Chapter 7: Population and Sample

The lecture goes through the fundamentals of population and sampling techniques used in research studies, with a focus on language education. It clarifies the meaning of the terms population and sample and the benefits and drawbacks of probability-based and non-probability-based sampling techniques. In order to assure the representativeness and generalizability of the study results, the course also stresses the need of choosing a good sampling procedure and figuring out the proper sample size. Additionally, it offers helpful advice on how to recognize the population and sample and how to choose the sample and conduct data analysis. The lesson gives students a solid knowledge of these crucial ideas and shows them how to put them into practice. It serves as a thorough introduction to population and sampling techniques in research studies for language education.

GUIDING QUESTIONS

1. Why is it important to choose a suitable sample and population for research on language teaching?
2. What various probability sampling techniques are there? What are each one’s advantages and disadvantages, and when is each one acceptable to use?
3. What are the different types of non-probability sampling methods? When is each one appropriate to use, and what are their strengths and limitations?
4. What ethical considerations must be taken into account when selecting a population and sample for language teaching research?
5. In what ways can the choice of sampling method affect the results and generalizability of research findings in language teaching?

POPULATION

The total set of people or things a research study focuses on is referred to as a population. For instance, any group of individuals who are learning a language or a particular target language, like English, might be included in a research study on language instruction. This might be achieved by researching a certain population, such as pupils in a particular school district, all local residents, or those who belong to a particular social group.

All students in a certain grade level or program enrolled at a particular institution may make up the population for a research study on language instruction and all language learners in a particular region or social group. The population can consist of all English language learners in a certain school district, for instance (Creswell & Creswell, 2017).

For instance, the population of all students participating in high school English as a Second Language (ESL) programs in a certain school district might be the subject of a research study on language instruction. The research might concentrate on acquiring data on the students’ language learning patterns, learning experiences, and attitudes. The results of such a research could assist educators in creating language-teaching programs that are more efficient and tailored to the requirements of the particular demographic.

According to Creswell and Creswell (2017), demonstrating the generalizability of the study results depends on precisely identifying the population. The population must be specified so that it answers the research question and that the study’s findings may be applied to the group being studied. The researcher must choose the demographic they aim for in a research study on language education and ensure it fits the research aims.

The research procedure in language education depends on the study’s population. The researcher may examine and interpret their data and extrapolate the findings to comparable communities by selecting the relevant population.

Any group of language learners, such as those learning a second language, those who are bilingual, or those learning their heritage language, might be the population interested in a study on language instruction. For instance, all students taking Spanish as a second language would make up the population if the research study’s goal is to examine the efficacy of flipped classroom education in teaching Spanish to college students.

After determining the population of interest, the researcher might choose a representative sample from that group to take part in the study. Depending on the study objective and the available resources, many sampling techniques, such as convenience, random, and stratified sampling, may be utilized.

Consider, for instance, that the researcher chooses to utilize convenience sampling and chooses a group of college students who are now enrolled in a Spanish language course at a certain institution. Therefore, the sample may not be entirely typical of all college students who are studying Spanish as a second language. However, assume the researcher chooses a sample of college students who are studying Spanish from various colleges around the nation using random selection. The sample is more likely to be representative of the population in such situation.

CASE STUDY

Case Study: Examining High School Students’ Attitudes Toward Language Learning

Ms. Perez, a language instructor at a nearby high school, is curious to know how her pupils feel about studying a second language. She wants to poll her pupils on their views toward language study, but she has to make sure that the sample she uses is typical of the high school students in the area.

Your tasks:

1. What demographics does Ms. Perez want to research, and how can she characterize them?
2. What kind(s) of sampling techniques may Ms. Perez use to choose her sample, and what are the benefits and drawbacks of each technique?
3. How can Ms. Perez choose the right sample size for her research?
4. Which ethical issues ought to be taken into account by Ms. Perez while choosing research participants?
5. What constraints should Ms. Perez be aware of when analyzing and interpreting the data gathered from her research and when extrapolating its results to the population of high school students in her region?

SAMPLING METHODS

In order to conduct studies that are relevant and fruitful, it is essential to carefully choose the participants. Various educational situations, student characteristics, and instructional methods make language teaching a complicated and varied profession (Mackey & Gass, 2021). The study topic, aims, and particular traits of the target group should all be taken into consideration while choosing participants. It is crucial to utilize suitable sampling techniques and consider factors like sample size, representativeness, and possible sources of bias to guarantee the validity and generalizability of research results (Johnson & Christensen, 2019).

Sampling is a crucial component of language education research since it helps identify participants from a broader community and produces relevant data for study. Probability sampling and non-probability sampling are the two main sampling techniques. Probability sampling is the process of choosing participants to guarantee that every member of the population has an equal chance of being included in the research. On the other hand, non-probability sampling describes the process of choosing participants when there is no assurance that everyone will have an equal chance of being included in the research.

The resources at hand, the study objective, and the population’s characteristics all have a role in the sampling technique used in language instruction studies. Before choosing a sample strategy that best addresses their research challenge, researchers must weigh the advantages and disadvantages of various sampling techniques.

When choosing participants for language instruction research, ethical aspects such as getting informed permission and preserving participant privacy and confidentiality should be taken into account in addition to sample techniques (Mackey & Gass, 2021). The possible effects of participant characteristics on the study findings (such as language ability, age, gender, and cultural background) should also be considered by researchers, along with practical recruitment and retention issues.

The process of choosing participants for research on language education is intricate and crucial, and it takes considerable organization and thought. Researchers may assure the validity and generalizability of their results and contribute to the development of knowledge in the area of language instruction by employing proper sampling techniques and taking ethical and practical considerations into account.

The many probability and non-probability sampling techniques utilized in language education research will be covered in this study. Each approach’s advantages and disadvantages will be discussed, as well as instances of how they have been used in various research situations. The study results’ validity and reliability will be discussed, as well as the significance of sample size, sampling frame, and sampling mistakes in language education research.

PROBABILITY SAMPLING

In quantitative research, probability sampling techniques such as random, stratified, cluster, and systematic sampling are advised because they result in representative samples that allow researchers to extrapolate findings to a wider population.

SIMPLE RANDOM SAMPLING

Every member of the population has an equal chance of being chosen for the sample using the probability sampling technique known as simple random sampling. When the population being studied is homogeneous and the goal of the research is to gather a representative sample of the population, this technique is advantageous.

In a basic random sample, every member of the population has an equal chance of being picked since the selection process is entirely random. A comprehensive list of the population’s members is compiled, and after that, people are chosen at random, guaranteeing that each person has an equal chance of being chosen for the research.

For instance, if a researcher wants to carry out a study on language teaching that involves students learning English as a second language (ESL), they can get a list of the students enrolled in the ESL program at the school and then choose students at random from that list using a random number generator or another random selection technique. Each participant in the program would have an equal chance of being chosen for the research using a simple random selection method.

One of the easiest and most often used probability sampling techniques, according to Babbie (2020), is simple random sampling. It is often used in studies that call for equal representation of the population being studied. Simple random sampling may provide a representative sample from a small sample size, but its efficacy is based on the size and homogeneity of the population.

STRATIFIED RANDOM SAMPLING

A probability sampling technique called stratified random sampling includes dividing the population into divisions called strata that have similar traits and proportionately choosing sample members from each stratum. In research when the population is varied and comprises subgroups that vary from one another on important characteristics, this strategy is helpful.

The population is initially split into a number of strata depending on pertinent characteristics to execute stratified random sampling. For instance, the population might be split into subgroups depending on variables like competence level, age, gender, or cultural background in a study on language teaching. A random sample is chosen from each stratum once the strata have been determined, ensuring that the participants are fairly represented (Bryman, 2016).

Consider a researcher who is looking at the impact of English language instruction on pupils enrolled in a high school ESL program. Freshmen, sophomores, juniors, and seniors might be used to categorize the population of ESL students at a particular high school, and then a random sample could be taken from each grade level (Bryman, 2016). By doing this, it is made sure that the sample is diversely representative of the population.

By lowering the possibility of bias that may arise from a homogenous sample, stratified random sampling increases the representativeness of a sample. It guarantees a proportionate representation of various demographic subgroups, enabling more precise deductions from the gathered data (Bryman, 2016).

Stratified random sampling, in conclusion, is a potent tool for language education researchers that guarantees a robust representation of the population being investigated. A stratified random sample might provide more insight into the traits and experiences of these subgroups if population variables are carefully considered and relevant strata or subgroups are found.

CLUSTER SAMPLING

A probability sampling technique called cluster sampling divides the population into smaller clusters based on proximity or other traits, and then randomly selects clusters from each cluster to take part in the research. When selecting people from the total population would be time- or logistically-intensive, cluster sampling is utilized.

Clusters in cluster sampling are often geographic areas where people are located near to one another, such as schools, residences, or hospitals. A sample frame made up of these clusters is utilized to create a sample for the survey or research. All people, a randomly chosen sample of persons, or a subsample of individuals from each chosen cluster may be chosen and included in the research (Bryman, 2016).

For instance, all of the English language learners in a certain region may be the population of interest in a research on English language education. An strategy known as cluster sampling might be utilized, in which institutions or schools that provide English language instruction are chosen, and then a sample of students is taken from those institutions.

The fact that cluster sampling saves on travel and other logistical costs that could be associated with sample bigger populations is a major benefit. Additionally, it helps when a study’s objective is to examine variations across populations. It is vital to keep in mind that this form of sampling runs the danger of grouping people with similar traits together, therefore it is crucial to make sure the chosen clusters are varied and representative of the community.

In conclusion, cluster sampling provides a practical and effective method for conducting studies on large populations that would otherwise be difficult to reach in the field of language teaching research. By segmenting the population into more manageable, smaller clusters, researchers can collect useful data with less time and money.

SYSTEMATIC SAMPLING

A kind of probability sampling known as systematic sampling involves randomly choosing members from a community at predetermined intervals, such as every tenth person. It is a simple technique of sampling, but it may add biases into the sample if the population or list being sampled exhibits a periodic pattern, resulting in an unrepresentative sample.

When there is a full list of students or learners accessible and a sample size will be selected based on the necessary number of students, systematic sampling may be an effective approach of choosing participants in language teaching research. Consider a researcher who wants to examine the oral competence of students in a foreign language program. In such situation, they may access every nth individual on the list to take part in the research using a systematic sampling strategy.

When the population is uniform and well-organized, such as when selecting a sample of academic institutions with the same curriculum and resources, systematic sampling is also helpful. When a comprehensive list of a particular population is available, Johnson and Christensen (2019) contend that systematic sampling is effective in educational research. It also takes less time than alternative probability sampling techniques.

However, systematic sampling is less generalizable in nature than other probability sampling techniques like simple random and stratified sampling since it adds a possible bias into the sample owing to a periodic pattern or orderliness of the list. Therefore, it is important to analyze each research work for biases that might be induced by the sampling process.

In conclusion, language-teaching researchers should be cautious to assess potential biases before using systematic sampling to select participants and ensure that the population is homogeneous and ordered. This is true even though systematic sampling is an efficient and simple method of sampling compared to other probability sampling methods.

NON-PROBABILITY SAMPLING

In qualitative research, when getting in-depth knowledge and comprehension of a topic is the main goal instead of generalization, non-probability sampling techniques including convenience, snowball, purposive, and quota sampling are often utilized.

CONVENIENCE SAMPLING

A non-probability sampling technique called convenience sampling includes choosing research participants who are most readily available and accessible. When a researcher has limited time or resources to carry out the study or when the research issue is tightly focused on a certain demographic that is easily accessible, convenience sampling may be a practical alternative in language instruction research.

For instance, a language instructor might simply sample every student in their class at a given moment in order to assess the efficacy of a specific teaching strategy, regardless of whether or not they were typical of the larger population of language learners.

Convenience sampling has the benefit of being quick and easy to reach participants. In exploratory research, when the goal is to develop hypotheses or preliminary insights, it is also helpful (Bryman, 2016). Convenience sampling’s primary drawback is that the population from which the participants are drawn is not well represented. The study’s conclusions thus cannot be applied to other groups or environments.

Therefore, researchers who employ convenience sampling in their studies of language education should be wary about generalizing their results to other groups or contexts. When utilizing convenience sampling, researchers should make sure the sample is homogenous and that the findings are interpreted in light of the sample’s constraints.

Conclusively, convenience sampling in language teaching research is rapid and economical, but it carries a risk of bias in selection and may not be generalizable to a larger population. Researchers that use convenience sampling need to be mindful of its drawbacks and how they could affect the reliability and validity of their results.

SNOWBALL SAMPLING

A non-probability sampling technique called snowball sampling is finding and recruiting one individual who meets the requirements for a study, then requesting that person to recommend other people who may also be qualified to participate (Frankfort-Nachmias, Leon-Guerrero, & Davis, 2019). Reaching out to groups who are difficult to reach, such as English language learners who may have limited access to formal education, resources, and support networks, requires the use of snowball sampling in language teaching research.

Snowball sampling in language teaching research may be suitable for researching certain groups, such as refugees, foreign students, or English language learners with poor ability. The main goal of snowball sampling is to take advantage of the connections that exist within these communities, where a first encounter might result in the referral of other participants.

Snowball sampling has the benefit of providing access to communities where other sample techniques would not be feasible. A more detailed picture of the study issue is provided by snowball sampling, which also includes people who have similar socioeconomic conditions, linguistic backgrounds, and attitudes (Bryman, 2016). Snowball sampling, however, might result in sample biases if the participants’ particular attitudes and ideas about a subject tend to draw others with like beliefs or traits.

Researchers should exercise caution when using snowball sampling in language instruction research since the sample produced is skewed toward individuals who share certain traits but are not necessarily representative of the greater community. Therefore, it is important for researchers to properly record the sampling process and evaluate the generalizability of their results.

In conclusion, snowball sampling is a useful sampling technique in language teaching research that may provide insightful data on groups who are challenging to reach. Although snowball sampling offers the ability to choose a distinctive sample, researchers should be mindful of its constraints and consider their influence when interpreting study results.

PURPOSIVE SAMPLING

Purposive sampling, sometimes referred to as judgmental or selective sampling, is a non-probability sampling technique that entails the deliberate selection of participants who satisfy certain requirements pertinent to the research topics under investigation. This approach is often used in qualitative research on language education to locate and enlist certain participants, such as linguists, policymakers, or administrators of language programs.

Purposive sampling may be suitable in language education research when specialized knowledge or skill is needed and other sampling techniques may not be able to provide the requisite sample. To achieve the study goal, purposive sampling research enables a greater level of participant characterization and categorization (Tashakkori & Teddlie, 2016).

Purposive sampling has the capacity to provide informed and relevant participants who are appropriate for the study topic, which is one of its key benefits. This allows for a deeper interpretation and comprehension of the study topic by the researcher (Bryman, 2016). Purposive sampling, which seeks to choose particular individuals who fit the study requirements, cannot, however, ensure the representation of the whole population or the group being investigated.

When researching language policies in a certain nation, a researcher might purposefully choose participants who have knowledge of the creation, implementation, and assessment of language policies. By speaking about the language policy, participants may provide insights that would be impossible to get from random selection alone.

To sum up, purposive sampling is a useful sampling technique in language instruction research for looking at certain areas of interest when a distinct population is needed. The study’s goals, the characteristics of the population being examined, and any possible biases that could be present must all be carefully taken into account by the researcher.

QUOTA SAMPLING

In order to achieve a representative sample, quota sampling is a non-probability sampling technique that includes choosing participants based on certain pre-determined qualities. The researcher starts by segmenting the population into strata based on certain demographic or behavioral characteristics, including educational attainment or language competence. Following that, candidates are chosen until predefined quotas for each stratum are satisfied (Bryman, 2016).

When certain subgroups of the population, such pupils who speak a particular language, must be represented in the sample but there are insufficient resources to sample the whole population, quota sampling may be utilized in language teaching research. The group may be divided into strata by factors like language ability, and the researchers can then make sure that participants are chosen up until the allocated quota for each stratum is met (Bryman, 2016).

One benefit of using quota sampling in language instruction research is that the researcher may guarantee that each subgroup is adequately represented by limiting the number of participants in each category within the community (Dörnyei, 2007). When convenience sampling is undesirable or when a balanced sample is required but the sample size is limited, quota sampling may be helpful.

However, as the sample is not randomly chosen from the population, quota sampling is prone to bias, much like other non-probability sampling techniques. The findings cannot be extrapolated to the whole population, which limits the generalizability of the conclusions.

To sum up, quota sampling is an effective sampling technique in language instruction research when a varied population has to be gathered without having the funds to examine the complete community. When utilizing this approach, researchers must take into account its drawbacks and be aware of the possibility of bias introduction.

Table 1. Types of sampling, summarized

Sampling Type	Approach	Description
Simple Random Sampling	Random	Each member of the population has an equal chance of being selected for the sample.
Stratified Random Sampling	Random	The population is divided into subgroups based on a characteristic of interest (e.g., age, gender) and a sample is randomly selected from each subgroup.
Cluster Sampling	Random	The population is divided into clusters (e.g., geographical areas), and a sample of clusters is randomly selected for inclusion in the study.
Systematic Sampling	Random	Members of the population are selected at regular intervals, such as every 10th person.
Convenience Sampling	Non-random	Members of the population are selected based on their availability or willingness to participate, rather than by chance.
Snowball Sampling	Non-random	Participants are recruited through referrals from other participants who share a certain characteristic or belong to a certain group.
Purposive Sampling	Non-random	Participants are selected based on a specific purpose, such as having expertise in a certain area or being a key informant.
Quota Sampling	Non-random	A sample is selected to meet predetermined quotas for certain characteristics (e.g., age, gender, occupation).

The left column in this example lists the many forms of sampling, the center column identifies whether the technique is non-random or random, and the right column gives a short explanation of each type of sample. Please take note that this is only a collection of ways that are often used rather than a comprehensive list of all feasible sample kinds or methodologies.

SAMPLE SIZE AND SURVEY RESPONSE RATES

In order to ensure that research results are reliable and valid, it is crucial to take survey response rates and sample sizes into account. Researchers must choose an adequate sample size and work to get a high response rate from participants in order to get accurate and representative findings.

The research topic, the degree of accuracy necessary, and the variability of the population being researched must all be taken into account when determining the right sample size (Cohen, Manion, & Morrison, 2018). Calculating the sample size may be done using statistical software or online calculators, which can assist guarantee that the sample is big enough to provide accurate findings.

It may be difficult to increase survey response rates, but doing so is necessary to minimize nonresponse bias and guarantee that the sample is representative of the population (Dillman, Smyth, & Christian, 2014). Offering incentives, using numerous contact channels, and stressing the significance of the study are all tactics for boosting survey response rates (Babbie, 2020).

Researchers must take into account possible causes of bias in their sampling and survey methodologies in addition to sample size and response rates. For instance, self-selection or convenience sampling might produce biased samples that do not fairly represent the community under study (Creswell & Creswell, 2017). To solve these issues, researchers may use stratification, random sampling, or other approaches to guarantee that the sample is impartial and representative.

Although random selection is essential for guaranteeing the validity of a research, it is not the only element that impacts the sample’s representativeness. The sample’s size is also crucial since a small sample may not be able to adequately represent the population’s variety and variability (Johnson & Christensen, 2019). Even if random sampling is utilized, if the sample is not representative, the results could not be generalizable to a larger population.

Although there are no hard-and-fast recommendations for choosing sample sizes, researchers may utilize certain broad principles. Census sampling may be useful for survey research if the population is small (less than 200 people). Populations under 400 may only need a sample of 50%, but populations exceeding 1,000 may need a sample of 20% or more (Krejcie & Morgan, 1970). 350 to 500 people may be enough for sampling in bigger populations of 5,000 or more. A minimum of 30 individuals should be evaluated in correlational investigations, although experimental research studies often call for at least 30 participants per group (Johnson & Christensen, 2019).

It is crucial to remember that these recommendations do not apply in all cases and could need to be modified based on the precise research topic and setting. To make sure that the results of their studies are accurate and trustworthy, researchers should carefully assess their sampling strategies and sample sizes.

ETHICAL ISSUES AND PARTICIPANTS

Informed permission, confidentiality, privacy, and possible participant injury are some of the ethical concerns with quantitative research in language instruction. In order to safeguard the rights and welfare of participants and to make sure that the study is carried out ethically, these problems must be taken into account.

The ethical concerns of working with participants must be taken into consideration by researchers when they perform study on language education. Research participants may be exposed to dangers as a result of their participation, and researchers have a duty to safeguard their wellbeing (Mackey & Gass, 2021). Additionally, participant-related ethical concerns may have an influence on the research’s validity and dependability.

INFORMED CONSENT

Informed consent is one ethical problem that researchers need to take into account. Before taking part in a research, participants must provide their informed permission. According to the American Psychological Association (2017), informed consent requires that participants be made aware of the study’s objectives, the methods involved, any risks or rewards, and their ability to revoke permission at any time. Students, instructors, or other stakeholders may participate in language instruction research, and it’s crucial to have their informed permission.

A key element of ethical research is informed consent. Prior to giving their informed permission to participate, participants must be fully informed about the study’s objectives, methods, risks, and benefits. Additionally, researchers must make sure that participants are aware of the study’s voluntary nature and their freedom to discontinue participation at any moment without incurring any fees or losing any advantages. Written permission forms or oral processes may both be used to acquire informed consent.

A sample for Informed consent:

Informed Consent Form

Title of the Study: [Insert the title of the study]

Introduction: You are being invited to participate in a research study conducted by [Insert name of the researcher]. The purpose of this study is to investigate [Insert the purpose of the study].

Procedures: If you agree to participate, you will be asked to [Insert a description of the study procedures]. The study will take approximately [Insert the estimated time for the study]. All data collected from you will be kept confidential and anonymous. The data will be used only for research purposes.

Benefits: There are no direct benefits for participating in this study. However, your participation will contribute to the understanding of [Insert the study purpose].

Risks: There are no known risks associated with participating in this study.

Voluntary Participation: Your participation in this study is completely voluntary. You may choose not to participate, or you may choose to withdraw at any time without penalty or loss of benefits to which you are otherwise entitled.

Confidentiality: All data collected from you will be kept confidential and anonymous. The data will be stored securely and will only be accessible to the researcher.

Contact Information: If you have any questions about the study, please feel free to contact [Insert name of the researcher and contact information].

Consent: I have read and understand the information provided above. I voluntarily agree to participate in this study.

Signature: ____________________________Date: _____________________

Printed Name: _________________________

CONFIDENTIALITY AND PRIVACY

Privacy and secrecy are further ethical concerns. The privacy and confidentiality of participants’ personal information must be safeguarded by researchers. This entails securing data storage and maintaining participant anonymity in reports that are released (Mackey & Gass, 2021).

Privacy and confidentiality are significant ethical issues in quantitative research. Participants have the right to anticipate that their privacy and personal information would be respected. Participants’ data must be protected and kept out of the hands of anybody other than the study team, according to researchers. Data encryption, password protection, or physical security measures like locked file cabinets may all help accomplish this.

An example of a privacy and confidentiality declaration

Confidentiality and Privacy Statement

As a participant in this study, please be assured that all data collected from you will be kept confidential and anonymous. Your privacy will be protected, and your participation will remain strictly confidential.

The data collected from you will only be used for research purposes and will be kept secure and inaccessible to anyone outside of the research team. The data will be stored in a password-protected computer or in a locked cabinet, and only the research team will have access to it.

Your name will not be used in any reports, presentations, or publications. Instead, a unique identifier will be assigned to your data to ensure that your privacy is protected.

In addition, the results of this study will be reported in aggregate form, meaning that the data will be presented in a way that does not identify individual participants.

If you have any concerns or questions about the confidentiality and privacy of your data, please do not hesitate to contact the research team.

Thank you for your participation in this study.

POTENTIAL HARM

Another crucial ethical factor in quantitative research is the possibility of participant damage. Researchers must make sure that research hazards are kept to a minimum and that participants don’t suffer any bodily, psychological, or emotional damage. When possible damage cannot be completely avoided, researchers must take precautions to lessen it and provide assistance to subjects who may feel upset or uncomfortable.

Researchers need to take into account any possible negative effects of their work. Physical, psychological, or societal harm are all possible. Researchers must make sure that any risks are balanced against the possible rewards of their work (American Psychological Association, 2017) and that any potential damage to participants is minimized.

An example of a warning for harm:

Potential Harm Statement

As a participant in this study, we want to ensure that you are aware of any potential risks or harms associated with your participation. While we do not anticipate any physical harm, there may be some emotional or psychological discomfort associated with your participation in this study.

We want to assure you that your participation in this study is completely voluntary, and you may choose to withdraw at any time without penalty or loss of benefits to which you are otherwise entitled.

If at any point during the study you feel uncomfortable or distressed, please notify the researcher immediately. The researcher will provide you with support and assistance to address any concerns you may have.

We take the well-being and safety of our participants very seriously, and we will do everything possible to minimize any potential risks or harms associated with your participation in this study.

Thank you for your participation in this study.

In conclusion, ethical issues are crucial in quantitative studies of language education. Researchers may assist to guarantee that their study is useful, important, and advances the area by taking into account the rights and welfare of participants and doing it ethically. When doing research on language education, participant ethics are of utmost importance. Informed permission must be obtained, confidentiality and privacy must be protected, and participants’ potential damage must be kept to a minimum.

PROBLEM-SOLVING

Problem: A researcher in language instruction is interested in determining if a novel approach to teaching English to non-native speakers is beneficial in a particular school district. The population’s size and the best way to choose a representative sample from the district are unknowns to the researcher.

CHAPTER SUMMARY

The basics of population and sampling approaches in research studies are covered in Chapter 7 of quantitative research in language education. The chapter describes and distinguishes between a sample and a population and covers the benefits and drawbacks of probability-based and non-probability-based sampling techniques. It emphasizes how important it is to use a suitable sampling technique and establish an acceptable sample size in order to guarantee the representativeness and generalizability of the study results. Additionally, the chapter offers helpful advice on how to choose a sample, identify the population and sample, and evaluate the data. Overall, the chapter gives readers a thorough introduction to population and sampling methodologies used in research studies for language education, giving them a solid grasp and example of how to use these fundamental ideas.

QUESTIONS FOR REVIEWING THE LESSON

1. What does a research study’s population mean?
2. What does a research study sample mean?
3. What distinguishes non-probability sampling from sampling based on probability?
4. Give some instances of non-probability sampling techniques.
5. What are some benefits and drawbacks of probability sampling?

Why is simple random sampling important?

7. How does utilizing simple random sampling compare to other methods?

Stratified random sampling – what is it?

9. What distinguishes stratified random sampling from ordinary random sampling?
10. How does utilizing stratified random sampling compare to other methods?

REFERENCES

American Psychological Association. (2017). Ethical principles of psychologists and code of conduct (2002, amended effective June 1, 2010, and January 1, 2017). https://www.apa.org/ethics/code/index

Babbie, E. R. (2020). The practice of social research. Cengage learning.

Bryman, A. (2016). Social research methods. Oxford University Press.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). John Wiley & Sons.

Dörnyei, Z. (2007). Research methods in applied linguistics: quantitative, qualitative, and mixed methodologies. Oxford University Press.

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2019). Social statistics for a diverse society. Sage Publications.

Johnson, R. B., & Christensen, L. (2019). Educational research: Quantitative, qualitative, and mixed approaches. Sage publications.

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.

Mackey, A., & Gass, S. M. (2021). Second language research: Methodology and design. Routledge.

Tashakkori, A., & Teddlie, C. (Eds.). (2016). Handbook of mixed methods in social and behavioral research. Sage Publications.

The lecture goes through the fundamentals of population and sampling techniques used in research studies, with a focus on language education. It clarifies the meaning of the terms population and sample and the benefits and drawbacks of probability-based and non-probability-based sampling techniques. In order to assure the representativeness and generalizability of the study results, the…