2 or more references Introduction Populations, Sampling, and Sampling Strategi

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Introduction

Populations, Sampling, and Sampling Strategies
This week we will explore the implementation (how) phase of the research process which includes choosing the population (sample) you are interested in studying, selecting a sampling strategy, determining the data you need to collect (type), and how to collect the data (instruments). When reflecting back about our PICO learning, a population (P) should be identified for investigation. It is seldom realistic, or even possible, to conduct research on the entire population (Houser, 2018; Polit & Beck, 2018).
Population = An entire set of subjects of interest
Sample = Subset carefully selected characteristics from the population
Instead, sampling involves carefully selecting a group of people, events, objects, or other elements as a representative of the population of interest (Houser, 2018); generally the terms subjects or participants will be used and include the defining sampling eligibility or inclusion criteria -which is a set/list of key objective attributes/characteristics of the subjects to be included in the study sample.
There are many key inclusion attributes/characteristics used to define the population of interest (P) or sample/subject characteristics; the below represents a few:
Age
Gender
Diagnosis/condition/issue
Race
Level education or years of experience
Setting-unit/department, primary care clinic
Exclusion characteristics would be attributes that cause a person to be excluded from the study. It is essential for the authors to include key demographics or descriiptors of the sample/subjects.
In addition to sample characteristics, the setting for the study is the place where data collection occurs; this may be in a hospital, clinic, school, home, or other locations. The setting may indicate if the research was conducted in a rural or urban setting, a large or small setting, and so forth. As you are reading through articles, review how well the characteristics of sample subjects and the study setting match your own setting —the closer the resemblance, the more applicable the evidence yielded from the research may be.
The sampling strategy is important because a significant sampling error could distort the findings and render them unreliable, invalid, and/or not able to be generalized by the entire population. The sampling strategy considers how subjects are recruited, selected and, when appropriate, assigned to groups. For key elements of an effective sampling strategy review pp. 179-173 of our Houser textbook.
The sampling strategy considers how subjects are recruited, selected and, when appropriate, assigned to groups. There are two major categories of sampling: (1) probability, also known as random sampling, and (2) nonprobability sampling.
Probability or Random Sampling
Probability in sampling increases the chances that the sample accurately represents the population. Probability sampling is preferred in quantitative research because the goal is to generalize findings from the sample to the population. Probability sampling is not appropriate for qualitative studies because this type of research does not seek to generalize results. Two criteria must be met for achieving a probability sample – random selection and independent selection. Read Houser for more details.
Random selection
In probability sampling, ideally, every member of the population has the same chance to be selected as a subject, but sampling the entire population is rarely feasible, and therefore, seldom used.
Random sampling involves choosing subjects at random from the population. This strategy should evenly distribute characteristics found in the population to the sample. These characteristics are called extraneous variables (EV). Extraneous variables are not the focus of the research study, but their presence may influence the dependent variable (DV), or outcome. Although random sampling is easier to achieve than sampling the entire population.
Random assignment is much more likely to be the sampling strategy chosen by the researcher. First, the researcher selects subjects who meet certain criteria found in the population. Next, the researcher randomly assigns the subjects to groups to increase the likelihood that any characteristics (EVs) that might affect the outcome (DV) will be equally distributed among the groups. Random assignment to groups is the major way to control for EVs in experimental studies.
Independent selection
The second criterion for achieving a probability sample is independent selection. This means that the selection of one subject is independent, or separate, from the selection of another. If independence is violated, the subjects may share characteristics that will show a correlation due to the sampling strategy instead of the effect of the independent variable (IV). Data results could be distorted.
Nonprobability sampling
Involves a strategy where subjects are not selected at random. Instead, they may be chosen due to convenience or for a particular purpose.
Convenience sample
The convenience sample is the most common strategy in nursing studies. As the name implies, the subjects are chosen by the researcher because they are accessible. The weakness of convenience sampling is that the characteristics of the population Extraneous variables (EVs) are less likely to be distributed evenly among the sample. Bias may occur that will negatively influence the outcomes (DV) of the research. The researcher may decide to use a convenience sample to recruit and select subjects, and then make random assignments to groups to minimize the chance of bias by evening distribution of EVs over all groups. Convenience sampling is a common sampling approach for quantitative studies.
Purposive sample
The purposive sample is one where selection of subjects is intentional, or done on purpose. This is a common strategy in qualitative studies where probability sampling is not needed because there is no need for generalization to a larger population. Subjects are carefully chosen for their ability to inform and enlighten the researcher about a phenomenon or other aspect of the research question under consideration.
Sample Size and Power
Sample size is the number of subjects that are studied. Qualitative studies may have rules and criteria for what is an appropriate sample size, but they are not as strict as those for quantitative studies. A case study may have only one subject. Therefore, samples in qualitative studies can be small in size. Typically, the researcher will recruit and collect data on subjects until saturation is reached, which is the point at which the researcher determines no new results are emerging. Saturation is one way to build confidence in the results of a qualitative study.
In quantitative research, a power analysis determines how large a sample should be to yield statistically significant results. In general, the sample size for a quantitative study must be a minimum of 30 subjects per group. Generally, larger sample sizes increase the likelihood that the researcher will notice subtle differences among groups.
The researcher may report the number of subjects who were recruited, the number that consented to participate, and the number that dropped out during the study. Bias may be detected if a certain segment of the population was not represented in the final results because they were not recruited, did not consent, or dropped out. When the potential for bias exists, the trustworthiness of the findings may come into question.
When you read the research report:
Look for a descriiption of the sampling strategy and how it was applied.
Does the strategy fit with the research design?
If this was a quantitative study, were there enough subjects to generalize the findings to the population?
If this was a qualitative study, was saturation achieved?
Data and Measurement
Measurement is “assigning numbers or some other classification by determining the quantity of a characteristic that is present” (Houser, 2018, p. 190). Numbers are used to collect data. According to Houser, (2018, p. 190), numbers are:
“objective
standardized
consistent
precise
statistically testable
undefined”
The researcher may work with numbers that can be calculated and have numerical value; for example, body temperature. At other times, the researcher assigns numbers to traits simply to classify them.
Data and Levels of Measurement
When thinking about quantitative research, variables must be expressed as numbers in order to use statistics for analysis. Variables are measurable characteristics identified from the elements of the research question (PICOT) that can be measured in a detectable way (Houser, 2018). Different types of numbers have different levels of measurement. See the table below for examples of differing types of measurable data and categories of levels of measurement.
Table 5.1
Levels of Measurement
Level of Measurement Descriiption Examples of Measurable Data Implications for Statistical Testing
Nominal
Variables are
categorized data;
classified; and
not ordered.
Gender
Ethnicity
Religion
Subjects cannot be compared. Analysis may include frequencies in each category. Analyze with nonparametric statistics.
Ordinal
Variables are
categorized data;
classified;
ordered and may be ranked; and
not proportional, or no fixed intervals.
Pain scale
Small, medium, large amounts
First place, second place, third place
Subjects can be compared. Analysis may include frequencies and percentiles. The median may be computed. Analyze with nonparametric statistics.
Interval
Variables are
continuous data;
quantified;
proportional, or fixed intervals; and
no true zero.
Fahrenheit or Celsius body temperature
Height
Values can be added and a mean computed. Analyze with parametric statistics.
Ratio
Variables are
continuous data;
quantified;
proportional, or fixed intervals; and
true zero.
Body weight
Heart rate
Values can be added and a mean computed. Analyze with parametric statistics.
Errors
Errors may occur when collecting data. A measurement error is the difference between the true number and the number that the instrument reads. Read more about random and systematic errors in your textbook. Errors in measurement can ripple throughout the rest of the research process and lead to faulty findings.
Data Collection and Instruments
Reliable, valid, clear, consistent, and unbiased data should be collected by trustworthy, credible, reliable, valid instruments. Data can be collected by primary data collection methods and secondary data collection methods. Refer to Houser for more details.
The computer industry has adopted the acronym GIGO, or garbage-in garbage-out, to explain that no matter how well information is processed, the quality of the information that comes out can be no better than the quality of the information that goes in (Kilkenny, & Robinson, 2018). If data are improperly collected, the findings based on that data are worthless, or garbage. How does the researcher collect reliable, valid, clear, consistent, and unbiased data?
The following are primary data collection methods, where the researcher or data collector directly measures the subjects (Houser, 2018):
Physiologic measures involve instruments that measure physiologic dimensions, such as blood gases, pulmonary function, and other diagnostic tests.
Psychometric instruments include scales or surveys.
Interviews and questionnaires collect subjective data, such as attitudes, or descriiptions of experiences provided by individuals or focus groups.
Observation obtains objective data related to behavior that the subject may not be able to describe, such as activity during sleep or agitation in the person withdrawing from alcohol.
Secondary data may be collected from sources that were not created for the current research study. Typically, the researcher looks through records, or “mines,” the relevant data that pertains to the variables. Includes data that has already been collected and examples include but are not limited to:
patient charts;
census reports;
public health records.
Keep in mind, the “survey is the most common method to collect research data” (Houser, 2018, p. 195).
Student Story Transcriipt
Instruments for Data Collection
Instruments measure variables (captures the data). Instruments must be reliable and valid in order to yield useful data results/analysis.
Reliable instruments measure a variable with precision and consistency.
Valid instruments measure in a manner that is accurate and truthful. A valid instrument measures the correct measure/data.
An instrument may be reliable but not valid in that it may consistently measure something that is not accurate. Instruments must be reliable in order to be valid and both attributes must exist in order to measure data in a way that inspires confidence in the research findings. Each method of data collection has its own strengths and weaknesses with respect to reliability and validity.
The methods section of articles you read should describe the measurements and the instruments that the researcher used to collect data. The researcher should describe the reliability and validity of the instrument. The existence of both attributes lends credibility to the claims that the researcher makes in the findings.
Answer the following question about one popular type of data collection instrument.
Question and Answer Transcriipt
As you are reading the methods section of articles, take notices of the samples/subject attributes. Read the author’s descriiption of data and measurements.
Where the attributes defined?
Do the data collection methods accurately measure the attributes?
Consider the reliability and validity of the data gathering tools/instruments.
The accuracy of the results from analysis of the data depend on these considerations. These are issues that the researcher must consider when collecting, analyzing, and interpreting data.
Reading Research Literature
When reading research studies, the details of the implementation are typically found in the sample and methods or procedures section. The authors should describe the details of how the study was carried out in such a way that the reader (you) should be able to actually replicate their study. These details should include information about:
population or sample
sample characteristics/attributes, selection and techniques
data:
what data measures were collected
when was data collected
how was data was collected
where was data collected
data collection tools/instruments—validity and reliability
Remember, it is all about the details!
Gaining insight into how to read various sections of a research article/study can help you determine whether the research is relevant to your practice, and provides evidence upon which to base your decisions.
Samples
Information about the samples is usually located in the methods section; it may also be labeled samples, subjects, participants. Some information may be in the abstract. Characteristics of the sample can be found in the analysis or results section.
Data Collection
A thorough descriiption should be provided in the methods section; sometimes it may be called procedures or protocols. This should be a major part of the write-up of the study as details are needed for a replication study.
When reading research, the methods section should describe the measurements and the instruments that the researcher used to collect data. The researcher may describe the reliability and validity of the instrument. The existence of both attributes lends credibility to the claims that the researcher makes about the findings.

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