2response 1yahira Aviles Diazweek 10 Discussion Sampling And Collecti ✓ Solved

2 RESPONSE 1 Yahira Aviles Diaz Week 10 Discussion: Sampling and Collecting Quantitative and Qualitative Data COLLAPSE Top of Form Position A: Probability sampling represents the best strategy for selecting research participants. Sampling refers to the process of selecting participants for a research study. There are two main types of sampling processes: the probability sampling or random sampling, and the nonprobability sampling or nonrandom sampling. Out of the two types of sampling processes, probability sampling represents the best strategy for selecting research participants. According to Babbie (2017), Burkholder et. al (2020), and Teddlie and Yu (2007), probability sampling is the best strategy to select participants because it better represents the population in quantitative research.

Therefore, it allows researchers to estimate the accuracy of the sample representation of the larger group. Consequently, by eliminating bias, researchers draw better conclusions at the time of analyzing data. Random sampling gives each person an equal opportunity to be selected as a sample to represent its population. Teddlie and Yu (2007), explained that for instance, if a researcher would like stratified sampling by gender, they would have to classify the population in all males and all females. Then, the researchers would proceed to randomly select a sample from the male group and from the female group.

Select a data collection method (e.g., surveys, interviews, observations) and briefly explain at least one strength and at least one limitation. Interviews are one of the most used data collection method as they provide with information that is not limited by numerical factors. They not only provide participant’s responses, but also allow researchers and insight on participant’s tone, inflection, and body language. However, Burkholder et. al (2020) provided a table of possible limitations for interview data collection that could impair its integrity. Something as simple as rushing the interview, or as complex as including presuppositions or not considering issues of power or culture can negatively influence the research.

Identify a potential ethical issue with this method and describe a strategy to address it. Interviews potential ethical issue is confidentiality and subjectivity. Researchers ought to provide participants with a privacy assurance. To address subjectivity, researchers ought to construct and follow interview protocols (p. 150).

Relationship between measurement reliability and measurement validity in education. Reliability refers to the confirmability or repeated results each time a measurement is performed. In the field of education, reliability is easily influenced by different factors. Some examples of these factors are students reading proficiency, illness, number of items per standard, guessing responses, marking errors, language proficiency, misunderstanding instructions, etc. (Drost, 2011). On the other hand, validity refers to the accurate representation between results and research.

This means that the variable in effect caused the change researched. For instance, a low proficient student in reading may also, show low scores in every other subject. The student’s reading proficiency may affect the accuracy of a math assessment. Therefore, the math assessment is not a valid instrument to assess the math skill as it is assessing the student’s ability to read and understand, for example, a word problem or the instructions. References Babbie, E. (2017).

Basics of social research (7th ed.). Boston, MA: Cengage Learning. Burkholder, G. J., Cox, K. A., Crawford, L.

M., & Hitchcock, J. H. (Eds.). (2020). Research designs and methods: An applied guide for the scholar-practitioner. Thousand Oaks, CA: Sage. Drost, E.

A. (2011). Validity and Reliability in Social Science Research. Education Research & Perspectives , 38 (1), . Bottom of Form RESPONSE 2 steven stoner Sampling and Collecting Data COLLAPSE Top of Form Restate your assigned position on sampling strategies. Explain why this position is the best strategy for selecting research participants.

Support your explanation with an example and support from the scholarly literature. Nonprobability sampling is the better choice for selecting research participants since it allows participants to be hand-picked by the researchers. In nonprobability sampling, the researcher knows the participants, so the time needed to choose them is significantly reduced. Nonprobability sampling is also faster and more cost-effective since the timing required to create a random group is eliminated (Burkholder, 2020). Select a data collection method (e.g., surveys, interviews, observations) and briefly explain at least one strength and at least one limitation.

In nonprobability sampling, a popular method for selecting participants is purposive sampling. Purposive sampling is used by the researcher when there may be a limited number of people who would have enough knowledge to accurately participate (Onawuegbuzie, 2007). The idea of hand-picking the participants in a study is a strength of purposive sampling. The researcher knows who they are working with and understands that the information gathered will be accurate to the survey. The major weakness of purposive sampling is the high level of bias that can exist.

The researcher is picking people they know about the topic; inevitably, the researcher will choose those who share the same knowledge base or feelings as they do. Identify a potential ethical issue with this method and describe a strategy to address it. An ethical issue in purposive sampling is confidentiality. When conducting a study, the participant's confidentiality and anonymity are of the utmost importance to protect them from harm. In a purposive sample, the participants are known to the researcher, so the concept of anonymity does not exist.

Bias can also come into play since the participants are based on their knowledge of the topic. A strategy that can be used to address confidentiality in purposive sampling is selecting some participants that may not have the same knowledge and therefore are not known to the researcher. While this will move the process away from purposive sampling to a center degree, it will help ensure that the research remains unbias and anonymity is maintained. Explain the relationship between measurement reliability and measurement validity using an example from your discipline. Reliability and validity are used in research to check the quality of the research being conducted.

Reliability is used to check the consistency of the study, and validity is used to check on the accuracy (Burkholder, 2020). In my research about transgender athletes in sports, measurement reliability would ensure that the research is legit and the participants are chosen based on ethical considerations. Reliability would allow the research to be duplicated by another at another time. The study's validity would show that the study did focus on transgender athletes and the struggles they face. Validity would show that my research was in line with previous research that has been conducted.

Burkholder, G. J., Cox, K. A., Crawford, L. M., & Hitchcock, J. H. (Eds.). (2019).

Research design and methods: An applied guide for the scholar-practitioner . Sage Publications. Onwuegbuzie, A. J., & Collins, K. M. (2007).

A typology of mixed methods sampling designs in social science research. Qualitative Report , 12 (2), . Bottom of Form Discussion: Sampling and Collecting Quantitative and Qualitative Data It is often not possible or practical to study an entire population, so researchers draw samples from which they make inferences about a population of interest. In quantitative research, where generalization to a population is typically valued, a researcher’s ability to make such inferences is only as good as the sampling strategy she or he uses to obtain the sample. Once an appropriate sample has been obtained, data collection should involve valid and reliable measures to ensure confidence in the results, as well as the ability to generalize the research outcomes.

Although generalization is typically not a goal in qualitative research, sampling is just as important in qualitative and mixed methods research, as is obtaining reliable and valid results. Indeed, for quantitative, qualitative, and mixed methods research, sampling strategies and accurate data collection methods are critical aspects of the research process. Specific methods of data collection (e.g., surveys, interviews, observations) produce specific types of data that will answer particular research questions, but not others; so here too, as covered in previous weeks, the research questions inform how the data will be obtained. Furthermore, the method used to collect the data may impact the reliability and the validity of that data.

For this Discussion, you will first consider sampling strategies. Then, you will turn your attention to data collection methods, including their strengths, limitations, and ethical implications. Last, you will consider measurement reliability and validity in the context of your discipline. With these thoughts in mind, if your last name starts with A through L, use Position A. If your last name starts with M through Z, use Position B .

Position A: Probability sampling represents the best strategy for selecting research participants. Position B: Nonprobability (or purposive) sampling represents the best strategy for selecting research participants. REQURIES MEDIA

Paper for above instructions

Discussion: Sampling and Collecting Quantitative and Qualitative Data

In research, whether quantitative or qualitative, the selection of participants is a foundational step that can significantly influence findings. According to literature, the method of sampling dictates how well the findings can generalize to a larger population (Babbie, 2017). This discussion will explore the essential aspects of sampling strategies, data collection methods, granularity surrounding ethical issues, and the nuanced relationship between measurement reliability and measurement validity.

Sampling Strategies: Probability vs. Nonprobability Sampling

Position A: Probability Sampling

Probability sampling is hailed as the gold standard in selecting research participants, particularly within quantitative studies. This method allows each member of the population an equal chance of being selected, thereby minimizing bias and enhancing the representativeness of results. As highlighted by Burkholder et al. (2020), the effectiveness of this sampling strategy lies in its capacity to allow researchers to make statistical inferences and test hypotheses effectively under controlled conditions.
One widely used form of probability sampling is stratified sampling, which involves dividing the population into subgroups (or strata) and then randomly selecting samples from each subgroup (Teddlie & Yu, 2007). For instance, when researching a school’s academic performance across genders, researchers may categorize the population into males and females and then randomly select from each category. This stratification ensures representation across crucial demographic variables.

Position B: Nonprobability Sampling

In contrast, nonprobability sampling, especially purposive sampling, allows researchers greater flexibility and speed in participant selection. This method is particularly beneficial in exploratory or qualitative research where the goal is to gain in-depth insight from particular subgroups of interest (Onwuegbuzie & Collins, 2007). For example, a researcher might handpick participants who have specific knowledge about a sensitive issue, such as healthcare inequalities.
However, nonprobability sampling's primary drawback is its susceptibility to bias, as researchers may unintentionally select participants similar to themselves or who have shared perspectives, thus limiting the research's generalizability (Burkholder et al., 2020). This potential bias raises ethical concerns as the validity of findings based on biased sampling can be questioned.

Data Collection Methods: Strengths and Limitations

Strength of Interviews

Interviews stand out as a popular qualitative data collection method that provides nuanced insights through the analysis of tone, inflection, and body language, which are often lost in quantitative methods (Burkholder et al., 2020). For example, when investigating personal experiences surrounding mental health issues, interviews can help capture the emotional depth of participants’ stories, yielding data rich in context.
However, a significant limitation of interviews involves the risk of subjectivity, wherein researchers' biases may unintentionally influence how questions are framed or interpreted (Drost, 2011). As noted by Burkholder et al. (2020), these biases can distort findings, making it challenging to establish a consistent research narrative.

Ethical Implications of Interviews

Interviews pose various ethical issues, particularly concerning confidentiality and informed consent. As participants disclose personal information, researchers must ensure that they are protected from potential repercussions. Maintaining confidentiality is crucial, and researchers should develop robust plans for data storage and anonymization (Teddlie & Yu, 2007).
One strategy to mitigate these risks is to implement structured protocols that lay out confidentiality assurances and guidelines for data use. Researchers should openly communicate these protocols before commencing the interviews, ensuring that participants understand their rights and the measures in place to protect their information.

The Interrelationship Between Measurement Reliability and Validity

In the realm of educational research, the concepts of reliability and validity serve as pillars that uphold the quality of research findings. Reliability refers to the consistency of results across repeated measurements, while validity pertains to how accurately the instrument measures what it intends to assess (Drost, 2011).
For instance, when utilizing standardized tests to evaluate student learning, a reliable test will yield consistent results across different administrations. However, if the test is not valid, it may not accurately reflect the student’s knowledge or abilities. For example, when a student struggles with reading comprehension, their performance on a math assessment that includes word problems may be skewed by their reading struggles. This circumstance exemplifies how a lack of validity undermines the reliability of the test results.
The challenge emerges in ensuring that assessments are both reliable and valid. Researchers must rigorously pilot test their instruments, analyze data carefully, and be open to revising tools to achieve optimal levels of both measurement aspects (Burkholder et al., 2020). Additionally, continuous training for educators and researchers on how to interpret and implement assessment results can further bolster the effective use of these metrics.

Conclusion

Sampling strategies, data collection methods, and the intersection of reliability and validity are integral components of high-quality research. Probability sampling provides robust generalizability, while nonprobability sampling can offer rich qualitative insights. Interviews, as a data collection method, come with ethical concerns that must be navigated carefully. Finally, balancing reliability and validity is essential for drawing credible conclusions from research data. Ensuring ethical research practices while maintaining rigorous methodological standards contributes to the knowledge base and trustworthiness of findings in any scholarly discipline.

References

1. Babbie, E. (2017). Basics of social research (7th ed.). Boston, MA: Cengage Learning.
2. Burkholder, G. J., Cox, K. A., Crawford, L. M., & Hitchcock, J. H. (2020). Research designs and methods: An applied guide for the scholar-practitioner. Thousand Oaks, CA: Sage.
3. Drost, E. A. (2011). Validity and Reliability in Social Science Research. Education Research & Perspectives, 38(1), 105-123.
4. Onwuegbuzie, A. J., & Collins, K. M. (2007). A typology of mixed methods sampling designs in social science research. Qualitative Report, 12(2), 281-316.
5. Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77-100.
6. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Thousand Oaks, CA: Sage.
7. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Research Paradigm Whose Time Has Come. Educational Researcher, 33(7), 14-26.
8. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York, NY: McGraw-Hill.
9. Robson, C. (2011). Real World Research (3rd ed.). Chichester, UK: Wiley.
10. Maxwell, J. A. (2013). Qualitative Research Design: An Interactive Approach (3rd ed.). Thousand Oaks, CA: Sage.