable, including attitudes, opinions, and measures of health and health outcomes.

The first modern social survey study was conducted by interviewing household members in London during the 1880s. The survey’s results appeared in the 1889 book, Life and Labour of the People of London, and a series of books that followed (Marsden & Wright, 2010). Social surveys have been ubiquitous in the United States since the 1950s, and U.S. health surveys have steadily increased since then, as well. Today, survey research is the most commonly used research method in the social sciences (except psychology), and it is widely used in the health sciences.

It is not surprising, therefore, that surveys are the most common type of study method used in chaplaincy research (Galek, Flannelly, Jankowski, & Handzo, 2011). During the last decade (2000–2009), survey studies in chaplaincy have explored a number of topics, asking: patients about their satisfaction with chaplaincy care; hospital administrators about the roles and functions of chaplains in their institutions; and chaplains about their interventions and the spiritual needs of their patients (Galek et al., 2011). Many survey studies related to chaplaincy fall into the category of descriptive studies, because they simply describe the attitudes, behaviors, health outcomes, and so forth of the people surveyed.

The Journal of Health Care Chaplaincy (JHCC) published a number of survey studies in the past few years that we think are good examples of descriptive studies. One is a survey that asked chaplains at several prominent U.S. hospitals about their access to medical records (Goldstein, Marin, & Umpierre, 2011). Another analyzed the survey responses of over 200 chaplains to questions about the challenges, rewards, and frustrations of working in the U.S. Veterans Health Administration (Beder & Yan, 2013). Large-scale descriptive studies have been used in epidemiology to measure the incidence and prevalence of diseases, other health problems, and behaviors in a population (Kleinbaum et al., 1982). Descriptive studies are mainly used in epidemiology when little is known about the occurrence or etiology (i.e., cause or origin) of a disease. Incidence and prevalence are different measures of the rate of occurrence or presence of diseases, among others, which will be discussed in a later article on research methodology.

The second major category of survey studies is the correlational study, which measures the correlations or relationships among different variables. Health researchers often look at the correlations between variables measuring personal characteristics and experiences and variables measuring health outcomes to see if the former are related to health outcomes. The first statistical procedure to measure correlations between variables was developed by Charles Darwin’s cousin Francis Galton to measure inherited similarities in physical attributes (Galton, 1888). Galton’s colleague, Karl Pearson developed the mathematical formula for the correlation coefficient, which is still used today (Dawson-Saunders & Trapp, 1994). The correlation coefficient measures the strength and direction of association between two variables.

When descriptive studies and correlational studies are conducted by surveying people at a certain point in time, they are called cross-sectional studies because their measures are recorded at a cross-section in time (Kleinbaum et al., 1982). As cross-sectional studies measure all of the study’s variables at the same time they can only tell us how things are and what is occurring at the time the survey was conducted. Descriptive studies and correlational studies are often lumped together as cross-sectional studies in the medical literature, although correlational studies typically provide more information about relationships between variables.

JHCC has published several correlational studies in the past few years. A recent pilot study of spirituality and anxiety in palliative care patients is a particularly good example because it analyzed the collected data using a statistical procedure called correlation (Gaudette & Jankowski, 2013). Correlational analysis, which is the basis for the term correlational study, measures the degree that two things are related to, or associated with, each other. In this study, correlations were used to measure the degree to which: (a) anxiety was related to beliefs about God, and (b) anxiety was related to spiritual practices, such as meditation. The study also made limited use of a related statistical procedure developed by Galton and Pearson called regression analysis. An excellent example of the capacity of correlational studies to shed light on complex issues is a large-scale study of Swiss patients that used a regression analysis to examine the extent to which patient satisfaction with chaplaincy care was influenced by various factors, such as patient age, gender, religion, health status, and hospital length of stay (Winter-Pfandler & Morgenthale...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Citation Information
• 1 Scientific Method and Its Application to Chaplaincy
• 2 Research Designs and Making Causal Inferences from Health Care Studies
• 3 Fundamentals of Measurement in Health Care Research
• 4 Operational Definitions in Research on Religion and Health
• 5 Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research
• 6 Measures of Central Tendency in Chaplaincy, Health Care, and Related Research
• 7 Measures of Variability in Chaplaincy, Health Care, and Related Research
• 8 Studying Associations in Health Care Research
• 9 The t-test: An Influential Inferential Tool in Chaplaincy and Other Healthcare Research
• 10 Threats to the Internal Validity of Experimental and Quasi-Experimental Research in Healthcare
• Index