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Nursing Research

Research Methodology By Kpolay O.Y Wisdom

What is Research Methodology

• (^) Research methodology refers to the systematic plan for conducting research.
• (^) Methodology refers to the overall plan or blueprint for conducting a study, which includes strategies for collecting, measuring, and analysing data to answer research questions or test hypotheses. Polit & Beck (2021)
• (^) Research methodology is the philosophical and procedural approach that guides the choice of research design, methods, and interpretation of results. Polit & Beck (2021)
• (^) Methodology is a system of explicit rules and procedures upon which research is based and against which claims for knowledge are evaluated. Neuman (2014)
• (^) It includes the overall approach, design, and procedures used to collect and analyse data in a study.

Quantitative Research Methodology

• (^) Quantitative research refers to a systematic investigation that focuses on quantifying phenomena and relationships through the collection and analysis of numerical data.
• (^) It seeks to test theories or hypotheses and often aims for generalisability.
• (^) Polit & Beck (2021): Define it as a "formal, objective, and systematic process in which numerical data are used to obtain information about the world."
• (^) Creswell (2018): Describes it as research that tests objective theories by examining relationships among variables using statistical procedures.
• (^) Neuman (2014): Views it as "empirical research that relies heavily on numerical measurement, structured data collection, and deductive logic

Characteristics of Quantitative

Research

• (^) Objectivity: Researcher remains detached and unbiased.
• (^) Structured Design: Pre-determined tools like questionnaires and checklists are used.
• (^) Large Samples: Often uses large, representative samples for generalisation.
• (^) Statistical Analysis: Data are analysed numerically (e.g., mean, correlation, regression).
• (^) Replicability: Designed so that studies can be repeated.
• (^) Reliability and Validity: Ensures consistency and accuracy of the results.

Types of Quantitative Research

Designs

1. Experimental Designs : These involve manipulation, control, and randomization to examine cause-and-effect relationships. a) Experimental Design b) Quasi-Experimental Design (type- Time-Series Design) 2. Non-Experimental Designs/ observational studies: These involve observation without intervention, often used to explore relationships or describe variables. a) Descriptive Design b) Correlational Design c) Exploratory Design d) Ex Post Facto Design e) Cross-Sectional Design f) Longitudinal Design

True Experimental Design

A True Experimental Design is a research design that uses randomisation, manipulation, and control to test hypotheses and determine causal relationships between variables. It is considered the gold standard in quantitative research for assessing cause and effect.

• (^) According to Polit & Beck (2021), true experiments require that participants be randomly assigned to different groups (e.g., intervention and control), and that the researcher actively introduces an intervention (independent variable).

True Experimental Design

Characteristics:

• (^) Randomisation
• (^) Manipulation
• (^) Control Group
• (^) Pre-test and Post-test Advantages:
• (^) High internal validity (can establish cause-effect relationships).
• (^) Minimises bias and confounding variables.
• (^) Can be replicated to validate findings. 🔹 Disadvantages:
• (^) May not be feasible in real-world or clinical settings.
• (^) Ethical concerns may arise (e.g., withholding treatment).
• (^) Expensive and time-consuming.
• (^) Limited external validity if conducted in artificial settings.

Quasi-Experimental Design

• (^) A Quasi-Experimental Design is used to evaluate causal relationships without full randomisation.
• (^) It involves manipulation of the independent variable but lacks either random assignment or a control group.
• (^) Creswell (2014) and Neuman (2014), quasi-experiments are commonly used in natural settings where randomisation is not possible, especially in social and health research. Characteristics:
• (^) Manipulation of the independent variable is present.
• (^) Lack of randomisation—groups may be pre-existing (e.g., schools, hospitals).
• (^) May or may not include a comparison or control group.
• (^) Often includes pre- and post-testing.

Quasi-Experimental Design

Advantages

• (^) More practical and ethically acceptable in many real-world settings.
• (^) Can study interventions in natural environments.
• (^) Useful when random assignment is impossible. Disadvantages
• (^) Lower internal validity due to lack of randomisation.
• (^) Greater risk of confounding variables.
• (^) Harder to rule out alternative explanations for results

• (^) Advantages:
  • (^) Allows for monitoring of patterns before and after an intervention.
  • (^) Controls for natural fluctuations or secular trends.
  • (^) More robust than simple pre/post-test design.
• (^) Disadvantages:
  • (^) Still lacks full randomisation.
  • (^) Requires many data points, which can be time-consuming.
  • (^) Susceptible to history effects (external events influencing the outcome).
• (^) Example:
• (^) A hospital records rates of hospital-acquired infections for six months, introduces a new sterilisation procedure, and continues monitoring for another six months. Analysis focuses on whether the change altered the trend.

Summary: Intervention (Experimental) Designs: Used when the researcher manipulates variables

and controls conditions to examine cause-and-effect

relationships.

Type What It Is Key Features Example True Experimental Design Involves randomisation, control group, and manipulation of independent variable Random assignment, control group, high internal validity Testing effect of a new drug using RCT Quasi-Experimental Design Lacks random assignment but still involves manipulation May lack control or randomisation Testing patient education outcomes across wards without randomisation Time-Series Design (Subtype of Quasi) Observes the same group over multiple time points Tracks changes before, during, and after intervention Observing infection rates before and after hand hygiene programme

Descriptive Design (Non-

Experimental)

Advantages:

• (^) Useful for gathering information about populations.
• (^) Provides a snapshot of current conditions.
• (^) Easy and inexpensive to conduct. 🔹 Disadvantages:
• (^) Cannot determine cause and effect.
• (^) Susceptible to bias in self-reports or observer interpretation. 🔹 Example:
• (^) A researcher surveys 200 nurses about their level of job satisfaction and demographic characteristics to describe workforce patterns in a hospital.

Correlational Design (Non-

Experimental)

• (^) Correlational research examines relationships between variables without manipulating them. It seeks to identify associations, not causation.
• (^) As per Creswell (2014) and Neuman (2014) , it's valuable in understanding patterns, especially in public health and nursing research. Characteristics:
  • (^) Measures variables as they occur naturally.
  • (^) Uses statistical correlation to determine strength and direction.
  • (^) No manipulation or intervention.