Using Research and Statistics in Health Care

Fundamentals of Statistics and Terminology

  • Statistics Definition: Statistics is defined as the science of analyzing and learning from data.

  • Population: The entire group of individuals to be studied. In measurement terms, it represents the set of measurements corresponding to the entire group of individuals about which information is desired.

  • Individual: A person or object that is a member of the population being studied. Individuals are the objects described by a set of data and can include people, animals, or things.

  • Sample: A subset or part of the population from which data is actually collected. It is used to draw conclusions and infer characteristics about the broader population.

  • Census: A specific type of sample survey that attempts to include the entire population in the sample.

  • Variable: Any characteristic of an individual.

Categorization of Statistical Methods

  • Descriptive Statistics:

    • Involves numerical or graphic summaries of data.

    • Presentations and tools include charts, graphs, tables, and summary statistics (such as the mean and standard deviation).

  • Inferential Statistics:

    • Consists of statistical techniques that allow conclusions to be drawn about the relationships found among different variables.

    • Examples include the Chi-square test, t test, and Analysis of Variance (ANOVA).

Classification of Study Types

  • Descriptive Studies:

    • Purpose: Simply describe situations and events.

    • Research Questions: Ask descriptive questions (e.g., "What is the average length of stay in a hospital after being admitted for an asthma attack?").

    • Analysis Tool: Descriptive statistics are used to analyze data from these studies.

  • Explanatory Studies:

    • Purpose: Elucidate relationships among variables. They may or may not seek to establish causality.

    • Research Questions: Ask inferential questions (e.g., "Are women who are sedentary during the 3rd3^{\text{rd}} trimester of pregnancy more likely to have a C-section than women who exercise regularly during the 3rd3^{\text{rd}} trimester?").

    • Analysis Tool: Uses inferential statistics.

  • Prediction & Control Studies:

    • Purpose: Seek to determine which variables predict other variables and to determine causality.

    • Research Questions: Ask inferential questions (e.g., "Are people who receive an experimental medication less likely to have symptoms of the disease than people who receive the standard medication?").

    • Analysis Tool: Uses inferential statistics.

The Ten-Step Study Plan

  1. Statement of the Problem and Its Significance:

    • Explains the purpose of the study, including the central research question, why it is important, and how it fits into the existing body of research.

    • Requires clear and concise formulation that explicitly states what the study seeks to accomplish (e.g., using phrases like "the purpose of this study is to…").

  2. Theoretical/Conceptual Framework:

    • All studies require an underlying theoretical or conceptual framework to organize the analysis.

    • Draws from existing theoretical models or provides a logic model.

    • Uses the framework to organize research questions and hypotheses, providing appropriate citations.

  3. Research Questions to Be Answered:

    • Stem directly from the statement of the purpose of the study.

    • Grounded in theory and current literature.

    • Explicitly state expected relationships and relate directly to the data that will be gathered by the researcher.

  4. List of Hypotheses to Be Tested:

    • Definition: A hypothesis is a tentative prediction or description of the expected relationship between two or more variables.

    • Function: Translates research questions into statements that can be tested using inferential statistics.

    • Directional (One-Way) Hypotheses: State the specific expected direction of the relationship between two variables (e.g., "People who are immunized will be less likely to contract the flu than people who are not immunized.").

    • Nondirectional (Two-Way) Hypotheses: State that a relationship exists between variables without specifying the direction (e.g., "There will be a relationship between obesity and exercise level.").

  5. Definitions of Key Terms and Variables:

    • Defines all terms that may be unclear to the reader and spells out all acronyms upon first use.

    • Defines all variables and specifies their roles:

      • Independent Variables: Variables that are manipulated and/or may affect the outcome of interest (typically including age, gender, and ethnicity).

      • Dependent Variables: The primary outcomes of interest (typically including health status, use of health services, and cost of care).

      • Covariates: Additional variables accounted for in the analysis.

  6. Description of the Research Design:

    • Details how data will be or were collected.

    • Identifies the study design type (observational, quasi-experimental, or experimental).

    • Specifies data gathering methods (interviews, surveys, medical records, etc.).

    • For secondary data analysis (existing data), describes and cites the originating study.

  7. Description of the Sample and How It Was Obtained:

    • Outlines the sampling method (random or nonrandom).

    • Specifies overall sample size and the size of each comparison group.

    • Details sociodemographics (age, gender, ethnicity, education, marital status, and other relevant variables).

  8. Description of the Planned Statistical Analysis:

    • Explains data cleaning procedures to ensure data is error-free.

    • Identifies the planned descriptive and inferential statistics.

    • Specifies the statistical models to be built (e.g., linear regression, logistic regression, ANOVA, among others).

  9. Statement of Assumptions, Limitations, and Delimitations:

    • Assumptions: Statements taken to be true without direct evidence (e.g., assuming participants report exercise frequency or body weight accurately).

    • Limitations: Weaknesses of the study that may limit result validity (e.g., small sample sizes, poor response rates, poor follow-up rates, or lack of random selection).

    • Delimitations: Boundaries to which the study was deliberately confined by the researcher (e.g., restricting inclusion to adults only, a specific age group, or women only), which directly limit generalizability.

  10. Dissemination Plan:

    • Establishes how study results will be shared with external audiences.

    • Includes internal reports, conference presentations, trade journal publications, and peer-reviewed journal publications.

Practice Questions and Concept Verification

  • Question 1: Explanatory studies simply seek to describe situations and events. (True or False)

    • Answer: False

    • Rationale: Explanatory studies seek to elucidate relationships among variables rather than merely describing situations and events.

  • Question 2: Descriptive studies do not need a framework. (True or False)

    • Answer: False

    • Rationale: All studies need to be organized using a theoretical or conceptual framework.

  • Question 3: Which of the following is an example of an assumption of a study?

    • A. The self-reported body weight was accurate.

    • B. Only women ages 1818 to 3434 were included in the study.

    • C. The response rate to the survey was only 33%33\%.

    • D. All of these are assumptions.

    • Answer: A. The self-reported body weight was accurate.

    • Rationale: Expecting subjects to correctly report their body weight is an assumption. Option B is a delimitation (a deliberate boundary) and option C is a limitation (a weakness impacting validity).