VARIABLES

• Definition: Variables are characteristics or attributes that can take on different values. They are qualities, quantities, or properties of people, objects, or situations that are subject to change or variation.

• Explanation: Think of variables as the things you are measuring or studying that can differ. For example, age, gender, income, and test scores are all variables because they can have different values for different individuals or in different situations.

INDEPENDENT VARIABLE

• Definition: The independent variable is the variable that a researcher deliberately manipulates or changes. It is also known as the “manipulated variable.”

• Explanation: This is the variable that is considered the potential cause in a cause-and-effect relationship. In the example “factors influencing the uptake of family planning services,” “factors influencing” represents the independent variable because these factors are what might be affecting the uptake.

DEPENDENT VARIABLE

• Definition: The dependent variable is the variable that is expected to be affected or influenced by the independent variable. It is also referred to as the “outcome variable.”

• Explanation: This is the variable that is considered the potential effect or outcome. Researchers are interested in understanding, explaining, or predicting changes in the dependent variable. In the “family planning uptake” example, “uptake of family planning services” is the dependent variable, as it is what the researchers are trying to understand and potentially influence through other factors.

OPERATIONAL DEFINITION

• Definition: An operational definition specifies exactly how a researcher will measure the variables they are investigating. It is a clear and precise description of how a variable will be observed and quantified in the actual study.

• Explanation: It’s about making abstract concepts measurable. For example, if you are studying “stress,” you need an operational definition of how you will measure stress – will it be through a questionnaire, physiological measures like heart rate, or observations of behavior? The operational definition makes the variable concrete for your research.

HYPOTHESIS

• Definition: A hypothesis is a statement that predicts a relationship between two or more variables in a research study. It is an educated or reasoned guess formulated by the researcher.

• Explanation: A hypothesis is a testable prediction. It’s a researcher’s best guess about what they expect to find in their study, based on existing knowledge or theory. Research is then conducted to see if the data supports or refutes the hypothesis.

LIMITATIONS

• Definition: Limitations are restrictions or weaknesses within a research study that may affect the credibility and how widely the findings can be applied (generalizability). These are potential flaws or shortcomings in the research.

• Explanation: Limitations can arise from various sources, such as limited resources, a small sample size, methodological weaknesses, or other factors beyond the researcher’s complete control. Acknowledging limitations demonstrates research integrity and a thorough understanding of the study’s context.

POPULATION

• Definition: In research, a population refers to the entire group of individuals or objects that share certain characteristics and are of interest for a particular study.

• Explanation: The population is the complete set of cases that the researcher wants to learn about. It could be all people with a specific disease, all hospitals in a region, or any defined group that fits the study’s criteria.

TARGET POPULATION

• Definition: The target population is the entire population that researchers are interested in drawing conclusions about and to which they intend to generalize their research findings.

• Explanation: This is the ideal population that the researcher wants to study and make inferences about. It’s the larger group that the research seeks to understand.

ACCESSIBLE POPULATION

• Definition: The accessible population is the subset of the target population that is actually available to the researcher for a particular study.

• Explanation: In practice, it’s often impossible to study the entire target population. The accessible population is the portion of the target population that is realistic and feasible for the researcher to reach and recruit for their study.

SAMPLE

• Definition: A sample is a smaller, selected portion or subset of the population that is chosen to participate in a research study.

• Explanation: Researchers study samples to gather data that will represent the larger population. Studying a sample is more practical, cost-effective, and time-efficient than studying the entire population.

REPRESENTATIVE SAMPLE

• Definition: A representative sample is a sample that closely mirrors the characteristics of the population from which it was drawn.

• Explanation: The goal of sampling is to obtain a sample that is representative of the population so that findings from the sample can be generalized back to the larger population with confidence.

SAMPLING

• Definition: Sampling is the act, process, or technique of selecting a representative portion (sample) from a population to determine characteristics of the whole population. It is the method used to choose participants for a study.

• Explanation: Sampling is the systematic process of selecting a subset of individuals or units from a population. Effective sampling is crucial for ensuring that the research findings are generalizable to the population of interest.

PROBABILITY SAMPLING

• Definition: Probability sampling involves selecting subjects or sampling units from a population using a random procedure.

• Examples: Simple Random Sampling, Stratified Random Sampling

• Explanation: In probability sampling, every member of the population has a known, non-zero chance of being selected into the sample. This randomness helps to minimize bias and increase the likelihood of obtaining a representative sample.

NON-PROBABILITY SAMPLING

• Definition: Non-probability sampling involves selecting subjects or sampling units from a population using a non-random procedure.

• Examples: Convenience Sampling, Purposive Sampling

• Explanation: In non-probability sampling, selection is not based on random chance. While easier to implement, non-probability sampling methods are more prone to bias and may limit the generalizability of findings to the larger population.

RELIABILITY

• Definition: Reliability refers to the degree of consistency and accuracy with which a measurement instrument measures the attribute it is designed to measure.

• Explanation: A reliable instrument produces similar results when used repeatedly under similar conditions. It is about the consistency and dependability of the measurement.

VALIDITY

• Definition: Validity is the degree to which a measurement instrument truly measures what it is intended to measure.

• Explanation: A valid instrument accurately reflects the concept or construct it is designed to assess. It is about the accuracy and truthfulness of the measurement.

PRE-TESTING

• Definition: Pre-testing is a stage in research where data collection instruments, such as questionnaires, are tested with a small group of individuals from the target population before the main study.

• Explanation: It’s a trial run of your data collection tools. By administering instruments to a smaller group, researchers can identify and resolve potential issues before the full-scale data collection begins.

Purpose of Pretesting

• Problem Identification: To find and fix problems with the data collection instrument, such as unclear questions, confusing wording, or formatting issues.

• Validity Assurance: To ensure that the data collection tools are valid and measure what they are intended to measure, leading to more reliable results.

• Feasibility Assessment: To determine if respondents are able and willing to provide the necessary information, and to check if the questions are appropriate and understandable for the target population.

• Solution Testing: To allow researchers to test potential solutions to identified problems and refine the questionnaire before wider use.

Principles of Pretesting

• Realistic Conditions: Pre-testing should be conducted under conditions that closely resemble the actual data collection environment.

• Representative Participants: Participants in the pre-test should be similar to those who will be included in the main study sample in terms of their characteristics.

• Detailed Note-Taking: Careful notes should be made about any problems encountered during pre-testing, along with potential solutions identified.

PILOT STUDY

• Definition: A pilot study is a smaller, preliminary version of the main research study. It is conducted to refine the methodology and test the feasibility of the research design.

• Explanation: A pilot study is like a dress rehearsal for the main study. It involves using similar subjects, settings, procedures, data collection methods, and analysis techniques as planned for the full study, but on a smaller scale.

Purpose of Pilot Study

• Methodology Refinement: To explore and test research elements and make necessary modifications to research tools and methodology before the main study.

• Feasibility Assessment: To ascertain the practicality and feasibility of conducting the full-scale research project, including data collection instruments and procedures.

ANALYSIS

• Definition: Analysis is the systematic process of organizing, structuring, and examining data in order to answer research questions or draw meaningful conclusions.

• Explanation: Data analysis is the critical step where raw data is transformed into interpretable findings. It involves using various techniques to identify patterns, relationships, and insights within the data. Effective analysis is essential for drawing valid and reliable conclusions from research. Data analysis typically follows the presentation and interpretation of research findings.

INFORMED CONSENT

• Definition: Informed consent is a process where individuals learn key facts about a research study, particularly a clinical trial, before deciding whether to participate. It is an ongoing process of providing information throughout the study.

• Explanation: Informed consent is a cornerstone of ethical research involving human participants. It ensures that individuals have sufficient information to make a voluntary and informed decision about whether to participate. Researchers must explain the study details to potential participants, and consent must be freely given and continuously respected.

COHORT

• Definition: In epidemiology, a cohort is a defined group of individuals who share a common characteristic or experience.

• Types: Cohorts can be prospective (followed forward in time from the present to the future) or retrospective (studied based on past data and experiences).

• Explanation: Cohort studies are a type of observational research that follows groups of individuals (cohorts) over time to examine the occurrence of outcomes, like diseases, in relation to exposures or characteristics they share.

BIAS

• Definition: Bias occurs when a particular viewpoint or influence prevents impartial or objective judgment on issues related to a subject.

• Explanation: Bias can systematically distort research findings, leading to inaccurate or misleading conclusions. In clinical studies, techniques like blinding and randomization are used to minimize and control bias.

BLIND (or Masked)

• Definition: In a “blind” or “masked” randomized trial, participants are not told which treatment group (or arm of the trial) they are assigned to.

• Explanation: Blinding is a technique used to reduce bias, particularly in clinical trials. By keeping participants (and sometimes researchers) unaware of treatment assignments, it helps to prevent expectations or knowledge of treatment from influencing outcomes or reporting.

SIDE EFFECTS

• Definition: Side effects are any unintended or undesired actions or effects of a drug or treatment.

• Explanation: Side effects can range from minor and temporary to serious and long-lasting. They are a critical consideration in medical research and treatment development. Experimental drugs and treatments must be thoroughly evaluated for both immediate and long-term side effects to ensure patient safety.