Sampling Plans and Strategies

In this chapter, we will focus on sampling plans, which is really how researchers choose who or what to include as part of their study.

Content:

1. The Purpose of Sampling
2. Comparing a Population and a Sample
3. Basics Concepts Related to Sampling
4. Inclusion and Exclusion Criteria in Sampling
5. Ethical Considerations in Inclusion and Exclusion Criteria
6. Probability vs. Nonprobability Samples
7. Sampling Strategies in Qualitative and Quantitative Research
8. Specific EBP Considerations in Sampling

Objectives:

1. Understand the purpose of sampling in research.
2. Differentiate between a population and a sample.
3. Identify basic concepts related to sampling.
4. Determine inclusion and exclusion criteria in sampling.
5. Analyze ethical considerations in sampling.
6. Distinguish between probability and nonprobability sampling.
7. Explore sampling strategies in qualitative and quantitative sampling.
8. Examine EBP considerations in sampling.

Key Terms:

Population: The entire group of individuals or entities that a researcher is interested in studying. In quantitative research, the goal is often to generalize findings from the sample to this broader group.

Sample: A subset of the population selected for participation in the study. The sample should ideally represent the population to allow for valid inferences.

Sampling Frame: A list or database that includes all members of the population from which the sample will be drawn. A complete and accurate sampling frame is essential for representative sampling.

Probability Sampling: A sampling method in which every member of the population has a known, non-zero chance of being selected. This includes techniques like simple random sampling, stratified sampling, and cluster sampling, commonly used in quantitative research.

Non-Probability Sampling: A sampling method where not all members of the population have a chance of being selected. This includes techniques like convenience sampling, purposive sampling, and snowball sampling, often used in qualitative research.

Random Sampling: A probability sampling technique where each member of the population has an equal chance of being selected, often used to reduce bias and enhance the representativeness of the sample.

Stratified Sampling: A probability sampling method where the population is divided into subgroups (strata) based on a characteristic, and random samples are drawn from each stratum. This ensures representation of key subgroups in the sample.

Purposive Sampling: A non-probability sampling method where participants are selected based on specific characteristics or qualities relevant to the research question, commonly used in qualitative research to gain deep insights.

Convenience Sampling: A non-probability sampling method where participants are selected based on ease of access and availability. This method is quick and inexpensive but may introduce bias.

Snowball Sampling: A non-probability sampling method often used in qualitative research where existing study participants recruit future participants from among their acquaintances. This is useful for reaching hard-to-access populations.

Sampling Bias: A systematic error that occurs when the sample is not representative of the population, often due to flaws in the sampling process, leading to skewed results.

Saturation: A concept in qualitative research where data collection continues until no new information or themes are emerging from the data, indicating that the sample size is sufficient.

Introduction

Sampling is a critical element in the research process that affects study findings’ validity, reliability, and generalizability (Elfil & Negida, 2017). For nursing students, understanding the nuances of sampling is essential for both conducting research and evaluating the research of others. Bhardwai (2019) states that sampling is one of the most important factors which determines accuracy of a study. This chapter will explore the foundational concepts of sampling, explain the rationale for sampling, and discuss various strategies and considerations involved in selecting and analyzing samples.

The Purpose of Sampling

In most research studies, collecting data from an entire population is often impractical, if not impossible, due to constraints such as time, cost, and accessibility. Sometimes, it is possible, to obtain data from an entire population, such as in a study performed in the 2016 Minnesota Student Survey in which a population study was conducted from the male student population (Taliaferro et al., 2020).  However, most often, researchers use a sample—a subset of the population—to represent the larger group. Sampling allows researchers to make inferences about the population without the need to study every individual. This approach is more feasible and allows for more detailed and focused data collection and analysis.

Sampling is defined as a procedure to select a representative group from the larger population (Bhardwaj, 2019). Sampling is “a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual” (Barratt & Shantikumar, 2018). The goal of sampling is to make accurate inferences about a whole population based on data collected from a smaller group within that population, known as a sample. Although it is sometimes feasible and beneficial to gather information from an entire population, researchers often opt for sampling due to practicality and cost-effectiveness. For instance, Taliaferro et al. (2020) conducted a secondary analysis using data from the 2016 Minnesota Student Survey, which encompassed adolescent boys, to investigate risk factors and involvement in physical violence and bullying. Their findings showed that 13.2% of the boys engaged in physical violence, while 21.6% were involved in bullying. The study also highlighted that high-risk adolescents with limited connections to parents or other adults were more likely to partake in these behaviors.

In most research scenarios, using a sample instead of the entire population is more efficient and manageable. Studying an entire population can be nearly impossible or impractical, even if it is the group the researcher aims to generalize the findings to. Thus, sampling methods are designed to select a representative subset of the population to accurately reflect the broader group (Boswell & Cannon, 2022).

For another example, in a study exploring the effectiveness of a new nursing intervention on patient recovery times, it would be impractical to include all patients across multiple hospitals. Instead, a representative sample of patients can be selected, and the findings from this group can be used to infer the intervention’s effectiveness for the broader population.

Comparing a Population and a Sample

A population refers to the entire group of individuals or elements that are the focus of a research study. This group might include all patients with a specific health condition, all nurses in a particular hospital, or all residents of a community. The population is the target of the study’s conclusions, but because it is usually too large to study in its entirety, researchers turn to sampling.

A sample, on the other hand, is a smaller, manageable subset of the population selected for study. The goal is to ensure that the sample is representative of the population, meaning the findings from the sample can be generalized to the population with a reasonable level of confidence. The representativeness of the sample is critical to the validity of the research outcomes.

Figure Above: Population vs. Sample

Basic Concepts Related to Sampling

Several key concepts underpin the practice of sampling in research:

1. Representativeness: A sample is representative if it accurately reflects the characteristics of the population from which it is drawn. Ensuring representativeness is crucial for the validity of the study’s conclusions.
2. Sampling Bias: This occurs when certain members of the population are more likely to be included in the sample than others, leading to distorted or unrepresentative findings. Sampling bias can arise from improper sampling methods or non-random selection.
3. Sampling Frame: The sampling frame is a list or database from which a sample is drawn. It should ideally include all members of the population, though in practice, it may exclude some individuals due to accessibility issues.
4. Sampling Error: Sampling error refers to the difference between the characteristics of the sample and those of the population. While some level of sampling error is inevitable, it can be minimized by using appropriate sampling techniques.

Inclusion and Exclusion Criteria in Sampling

Inclusion and exclusion criteria are essential for defining who is eligible to participate in a study. Establishing inclusion and exclusion criteria for study participants is a standard, required practice when designing high-quality research protocols (Patino & Ferreira, 2018). Inclusion criteria are defined as the key features of the target population that the investigators will use to answer their research question (Boswell & Cannon, 2022). Exclusion criteria are key features that would not be eligible to be part of the sample. Therefore, exclusion criteria are essential in research for controlling confounding variables. These exclusion criteria are characteristics that may interfere with the study’s outcomes. Typical inclusion criteria include demographic, clinical, and geographic characteristics.

For example, what might be your criteria for including someone from participation? Are any criteria based on age, gender, race, ethnicity, sexual orientation, origin, type and stage of disease, the subject’s previous treatment history, and the presence or absence (as in the case of the “healthy” or “control” subject) of other medical, psychosocial, or emotional conditions? If so, justify. For example, if you are doing a study on pregnant persons, will you exclude men (most likely, you will)? If you exclude men, even if it seems obvious, this needs to be determined and stated.

• Inclusion Criteria: These are the characteristics that participants must have to be included in the study. Inclusion criteria help ensure that the sample is relevant to the research question and that the findings will be applicable to the population of interest. For example, in a study on diabetic patients, inclusion criteria might include being diagnosed with Type 2 diabetes and being over the age of 18.
• Exclusion Criteria: These are the characteristics that disqualify potential participants from the study. Exclusion criteria are used to remove individuals who might confound the results or who do not fit the study’s focus. Continuing the example above, exclusion criteria might include pregnant patients or other chronic conditions that could interfere with the study outcomes.

The careful selection of inclusion and exclusion criteria is critical to the integrity of the study, as it helps to ensure that the sample is both relevant and homogeneous, thus reducing variability in the data.

Table Above: Inclusion and Exclusion Criteria Example

Internal Validity in Relation to Inclusion and Exclusion Criteria

Inclusion and exclusion criteria play a crucial role in maintaining internal validity by ensuring that the sample is well-defined and free from confounding variables. Internal validity refers to the extent to which a study can confidently establish a cause-and-effect relationship between the independent and dependent variables, without interference from extraneous factors.

Internal validity refers to the degree to which a study accurately establishes a cause-and-effect relationship between variables, without being influenced by confounding factors or biases. A study with high internal validity ensures that the observed changes in the dependent variable are directly caused by the independent variable, rather than by extraneous influences.

For example, in a clinical trial testing a new blood pressure medication, high internal validity means that any reduction in blood pressure can be confidently attributed to the medication itself, rather than factors like diet, stress levels, or researcher bias.

By carefully selecting inclusion criteria, researchers ensure that participants share key characteristics relevant to the research question, reducing variability that might obscure true relationships. For example, in a study evaluating a new diabetes medication, including only patients diagnosed with Type 2 diabetes (rather than all diabetic patients) helps isolate the treatment effect.

Similarly, exclusion criteria help eliminate confounding variables that could weaken internal validity (Garg, 2016). For instance, excluding participants with multiple chronic conditions in the diabetes study ensures that changes in blood sugar levels are due to the medication rather than other underlying health conditions. However, while strict inclusion/exclusion criteria strengthen internal validity, they may limit external validity, meaning the findings may not be generalizable to a broader population. Researchers must carefully balance internal control and real-world applicability when designing studies.

External Validity in Relation to Inclusion and Exclusion Criteria

External validity refers to the extent to which the findings of a study can be generalized to other populations, settings, and times. A study with high external validity produces results that are applicable beyond the specific sample and conditions of the research, making it useful for real-world practice.

Inclusion and exclusion criteria directly impact external validity by determining how well a study’s findings can be generalized to broader populations. External validity refers to the extent to which research results apply to people, settings, and situations beyond the study sample.

If inclusion and exclusion criteria are too restrictive, the study may have high internal validity but low external validity, meaning the findings are not widely applicable. For example, a clinical trial testing a new hypertension medication might only include healthy adults aged 40-60, excluding those with multiple chronic conditions. While this approach controls confounding variables, it also limits the study’s generalizability to older adults or patients with comorbidities who are more representative of real-world hypertension cases.

Conversely, if inclusion criteria are too broad, external validity may improve, but internal validity may suffer due to increased variability in patient characteristics. Researchers must strike a balance—ensuring that the sample reflects the target population while maintaining control over confounding factors. Well-designed inclusion and exclusion criteria help create findings that are both scientifically rigorous and applicable to real-world clinical practice.

Ethical Considerations in Inclusion and Exclusion Criteria

When establishing inclusion and exclusion criteria, researchers must carefully navigate ethical considerations to ensure that the study is conducted fairly and respects the rights and well-being of all potential participants (Velasco, 2010). Inclusion and exclusion criteria are essential for defining the study population, but these criteria can also raise ethical issues related to equity, justice, and the protection of vulnerable groups.

Equity and Fairness

One ethical concern is ensuring that the inclusion criteria do not unjustly favor or exclude certain groups. For instance, a study might inadvertently exclude participants based on factors such as age, gender, ethnicity, or socioeconomic status, which could lead to biased findings and limit the generalizability of the results. Researchers have an ethical responsibility to ensure that the criteria are justified and that any exclusions are necessary and relevant to the study’s objectives. For example, if a study excludes elderly patients, the justification should be clearly tied to the research question and not based on assumptions or stereotypes.

Justice and Access to Research Benefits

The principle of justice requires that the benefits and burdens of research be distributed fairly among all groups in society (Polit & Beck, 2021). Excluding certain populations without adequate justification can deny them the potential benefits of participating in research, such as access to new treatments or interventions. Conversely, including participants who are unlikely to benefit from the research can be ethically problematic, especially if participation poses any risk (Hopp & Rittenmeyer, 2020). Researchers must balance the need to protect vulnerable populations with the ethical obligation to provide equitable access to research opportunities.

Protection of Vulnerable Groups

Vulnerable populations, such as children, pregnant women, individuals with cognitive impairments, or economically disadvantaged groups, require special consideration in the inclusion and exclusion criteria (Polit & Beck, 2021). Researchers must ensure that these groups are not exploited or exposed to unnecessary risks. At the same time, excluding vulnerable groups entirely can lead to a lack of research evidence that is relevant to these populations, which can have negative implications for their care. Ethical research design should carefully weigh the risks and benefits, providing additional protections when including vulnerable participants, such as obtaining assent along with parental consent for minors or implementing enhanced monitoring for high-risk groups (Cipriano, 2015).

Institution Review Board’s Role Related to Sampling

All research that involves human subjects must apply to an ethics board or Institutional Review Board before any part of the research begins. An Institutional Review Board (IRB) plays a crucial role in ensuring that research involving human participants is ethically sound, just, and equitable. One of its primary responsibilities is to review inclusion and exclusion criteria to prevent unjust participant selection and ensure that research findings are applicable to diverse populations (Cipriano, 2015). The IRB ensures that all individuals have fair access to participation unless scientifically justified exclusions are necessary.

1. Preventing Unjust Exclusion of Vulnerable Groups

The IRB ensures that inclusion and exclusion criteria do not unfairly exclude individuals based on factors such as race, ethnicity, gender, socioeconomic status, or disability, unless exclusion is necessary for scientific validity. For example, a study on hypertension treatments should not exclude women or minority populations unless there is a strong scientific or safety-related reason.

2. Justifying Inclusion and Exclusion Criteria

Researchers must provide a clear, scientific justification for inclusion and exclusion decisions. The IRB reviews whether:

• Exclusions are based on valid safety concerns (e.g., pregnant women in a drug trial where fetal harm is unknown).
• The study population reflects the group affected by the condition being studied.
• The criteria do not systematically exclude certain populations, limiting the generalizability of results.

3. Promoting Justice in Participant Selection

The Belmont Report’s principle of justice states that the burdens and benefits of research should be fairly distributed. The IRB ensures that researchers do not target disadvantaged groups (e.g., low-income individuals, prisoners) unfairly while also ensuring that underrepresented populations are not automatically excluded from research that could benefit them.

4. Addressing Ethical Concerns in Sampling Methods

The IRB evaluates whether recruitment methods promote equal opportunity for participation and prevent coercion. This includes:

• Ensuring recruitment strategies do not pressure participants (e.g., students should not feel forced to participate in faculty-led studies).
• Avoiding convenience sampling that might exclude underrepresented populations.
• Assessing whether incentives for participation are appropriate and not coercive.

5. Protecting Participants’ Rights and Well-Being

Beyond fair inclusion and exclusion, the IRB ensures that:

• Participants understand their rights through informed consent.
• Vulnerable populations (e.g., children, individuals with cognitive impairments) have added protections (e.g., parental consent, legally authorized representatives).
• The study does not place undue burden on specific groups while denying them potential benefits of research.

By thoughtfully addressing these ethical considerations in the design of inclusion and exclusion criteria, researchers can ensure that their studies are both scientifically valid and ethically sound, ultimately contributing to the production of evidence that is both fair and applicable to diverse populations.

Probability vs. Nonprobability Sampling

Sampling methods can be broadly classified into two categories: probability and nonprobability sampling.

• Probability Sampling: In probability sampling, every member of the population has a known, non-zero chance of being selected. This approach enhances the likelihood that the sample will be representative of the population. Common types of probability sampling include:
• • Simple Random Sampling: Every individual in the population has an equal chance of being selected.
  • Systematic Sampling: Selection is made at regular intervals from a randomly ordered list.
  • Stratified Sampling: The population is divided into subgroups (strata) based on specific characteristics, and random samples are drawn from each stratum.
  • Cluster Sampling: The population is divided into clusters, typically based on geographical areas, and entire clusters are randomly selected for study.

• Nonprobability Sampling: In nonprobability sampling, the likelihood of any individual being selected is unknown. This type of sampling does not include random selection of elements and therefore has a higher possibility of yielding a potentially biased, nonrepresentative sample (Boswell & Cannon, 2020). This method is often used in exploratory research or when probability sampling is impractical. Common types of nonprobability sampling include:
• • Convenience Sampling: Participants are selected based on availability and willingness to participate.
  • Purposive Sampling: Participants are selected based on specific characteristics that are relevant to the study.
  • Snowball Sampling: Current participants recruit future participants from among their acquaintances, which is useful for studying hard-to-reach populations.
  • Quota Sampling: The researcher ensures that certain characteristics are represented in the sample in proportion to their prevalence in the population.

INSERT TABLE

There are four levels of measurement:

Nominal: The data can only be categorized

Ordinal: The data can be categorized and ranked

Interval: The data can be categorized, ranked, and evenly spaced

Ratio: The data can be categorized, ranked, even spaced, and has a natural zero

Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.

Why are levels of measurement important?

The level at which a researcher measures a variable determines how he/she can analyze the data.

The different levels limit which descriptive statistics can be used to get an overall summary of the data, and which type of inferential statistics can be performed on the data to support or refute the hypothesis.

In many cases, the variables can be measured at different levels, so the researcher has to choose the level of measurement they will use before data collection begins.

Reliability and Validity as Applied to Critical Appraisal of Research

Reliability measures the ability of a measure to consistently measure the same way. Validity measures what it is supposed to measure. Do we have the need for both in research? Yes! If a variable is measured inaccurately, the data is useless. Let’s talk about why.

For example, let’s set out to measure blood glucose for our study. The validity is how well the measure can determine the blood glucose. If we used a blood pressure cuff to measure blood glucose, this would not be a valid measure. If we used a blood glucose meter, it would be a more valid measure. It does not stop there, however. What about the meter itself? Has it been calibrated? Are the correct sticks for the meter available? Are they expired? Does the meter have fresh batteries? Are the patient’s hands clean?

Reliability wants to know: Is the blood glucose meter measuring the same way, every time?

Validity is asking, “Does the meter measure what it is supposed to measure?” Construct validity: Does the test measure the concept that it’s intended to measure? Content validity: Is the test fully representative of what it aims to measure? Face validity: Does the content of the test appear to be suitable to its aims?

Leibold, 2020

Obtaining Samples for Population Generalizability

In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.

A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population. For example, if we wanted to investigate heart failure, there would be no possible way to measure every single human with heart failure. Therefore, researchers will attempt to select a sample of that large population which would most likely reflect (AKA: be a representative sample) the larger population of those with heart failure. Remember, in quantitative research, the results should be generalizable to the population studied.

There is a lot of confusion with students (and even some researchers!) when they refer to “random assignment” versus “random sampling”. Random assignment is a signature of a true experiment. This means that if participants are not truly randomly assigned to intervention groups, then it is not a true experiment. Remember, random sampling is a technique used to select individuals from a larger population to be included in a study. The key characteristic of random sampling is that every member of the population has an equal chance of being selected. Random assignment is a process used in experimental research to assign participants to different groups or conditions (e.g., treatment vs. control) in a way that each participant has an equal chance of being placed in any group.

A researcher will specify population characteristics through eligibility criteria. This means that they consider which characteristics to include (inclusion criteria) and which characteristics to exclude (exclusion criteria).

Inclusion and exclusion criteria are essential for defining who is eligible to participate in a study. Establishing inclusion and exclusion criteria for study participants is a standard, required practice when designing high-quality research protocols. Inclusion criteria are defined as the key features of the target population that the investigators will use to answer their research question (Boswell & Cannon, 2022). Typical inclusion criteria include demographic, clinical, and geographic characteristics.

For example, what might be your criteria for including someone from participation? Are any criteria based on age, gender, race, ethnicity, sexual orientation, origin, type and stage of disease, the subject’s previous treatment history, and the presence or absence (as in the case of the “healthy” or “control” subject) of other medical, psychosocial, or emotional conditions? If so, justify. For example, if you are doing a study on pregnant persons, will you exclude men (most likely, you will)? If you exclude men, even if it seems obvious, this needs to be determined and stated.

• Inclusion Criteria: These are the characteristics that participants must have to be included in the study. Inclusion criteria help ensure that the sample is relevant to the research question and that the findings will be applicable to the population of interest. For example, in a study on diabetic patients, inclusion criteria might include being diagnosed with Type 2 diabetes and being over the age of 18.
• Exclusion Criteria: These are the characteristics that disqualify potential participants from the study. Exclusion criteria are used to remove individuals who might confound the results or who do not fit the study’s focus. Continuing the example above, exclusion criteria might include pregnant patients or other chronic conditions that could interfere with the study outcomes.

For example, if we were studying chemotherapy in breast cancer subjects, we might specify:

• Inclusion Criteria: Postmenopausal women between the ages of 45 and 75 who have been diagnosed with Stage II breast cancer.

• Exclusion Criteria: Abnormal renal function tests since we are studying a combination of drugs that may be nephrotoxic. Renal function tests are to be performed to evaluate renal function and the threshold values that would disqualify the prospective subject is serum creatinine above 1.9 mg/dl.

Sampling Designs:

There are two broad classes of sampling in quantitative research: Probability and nonprobability sampling.

Probability sampling: As the name implies, probability sampling means that each eligible individual has a random chance (same probability) of being selected to participate in the study.

There are three types of probability sampling:

Simple random sampling: Every eligible participant is randomly selected (e.g. drawing from a hat).

Stratified random sampling: Eligible population is first divided into two or more strata (categories) from which randomization occurs (e.g. pollution levels selected from restaurants, bars with ordinances of state laws, and bars with no ordinances).

Systematic sampling: Involves the selection of every __th eligible participant from a list (e.g. every 9th person).

Nonprobability sampling: In nonprobability sampling, eligible participants are selected using a subjective (non-random) method.

There are four types of nonprobability sampling:

Convenience sampling: Participants are selected for inclusion in the sample because they are the easiest for the researcher to access. This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.

Quota sampling: Participants are from a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, the researcher could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.

Purposive sampling: A group of non-probability sampling techniques in which units are selected because they have characteristics that the researcher needs in their sample. In other words, units are selected “on purpose” in purposive sampling.

         Snowball sampling: Existing study participants recruit future participants from their acquaintances or networks. It is often used in studies                     where the population is hard to reach.

And, a 5th type: Consecutive sampling: A sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Consecutive sampling is defined as a nonprobability technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Here, the researcher selects a sample or group of people, conducts research over a period, collects results, and then moves on to another sample.

The goal in sampling is that it is a representative sample that is similar to the larger population. A representative sample should resemble the population in key demographic, behavioral, or other relevant characteristics. This ensures that the findings from the sample can be extrapolated to the population. The sample must be large enough to capture the diversity of the population and minimize sampling error (the difference between the sample results and the actual population parameters). A small sample might not adequately represent the population, especially in terms of its variability. To create a representative sample, the selection process must be unbiased, meaning that no specific group is more or less likely to be included than others. Bias in the selection process can lead to an unrepresentative sample, which will distort the study results.

Common Data Collection Methods in Quantitative Research

There are various methods that researchers use to collect data for their studies. For nurse researchers, existing records are an important data source. Researchers need to decide if they will collect new data or use existing data. There is also a wealth of clinical data that can be used for non-research purposed to help answer clinical questions.

Let’s look at some general data collection methods and data sources in quantitative research.

Existing data could include medical records, school records, corporate diaries, letters, meeting minutes, and photographs. These are easy to obtain do not require participation from those being studied.

Collecting new data:

Let’s go over a few methods in which researcher can collect new data. These usually requires participation from those being studied.

Self-reports can be obtained via interviews or questionnaires. Closed-ended questions can be asked (“Within the past 6 months, were you ever a member of a fitness gym?” Yes/No) or open-ended questions such as “Why did you decide to join a fitness gym?” Important to remember (this sometimes throws students off) is that conducting interviews and questionnaires does not mean it is qualitative in nature! Do not let that throw you off in assessing whether a published article is quantitative or qualitative. The nature of the questions, however, may help to determine the type of research (quantitative or qualitative), as qualitative questions deal with ascertaining a very organic collection of people’s experiences in open-ended questions. 

Advantages of questionnaires (compared to interviews):

• Questionnaires are less costly and are advantageous for geographically dispersed samples.
• Questionnaires offer the possibility of anonymity, which may be crucial in obtaining information about certain opinions or traits.

Advances of interviews (compared to questionnaires):

• Higher response rates
• Appropriate for more diverse audiences
  • Some people cannot fill out a questionnaire.
• Opportunities to clarify questions or to determine comprehension
• Opportunity to collect supplementary data through observation

Psychosocial scales are often utilized within questionnaires or interviews. These can help to obtain attitudes, perceptions, and psychological traits. 

Likert Scales:

• Consist of several declarative statements (items) expressing viewpoints
• Responses are on an agree/disagree continuum (usually five or seven response options).
• Responses to items are summed to compute a total scale score.

Visual Analog Scale:

• Used to measure subjective experiences (e.g., pain, nausea)
• Measurements are on a straight line measuring 100 mm.
• End points labeled as extreme limits of sensation

Observational Methods include the observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research.

When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation. When individuals are observed in their natural environment, this is known as naturalistic observation.  In participant observation, the researcher immerses himself or herself in the environment and becomes a member of the group being observed.

Biophysiologic Measures are defined as ‘those physiological and physical variables that require specialized technical instruments and equipment for their measurement’. Biophysiological measures are the most common instruments for collecting data in medical science studies. To collect valid and reliable data, it is critical to apply these measures appropriately.

• In vivo refers to when research or work is done with or within an entire, living organism. Examples can include studies in animal models or human clinical trials.
• In vitro is used to describe work that’s performed outside of a living organism. This usually involves isolated tissues, organs, or cells.

Let’s watch a video about Sampling and Data Collection that I made a couple of years ago.

References & Attribution

“Magnifying glass” by rawpixel licensed CC0

“Orange flame” by rawpixel licensed CC0

Bhandari, P. (2022). Levels of Measurement | Nominal, Ordinal, Interval and Ratio. Scribbr. https://www.scribbr.com/statistics/levels-of-measurement/ 

Polit, D. & Beck, C. (2021). Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research (10th ed.). Wolters Kluwer Health.