How is the number of subjects for participation determined for a qualitative study?

Choice A rationale

This statement is incorrect because it reverses the relationship between the two groups. A population is the entire set of individuals or objects that meet a specific set of criteria, while a sample is a smaller group taken from that population. A population cannot be a segment of a sample because the population is the larger, encompassing entity. The sample must represent the population so that researchers can generalize their findings from the small group to the large.

Choice B rationale

Both samples and populations can consist of individuals, groups, objects, or events. There is no distinction in the type of entities they represent based on these terms. For example, a researcher could study a population of medical records or a sample of surgical procedures. The terms refer to the size and inclusion scope of the group being studied, not the nature of the subjects themselves. Both categories are used across all types of research subjects.

Choice C rationale

A sample is a subset of the population selected to participate in a study, intended to reflect the characteristics of the whole group. Since it is often impossible or impractical to study an entire population, such as all people with diabetes, researchers select a representative sample. By studying this segment, researchers can make inferences about the larger population. The accuracy of these inferences depends on how well the sample represents the diverse traits of the defined population.

Choice D rationale

This statement is inaccurate because the defining characteristics for both are usually the same. The criteria used to define the population, known as eligibility or inclusion criteria, are the same criteria used to select the sample. A sample does not necessarily have "narrower" characteristics; it simply has fewer members. Both must share the same attributes so that the data collected from the sample can be validly applied to the broader population being investigated.

Choice A rationale

This study aims to identify variables that determine or forecast a specific outcome, which is a hallmark of quantitative research designs. Predicting treatment choices involves measuring statistical relationships between variables like age, stage of cancer, and patient preferences to find correlations. Qualitative studies do not seek to predict but rather to understand. Therefore, the use of predictive modeling suggests a numerical, objective approach rather than an interpretive, subjective one typical of qualitative inquiry.

Choice B rationale

The focus on the lived experience and the process of moving through a difficult situation indicates a qualitative approach. Qualitative research explores the depth, meaning, and complexity of human experiences using words rather than numbers. Transcending is a subjective concept that cannot be easily quantified with traditional measurement tools. This title suggests an ethnographic or phenomenological study aimed at understanding how individuals perceive and handle pain during a specific medical procedure, reflecting qualitative methodology.

Choice C rationale

Evaluation research often utilizes quantitative methods to measure the effectiveness and efficacy of specific interventions through standardized outcomes. Evaluating nursing interventions typically requires experimental or quasi-experimental designs where data is collected to determine if the intervention caused a measurable change. While evaluation can be qualitative, the language used here is more consistent with clinical trials or outcome studies that rely on statistical significance to validate nursing practices across various oncology patient populations.

Choice D rationale

This title describes a study testing the effect of a specific tool on a symptom, which usually involves a quantitative experimental design. Using a distraction method like virtual reality during chemotherapy involves measuring variables such as pain scores or anxiety levels before and after the intervention. The goal is to establish a cause-and-effect relationship through numerical data. This objective measurement of an intervention's impact aligns with the positivist paradigm of quantitative research rather than qualitative exploration.