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Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India

Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from... International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India 1 2 H. R. Ganesha & Aithal P. S. Research Professor, Institute of Management & Commerce, Srinivas University, Mangaluru, India, and Board Member, Gramss Retail Trading Private Limited, Bengaluru, India, OrcidID: 0000-0002-5878-8844; E-mail: hrganesha@yahoo.co.in Professor & Vice-Chancellor, Srinivas University, Mangaluru, India, OrcidID: 0000-0002-4691-8736; E-mail: psaithal@gmail.com Subject Area: Research Methodology. Type of the Paper: Conceptual Paper. Type of Review: Peer Reviewed as per |C|O|P|E| guidance. Indexed In: OpenAIRE. DOI: https://doi.org/10.5281/zenodo.7304622 Google Scholar Citation: IJAEML How to Cite this Paper: Ganesha, H. R., & Aithal, P. S., (2022). Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India. International Journal of Applied Engineering and Management Letters (IJAEML), 6(2), 288-306. DOI: https://doi.org/10.5281/zenodo.7304622 International Journal of Applied Engineering and Management Letters (IJAEML) A Refereed International Journal of Srinivas University, India. Crossref DOI: https://doi.org/10.47992/IJAEML.2581.7000.0159 Received on: 20/10/2022 Published on: 05/11/2022 © With Authors. This work is licensed under a Creative Commons Attribution-Non-Commercial 4.0 International License subject to proper citation to the publication source of the work. Disclaimer: The scholarly papers as reviewed and published by the Srinivas Publications (S.P.), India are the views and opinions of their respective authors and are not the views or opinions of the S.P. The S.P. disclaims of any harm or loss caused due to the published content to any party. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 288 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India 1 2 H. R. Ganesha & Aithal P. S. Research Professor, Institute of Management & Commerce, Srinivas University, Mangaluru, India, and Board Member, Gramss Retail Trading Private Limited, Bengaluru, India, OrcidID: 0000-0002-5878-8844; E-mail: hrganesha@yahoo.co.in Professor & Vice-Chancellor, Srinivas University, Mangaluru, India, OrcidID: 0000-0002-4691-8736; E-mail: psaithal@gmail.com ABSTRACT Purpose: The purpose of this article is to explain standard formulas available for deriving sample size, the essence of every component of formulas, and available techniques for selecting samples from the research population in turn, guiding the Ph.D. scholars to finalize appropriate sample size and sampling technique. Design/Methodology/Approach: Postmodernism philosophical paradigm; Inductive research approach; Observation data collection method; Longitudinal data collection time frame; Qualitative data analysis. Findings/Result: As long as the Ph.D. scholars can understand an appropriate sample size and available sampling techniques and make mindful choices of sample size and sampling technique across various stages/phases of the research to answer their research questions they will be able to determine (on their own) all the other choices in succeeding steps of doctoral-level research such as i) data collection instrument and iii) data analysis techniques. Originality/Value: There is a vast literature about how to derive the sample size and how to select samples from the research population. However, only a few have explained them together comprehensively which is conceivable to Ph.D. scholars. In this article, we have attempted to explain every component of sample size formulas and capture most of the sampling techniques briefly that would enable Ph.D. scholars in India to glance through and make a scholarly choice of appropriate sample size and sample selection techniques. Paper Type: Conceptual. Keywords: Research Methodology; Research Design; Research Process; PhD; Ph.D.; Coursework; Doctoral Research; Sample Size; Research Population; Population Size; Sample Proportion; Margin of Error; Confidence Interval; Confidence Level; Non-random Sampling; Random Sampling; Non-probability Sampling; Probability Sampling; Judgemental Sampling; Purposive Sampling; Quota Sampling; Dimensional Sampling; Convenience Sampling; Snowball Sampling; Simple Random Sampling; Systematic Sampling; Stratified Sampling; Cluster/Area Sampling; Multistage Sampling; Postmodernism 1. BACKGROUND : Various research studies have identified factors affecting the Ph.D. success rate across the world. “To name a few a) scholar-supervisor/guide relationship; b) mentorship; c) dissertation process; d) role of the department; e) role of peer qualities; f) transformational learning experience provided; g) level of curiosity and interest in reviewing the existing literature; h) planning and time management skills; i) level of creative thinking and writing skills; j) amount of freedom in the research project; k) level of a supportive environment for Ph.D. scholars’ well-being; l) higher-education practices; m) supervisors’ research capabilities and gender; n) expectations set by the research environment; o) Ph.D. scholars’ expectations; p) support network; q) level of Ph.D. scholars’ socialization with the research community; r) Ph.D. scholars’ navigation system; s) different terminologies for various components H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 289 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION of doctoral-level research are given by different disciplines creating undue confusion in scholars’ minds; t) data collection methods which just play the role of data collection and it is just one of the steps of the doctoral-level research process being portrayed as the research methodology/design; u) scholars’ inability to identify their genuine interest in a fact/phenomenon/reality/truth/dependent variable, intensive review of existing literature, locating an important research gap, and finally formulating a research question; v) a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., choosing an appropriate research philosophical paradigm that lays stepping stones toward answering the research question in a scientific and scholarly way; w) a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., choosing an appropriate research approach/reasoning that paves path for decision concerning data collection and analysis; x) a humongous confusion among Ph.D. scholars in India about the difference between research methodology/design and research data collection methods; y) lower level of clarity and the beginning of the Ph.D. journey without a clear understanding of the essence of research data collection time frames” [1-53]. Furthermore, in reality, a majority of stakeholders in the research education system have a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., deriving the right sample size and selecting samples that are true representatives of the research population. A majority of them guide the Ph.D. scholars to begin the journey without educating the scholars about the most important aspect/objective/purpose of deriving the right sample size and choosing an appropriate sampling technique to select samples from the research population. They also mandate that scholars use certain standard sample sizes and sampling techniques that are commonly used in a discipline or the one with which they are comfortable. In addition, there is a humongous confusion about the difference between sample size and sample proportion, and the convenience sampling technique being misinterpreted as the one that is most convenient for the scholars to select samples from their research population. This lower level of clarity and the beginning of the Ph.D. journey without a clear understanding of the essence of deriving the right sample size and choosing the appropriate sampling technique used in selecting samples from the research population is making it difficult for Ph.D. scholars to complete the journey successfully and most importantly if some scholars complete their Ph.D. journey successfully, their awareness about the reasons for their decision about the sample size and sampling technique is very low. We believe that if the scholars can begin their Ph.D. journey by allocating a higher level of focus and time toward understanding the right sample size and sample selection techniques their journey will be with a very lower level of complications and with a higher level of awareness about their choice of sample size and sampling technique. But this reality is knowingly or unknowingly, intentionally, or unintentionally suppressed by a majority of stakeholders in the research education system in India. In other words, this suppressed reality has resulted in creating humungous confusion among Ph.D. scholars in India about the key components of the sample size derivation formula viz., sample proportion, the margin of error/confidence interval, and confidence level, and the purpose/objective/deliverables of each sampling techniques. One thing Ph.D. scholars must always remind themselves of throughout their Ph.D. journey is the fact that they will be awarded a Ph.D. degree for doing doctoral-level research. Doing doctoral-level research and generating research outputs such as research articles and a thesis determines the probability of success in getting a Ph.D. degree. The first step of the doctoral-level research process is identifying research gaps and formulating a research question, the second one is choosing an appropriate research philosophical paradigm, the third step is choosing an appropriate research approach/reasoning, the fourth step is choosing the appropriate research data collection method choice, the fifth step is choosing an appropriate data collection time frame, and the sixth and seventh step is to derive the sample size and choosing samples from the research population respectively [46-53]. It is thus inevitable and imperative that Ph.D. scholars understand statistically derive the sample size and choose one of the sampling techniques to select samples from the research population. The doctoral- level research which is the single most important requirement of the Ph.D. program is cognitively demanding and intends to create researchers who can create new knowledge or interpret existing knowledge about reality by using different perspectives, paradigms, and reasoning. Knowledge sharing requires autonomy, good quality time, a stress-free brain for deep thinking, and the freedom to look for more meaningful findings. This is the single most important reason for making doctoral- H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 290 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION level research flexible wherein the scientific and scholarly world gives autonomy to Ph.D. scholars to formulate their question and answer it within 3-6 years using an appropriate research approach/reasoning. Nevertheless, only 50% of scholars admitted to Ph.D. in India completed, and that too in ten years whether or not they are aware of the importance of reasoning in doctoral-level research [46]. Appropriate sample size and selection of samples from the research population depends upon i) the type of the research question (descriptive; relational; causal) [49]; ii) the research philosophical paradigm (positivism; interpretivism; critical realism; postmodernism; pragmatism) [50]; iii) the research approach/reasoning (deductive; inductive; abductive) [51]; iv) time available for scholars to collect data [46]; v) data collection method and method choice [52]; vi) resources that are available for scholars to collect data [46]; vii) data collection time frame choice [53]. Deriving sample size and choosing an appropriate sampling technique for choosing samples from the research population is one of the most important decisions scholars need to make during their Ph.D. journey. We strongly recommend scholars know their competence, research environment, and support system before finalizing the sample size and sampling technique. Do note that the sample size and sampling technique tells us ‘From How Many’ and ‘From Whom’ to collect research data [48]. 2. OBJECTIVE : There is humongous confusion among Ph.D. scholars in India about the difference between two standard formulas for deriving sample size, every component of these two formulas, and various available techniques to select samples from their research population. Furthermore, deciding the right sample size and selecting samples that are representative of the research population is one of the most important choices scholars are required to make during the doctoral-level research process. Owing to such confusion the key objective of this article is to explain standard formulas available for deriving sample size, the essence of every component of formulas, and available techniques for selecting samples from the research population in turn, guiding them to finalize appropriate sample size and sampling technique. 3. DERIVING SAMPLE SIZE : Deriving sample size is required to finalize ‘From How Many’ respondents/participants/subjects/ cases/groups/units of analysis/samples we require to collect the research data [48]. Deriving the sample size step is one of the easiest steps in the doctoral-level research process as the Ph.D. scholars will get the help of a ‘Facilitator’ famously known as Statistical Techniques [47]. Scholars might think about whether they are good at Mathematics/Statistics. However, they need to be cognizant of the fact that, Statistics is not Mathematics! and does not require talent or previous association with subjects concerning Mathematics/Statistics. It just requires hard work, and more than the hard work requires scholars to focus on the purpose of deriving sample size and the role of statistical techniques. Scholars need not be an expert in Mathematics or Statistics and most importantly they are not required to memorize the formulas. They just need to know why they have taken the help of a particular formula. Statistics also uses numbers, but numbers are not the primary focus. It is a form of inductive reasoning that uses mathematics as one of its tools to discover new knowledge. It is a thinking tool and science of learning from data [46]. We know that scholars are interested in studying a population/universe/group of their research question, but unfortunately, it is impossible to collect research data from the entire population of the research question. For example, if the key objective of the research is to understand the relationship between ‘online teaching mode’ (Independent Variable) and the ‘learning levels’ (Dependent Variable) of students studying for a master’s degree in Psychology in India (Units of Analysis) which means the population of the research question is ‘ALL’ the students enrolled in a Master of Psychology program in India. Now one can imagine how difficult it is for scholars to reach out to every student of Psychology (Master’s) admitted across 28 States and 8 Union Territories of India. Hence, experts in the field of Statistics have discovered a ‘Sample’ which is a smaller and more manageable version of a larger population of the research question. It is a subset containing characteristics of a larger population and represents the population as illustrated in figure A (All the white patches in the population are the samples selected from the population). The use of samples allows scholars to conduct research with more manageable data and on time. Statistical techniques help scholars scientifically arrive at an ideal sample size for their research and the only way to avoid Statistics during H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 291 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION this step is to collect data from the entire population (known as Census). However, Statistical techniques can only help scholars derive the sample size through standard formulas, but they need to know and decide on a few components of these formulas as discussed below [55-71]. Fig. A: Population and Sample [54] 3.1. Decision 1 - Population Size : The size of the overall population scholars wish to examine should be taken into consideration when deciding on the sample size. A population is an entire group that scholars want to conclude about, and it is from the population that a sample is selected, using various sampling techniques. The research population size may be known such as the total number of employees in a particular company or the total number of full-time Ph.D. scholars in Uttar Pradesh, and in some cases, the population size is unknown such as the number of working women in Ahmedabad. But there is a need for a close estimate, especially when dealing with relatively small or easy-to-measure groups of people. 3.2. Decision 2 - Sample Proportion : Sample Proportion is required to determine the appropriate sample size for estimating the proportion of the research population that possesses a particular property/character/common element (criteria). Defining the Sample of the research is an important task, and scholars are the only persons who have a better understanding of the criteria of the sample. Sample Proportion can often be determined by using the results from a previous study (similar), or by running a small pilot study. If scholars are unsure, scholars can use 50% as the Sample Proportion (safer side), which is conservative and gives the largest sample size. However, be aware that scholars can only copy the Sample Proportion of a similar previous study and they are not allowed to copy the sample size of any previous studies (read and understand this sentence once again). 50% sample proportions meant that about 50% of the research population is expected to ‘meet the criteria’ of the definition of samples of the research study. For example, if we decide to choose 50% as the sample proportion to calculate the sample size that means we are sure that 50% of the research population is owing a car if we are studying the experience of car owners or 50% of the research population can speak more than one language if we are trying to understand the communication skills of people who speak more than one language or 50% of the research population was isolated during Covid-19 lockdown if we are trying to understand the experience of home isolation. Be aware that this decision is purely left to scholars’ discretion and no one can question this decision as long as scholars can justify/defend their decision on the Sample Proportion. 3.3. Decision 3 - Margin Of Error (MOE) and Confidence Interval (CI) : The MOE/CI tells scholars how confident they can be that the results from a study reflect what they would expect to find if it were possible to survey the entire research population being studied. It is H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 292 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION usually a plus or minus (±) figure. MOE is represented in ± % points, whereas CI is represented in ± absolute value. Let us assume that a scholar has decided on ±5% points MOE while calculating the sample size which means, this scholar is fine in allowing only ±5% points mistake in his/her claim/finding of the research. Usually ±5% points MOE is set by the scholars for sample size calculation. For example, if we have found that about 90% of 50 B.Com students (Samples) we selected out of a total of 350 B.Com students at Srinivas University (Research Population) have agreed that ‘online teaching mode’ (Independent Variable) has a positive impact on the ‘learning levels’ (Dependent Variable) and hence we have concluded our research as ‘Online Teaching Mode has Positive Impact on Learning Levels of B.Com Students at Srinivas University’. Now the meaning of ±5% points MOE is that if another Researcher selects another 50 B.Com students at Srinivas University who were not part of our previous samples, then we are confident that between 90% (-5%: 45 students) and 95% (+5%: 48 students) would also agree that the online teaching mode has a positive impact on their learning levels as during our research 90% of the students agreed. 3.4. Decision 4 - Confidence Level (CL) : The CL is the percentage of probability or certainty that the MOE/CI would contain the true population parameter when we draw a random sample many times. It is expressed as a percentage and represents how often the percentage of the research population who would pick an answer lies within the MOE/CI. For example, a 99% confidence level means that should we repeat an experiment or survey over and over again, 99 percent of the time, our results will match the results we get from a research population. In other words, there is only a 1% chance that the results from the research population will be less or more than the MOE/CI. Usually, a Confidence Level of 95% is acceptable if scholars belong to disciplines other than Basic Sciences, Medical Sciences, Clinical Studies, Engineering, Technology, or Health sciences else it needs to be kept at 99%. Do note that the higher the Confidence Level set during the research higher the reliability and validity of our research claim/finding/conclusion. Formula 1 - Population Size Known : Sample Size; Where, Where, (1)  ‘N’ is Population Size  ‘p’ is Sample Proportion  ‘MOE’ is the Margin of Error  ‘Z’ is a Critical value. It is a mathematical constant defined by the Confidence Level chosen. Standard values for ‘Z’ are; for 85% CL 1.440; for 90% CL 1.645; for 95% CL 1.960; for 99% CL 2.576. Formula 2 - Population Size Unknown : (2) Sample Size  ‘p’ is Sample Proportion  ‘MOE’ is the Margin of Error  ‘Z’ is a Critical value. It is a mathematical constant defined by the Confidence Level chosen. Standard values for ‘Z’ are; for 85% CL 1.440; for 90% CL 1.645; for 95% CL 1.960; for 99% CL 2.576. Once the scholars have made all the above four decisions their work is done. Now they need to enter the numbers of all these decisions into the standard sample size formula to derive the sample size for the research data collection. There are two formulas for calculating the sample size [55] such as i) formula 1 when we know the exact size of the research population (1), and ii) formula 2 when we do not know the research population size (2). Once scholars have derived the sample size, they need to remember to set their sample size as 20% higher than what they got from the formula. The additional sample size is always necessary as there are chances that the samples chosen by the scholars might not respond to all their questions/treatments/interventions or they might answer a few questions without much deliberation, or they might not turn up when scholars start the data collection process. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 293 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION 4. CHOOSING SAMPLES FROM THE RESEARCH POPULATION : Once the scholars have finalized ‘From How Many’ to collect the research data, now in the next step of the doctoral-level research process they need to finalize ‘From Whom’ (respondents/ participants/subjects/cases/groups/units of analysis/samples) to collect the research data that are representing the population of their research question. Selecting appropriate samples from the research population is also one of the easiest steps as the scholars’ task is to only choose one of the nine techniques. Choosing the right samples from the research population is also known as the Sampling/Sampling Technique. Though the procedure of selecting a sample differs according to the type of sample selected, certain fundamental rules remain the same that are listed below.  The research group or universe or population must be defined precisely.  Before choosing the sample, the unit of analysis/sample should be defined. A clear description of the sample based on the scholars’ research questions is mandatory. For example, Gender (Male/Female); Age; Marital Status (Married/Unmarried/Divorced); Occupation (Working/Non-working); Disease (New/Chronic/Hereditary/Non-hereditary); Customer (New/Existing).  The appropriate source list which contains the names of the units of a research group or universe or population from which the sample is to be selected should be prepared beforehand in case it does not already exist.  The size of the sample to be selected should be pre-determined as discussed in the previous step. Fig. 1: Population frame There are two main categories of Sampling Techniques viz, Non-random/Non-probability Sampling and Random/Probability Sampling [72-103]. Assume that our research population is a Research Methodology Classroom with 288 Ph.D. scholars in it. The Sample Size derived using formula 1 (population size is known) is 58 (keeping p=0.95; MOE=0.05; CL=95%). Let us now understand different types of Sampling Techniques with examples using this research population. Firstly, as we know the research population size we can create a frame of the research population as shown in figure 1 by giving each of the Ph.D. scholars a number or code. 4.1. Judgemental/Purposive Sampling : It is a Non-random/Non-probability Sampling Technique. In this type, we purely consider the purpose of our research, along with the understanding of the target population. For instance, when we want to understand the thought process of scholars interested in enrolling in a ‘Post-doc’ program after their Ph.D., our Sample selection criteria will be, asking a simple question to all the 288 scholars i.e., “are you interested in doing a Post-doc program after Ph.D.?” And those who respond with a “no” are excluded from the sampling. We will choose the scholars who said ‘yes’ to our question as our 58 H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 294 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Samples. This technique is illustrated in figure 2 with Samples being selected and highlighted with a grey-colored filling. Fig. 2: Sampling frame for Judgemental/Purposive sampling 4.2. Quota/Dimensional Sampling : It is a Non-random/Non-probability Sampling Technique. Quota sampling is where we take a very tailored sample that is in proportion to some characteristic or trait of the research population. For example, if our research population consists of 50% female and 50% male, our sample should reflect those percentages. If the Research Methodology classroom has 50% Male and 50% Female scholars then firstly we will divide our research population into two parts (Male and Female) as highlighted with red frames in figure 3 and we will select 29 males and 29 females from each part of our sampling frame as illustrated in figure 3. with samples being selected highlighted with a grey-colored filling. 4.3. Convenience Sampling : It is a Non-random/Non-probability Sampling Technique. In situations, wherein we have nearly no authority to select the sample elements, it is purely done based on proximity. Unfortunately, this technique is misunderstood by many Ph.D. scholars in India as choosing samples that are convenient for them. The convenience Sampling Technique must be chosen only in case the distance between the scholar and the sample is very long and it is impossible to collect research data from them. For example, if we are interested in understanding the impact of the Research Methodology class using a face-to-face interview (Survey method) that requires us to meet the sample in person then we might want to choose 58 scholars who are staying very close to (proximity) our place of stay/research/study. In this case, our sampling frame might look like the one illustrated in figure 4. with samples being selected highlighted with a grey-colored filling. Fig. 3: Sampling frame for Quota/Dimensional sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 295 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 4: Sampling frame for Convenience sampling 4.4. Snowball Sampling : It is a Non-random/Non-probability Sampling Technique. The process of Snowball sampling is much like asking our subjects/respondents/participants/groups/units of analysis/samples to nominate another one with the same characteristic/trait as our next Sample. We will then observe the nominated samples and continue in the same way until obtaining a sufficient number of samples. For example, if we are interested in understanding the key objective of scholars in the Research Methodology classroom to enroll in the Ph.D. program then we know that a majority of the scholars will not be giving an honest answer. In this case, Snowball Sampling Technique is the appropriate technique to select samples. Here we will ask the scholar who has honestly answered our question and use this scholar to select other scholars based on the nomination process. In this case, our Sampling frame might look like the one illustrated in figure 5, with samples being selected highlighted with a grey-colored filling. Fig. 5: Sampling frame for Snowball sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 296 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 6: Sampling frame for Simple Random sampling 4.5. Simple Random Sampling : It is a Random/Probability Sampling Technique. A probability sampling in which we simply select samples from the research population randomly. This technique ensures that each sample of the research population gets an equal chance of being selected. For example, if we are interested in understanding the impact of the Research Methodology class using an online questionnaire (Survey method) then we might choose 58 scholars randomly. In this case, our Sampling frame might look like the one illustrated in figure 6. with samples being selected highlighted with a grey-colored filling. 4.6. Systematic Sampling : It is a Random/Probability Sampling Technique. A type of probability sampling method in which samples from a larger research population are selected according to a random starting point but with a fixed, periodic interval. This interval is also called a Sampling Interval which is calculated by dividing the overall research population size by the desired sample size (in this example 288÷5 = 5). We will select a scholar after a sampling interval of 5. In this case, our Sampling frame might look like the one illustrated in figure 7. with samples being selected highlighted with a grey-colored filling. Fig. 7: Sampling frame for Systematic sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 297 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 8: Sampling frame for Stratified sampling 4.7. Stratified Sampling : It is a Random/Probability Sampling Technique. Involves the division of a research population into smaller sub-groups known as Strata. In stratified random sampling or stratification, the strata are formed based on samples' shared attributes or characteristics such as the ‘discipline’ of Ph.D. in the Research Methodology classroom example. All the research population elements are categorized into mutually exclusive and exhaustive groups (5 strata, 11 from each = 58 in this example). For example, if we want to ensure scholars from all the disciplines in the Research Methodology classroom are given an equal chance of being selected, we will first create Strata of each discipline (Allied Health Sciences, Education, Engineering, Social Sciences, and Management) and then randomly select scholars from each Strata. In this case, our Sampling frame might look like the one illustrated in figure 8. with samples being selected highlighted with a grey-colored filling. 4.8. Cluster/Area Sampling : It is a Random/Probability Sampling Technique. Involves the division of a population into smaller sub-groups known as Cluster. The clusters are formed based on samples’ shared attributes or characteristics such as scholars under a Research Supervisor/Guide in addition to a discipline (Allied Health Sciences, Education, Engineering, Social Sciences, and Management). Here we will divide the research population by discipline and then within each discipline, we will choose ‘all’ scholars under a specific Research Supervisor/Guide. Do note that the randomization is only in choosing the Research Supervisor/Guide and not the scholars under a Supervisor/Guide. In this case, our Sampling frame might look like the one illustrated in figure 9, with samples being selected highlighted with a grey- colored filling. Fig. 9: sampling frame for Cluster/Area sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 298 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 10: sampling frame for Multi-stage sampling 4.9. Multistage Sampling : It is a Random/Probability Sampling Technique. The research population is partitioned into groups, like cluster sampling, but in this design new samples are taken from each cluster sampling. Two-stage sampling is used when the sizes of the clusters are large, making it difficult or expensive to observe all the units inside them. For example, if we are interested in knowing the impact of the Research Methodology course on the Ph.D. scholar and decide to choose Multistage Sampling. We will firstly divide (Stage 1) the entire population by their discipline, secondly, we will divide the scholars in each discipline by their gender (Stage 2), and lastly we will divide the scholars in each gender by their Ph.D. type (Full-time and Part-time; Stage 3). Only after the three stages of division, we will then randomly choose the samples from each stratum. In this case, our Sampling frame might look like the one illustrated in figure 10, with samples being selected highlighted with a grey-colored filling. 4.10. Choosing an Appropriate Sampling Technique : After understanding all the available Sampling Techniques, scholars might be thinking that all of them sound good but how do choose one of them? We recommend scholars consider the following while they choose a Sampling Technique to select samples from their research population.  The level of homogeneity in the population.  Existing knowledge about the variables and units of analysis of the research question.  The level of accuracy and precision required to claim the research findings.  Cost and time required for Sampling Technique chosen. We suggest scholars avoid Non-random/Non-probability sampling techniques unless it is the last resort. Use them during the early/exploratory stages/phases of the research. Do note that the higher the difficulty level of the sampling technique lesser the error in the research findings/claims. And irrespective of the sampling technique scholars decide to choose, always try, and select at least 20% more samples than the derived Sample Size. There are chances that the samples chosen by the scholars might not respond to all their questions/treatments/interventions or they might answer a few questions without much deliberation, or they might not turn up when scholars start the data collection process. 5. CONCLUSION : Among the two main Sampling Techniques available Random/Probability sampling is the most preferred among scholars belonging to the Basic/Natural Science, Engineering, and Technology disciplines, and Non-random/Non-probability sampling is the most preferred for scholars belonging to other disciplines in India. We understand the Ph.D. program is time-bound and hence using one of the Non-random/Non-probability sampling techniques during the Ph.D. program is acceptable. But knowingly or unknowingly, intentionally, or intentionally a significant majority of researchers in India use Non-random/Non-probability sampling techniques even after the completion of the Ph.D. program. The fear among Indian researchers is that Random/Probability sampling techniques require H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 299 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION a lot of time investment, they are complicated, and most importantly the research output in the form of research article publications will slow down drastically. The mere pressure on Ph.D. scholars and Ph.D. holders in India to publish a certain number of research articles which is connected to their performance measurement is also one of the key reasons for this. Ph.D. scholars and Ph.D. holders must be aware that a scholarly description, explanation, or claim about a reality/fact/truth/effect/dependent variable and a piece of complete knowledge about reality is complete only when they are derived from collecting and evaluating data using multiple sampling techniques i.e., ensuring an equal opportunity was given to each sample of the research population to get selected. It is the responsibility of every stakeholder in the research environment and system to ensure that the scholars are made aware of every step involved in carrying out doctoral-level research in addition to the purpose, objective, and key deliverables of various available sampling techniques for them to choose an appropriate one to achieve their key research objective during the Ph.D. journey. Designing robust coursework that is intended to create awareness about the essence of sample size and sampling techniques is an appropriate way of fulfilling this responsibility. As long as the Ph.D. scholars can understand an appropriate sample size and available sampling techniques and make mindful choices of sample size and sampling technique across various stages/phases of the research to answer their research question they will be able to determine (on their own) all the other choices in succeeding steps of doctoral-level research such as i) data collection instrument and iii) data analysis techniques. REFERENCES : [1] Titus, S. L., & Ballou, J. M. (2013). Faculty members’ perceptions of advising versus mentoring: Does the name matter?. Science and Engineering ethics, 19(3), 1267-1281. Google Scholar [2] Ali, A., & Kohun, F. (2006). Dealing with isolation feelings in IS doctoral programs. International Journal of Doctoral Studies, 1(1), 21-33. Google Scholar [3] Ali, A., Kohun, F., & Levy, Y. (2007). Dealing with Social Isolation to Minimize Doctoral Attrition- A Four Stage Framework. International Journal of Doctoral Studies, 2(1), 33-49. Google Scholar [4] Spaulding, L. S., & Rockinson-Szapkiw, A. (2012). Hearing their voices: Factors doctoral candidates attribute to their persistence. International Journal of Doctoral Studies, 7, 199. Google Scholar [5] Golde, C. M., & Dore, T. M. (2001). At cross purposes: What the experiences of today's doctoral students reveal about doctoral education, ERIC Processing and Reference Facility, 1-62. Google Scholar [6] Golde, C. M. (2005). The role of the department and discipline in doctoral student attrition: Lessons from four departments. The Journal of Higher Education, 76(6), 669-700. Google Scholar [7] Golde, C. M., & Walker, G. E. (Eds.). (2006). Envisioning the future of doctoral education: Preparing stewards of the discipline-Carnegie essays on the doctorate (Vol. 3). John Wiley & Sons. Google Scholar [8] Gardner, S. K. (2009). Student and faculty attributions of attrition in high and low-completing doctoral programs in the United States. Higher education, 58(1), 97-112. Google Scholar [9] Gardner, S. K. (2010). Faculty perspectives on doctoral student socialization in five disciplines. International Journal of Doctoral Studies, 5(1), 39-51. Google Scholar [10] Solmon, M. A. (2009). How do doctoral candidates learn to be researchers? Developing research training programs in kinesiology departments. Quest, 61(1), 74-83. Google Scholar [11] Nogueira-Martins, L. A., Fagnani Neto, R., Macedo, P. C. M., Citero, V. D. A., & Mari, J. D. J. (2004). The mental health of graduate students at the Federal University of São Paulo: a preliminary report. Brazilian Journal of Medical and Biological Research, 37, 1519-1524. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 300 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [12] Knox, S., Schlosser, L. Z., Pruitt, N. T., & Hill, C. E. (2006). A qualitative examination of graduate advising relationships: The advisor perspective. The Counseling Psychologist, 34(4), 489-518. Google Scholar [13] Grady, R. K., La Touche, R., Oslawski-Lopez, J., Powers, A., & Simacek, K. (2014). Betwixt and between: The social position and stress experiences of graduate students. Teaching Sociology, 42(1), 5-16. Google Scholar [14] Russell, J., Gaudreault, K. L., & Richards, K. A. R. (2016). Doctoral student socialization: Educating stewards of the physical education profession. Quest, 68(4), 439-456. Google Scholar [15] Russell, J. A. (2015). Rolling with the punches: Examining the socialization experiences of kinesiology doctoral students. Research quarterly for exercise and sport, 86(2), 140-151. Google Scholar [16] Harding-DeKam, J. L., Hamilton, B., & Loyd, S. (2012). The hidden curriculum of doctoral advising. NACADA Journal, 32(2), 5-16. Google Scholar [17] Mansson, D. H., & Myers, S. A. (2012). Using mentoring enactment theory to explore the doctoral student–advisor mentoring relationship. Communication Education, 61(4), 309-334. Google Scholar [18] Robinson, E. M., & Tagher, C. G. (2017). The companion dissertation: Enriching the doctoral experience. Journal of Nursing Education, 56(9), 564-566. Google Scholar [19] Haynes, K. N. (2008). Reasons for doctoral attrition. Health, 8, 17-4. Google Scholar [20] Mazerolle, S. M., Bowman, T. G., & Klossner, J. C. (2015). An analysis of doctoral students' perceptions of mentorship during their doctoral studies. Athletic Training Education Journal, 10(3), 227-235. Google Scholar [21] Holsinger Jr, J. W. (2008). Situational leadership applied to the dissertation process. Anatomical Sciences Education, 1(5), 194-198. Google Scholar [22] McNamara, J. F., Lara-Alecio, R., Hoyle, J., & Irby, B. J. (2010). Doctoral program issues: Commentary on companion dissertations. A Doctoral Issues Presentation at the National Council of Professors of Educational Administration Lexington, KY, August 2, 2006. Google Scholar [23] Carter-Veale, W. Y., Tull, R. G., Rutledge, J. C., & Joseph, L. N. (2016). The dissertation house model: Doctoral student experiences coping and writing in a shared knowledge community. CBE—Life Sciences Education, 15(3), ar34. Google Scholar [24] Devos, C., Boudrenghien, G., Van der Linden, N., Azzi, A., Frenay, M., Galand, B., & Klein, O. (2017). Doctoral students’ experiences leading to completion or attrition: A matter of sense, progress and distress. European journal of psychology of education, 32(1), 61-77. Google Scholar [25] Beatty, S. E. (2001). The doctoral supervisor-student relationship: some American advice for success. The Marketing Review, 2(2), 205-217. Google Scholar [26] Carpenter, S., Makhadmeh, N., & Thornton, L. J. (2015). Mentorship on the doctoral level: An examination of communication faculty mentors’ traits and functions. Communication Education, 64(3), 366-384. Google Scholar [27] Most, D. E. (2008). Patterns of doctoral student degree completion: A longitudinal analysis. Journal of College Student Retention: Research, Theory & Practice, 10(2), 171-190. Google Scholar [28] Stock, W. A., Siegfried, J. J., & Finegan, T. A. (2011). Completion rates and time-to-degree in economics PhD programs (with comments by David Colander, N. Gregory Mankiw, Melissa P. McInerney, James M. Poterba). American Economic Review, 101(3), 176-88. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 301 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [29] Wamala, R., Ocaya, B., & Oonyu, J. C. (2012). Extended Candidature and Non-Completion of a Ph. D. at Makerere University, Uganda. Contemporary Issues in Education Research, 5(3), 175-184. Google Scholar [30] https://academy.pubs.asha.org/2011/12/higher-education-practices-that-promote-phd- completion/. Retrieved in October 2022. [31] Preston, J. P., Ogenchuk, M. J., & Nsiah, J. K. (2014). Peer mentorship and transformational learning: PhD student experiences. Canadian Journal of Higher Education, 44(1), 52-68. Google Scholar [32] Devine, K., & Hunter, K. H. (2017). PhD student emotional exhaustion: the role of supportive supervision and self-presentation behaviours. Innovations in Education and Teaching International, 54(4), 335-344. Google Scholar [33] Van Rooij, E., Fokkens-Bruinsma, M., & Jansen, E. (2021). Factors that influence PhD candidates’ success: the importance of PhD project characteristics. Studies in Continuing Education, 43(1), 48-67. Google Scholar [34] Chenevix-Trench, G. (2006). What makes a good PhD student?. Nature, 441(7090), 252-252. Google Scholar [35] Dericks, G., Thompson, E., Roberts, M., & Phua, F. (2019). Determinants of PhD student satisfaction: the roles of supervisor, department, and peer qualities. Assessment & evaluation in higher education volume 44(7), 1053-1068. Google Scholar [36] Corsini, A., Pezzoni, M., & Visentin, F. (2022). What makes a productive Ph. D. student?. Research Policy 51(10), 104561. Google Scholar [37] Lindvig, K. (2018). The implied PhD student of interdisciplinary research projects within monodisciplinary structures. Higher Education Research & Development, 37(6), 1171-1185. Google Scholar [38] Holbrook, A., Shaw, K., Scevak, J., Bourke, S., Cantwell, R., & Budd, J. (2014). PhD candidate expectations: Exploring mismatch with experience. International Journal of Doctoral Studies, 9, 329. Google Scholar [39] Björkman, B. (2015). PhD supervisor-PhD student interactions in an English-medium Higher Education (HE) setting: Expressing disagreement. European Journal of Applied Linguistics, 3(2), 205-229. Google Scholar [40] Dimitrova, R. (2016). Ingredients of good PhD supervision-evidence from a student survey at Stockholm university. Utbildning och Lärande/Education and Learning, 10(1), 40-52. Google Scholar [41] Sullivan-Bolyai, S., & L'Esperance, S. (2022). Reflections on virtual research conferences and PhD student socialization: The missing link of in-person human connectedness. Applied Nursing Research, 64 (April 2022), 151553. Google Scholar [42] Alpert, F., & Eyssell, T. H. (1995). Getting the most from your doctoral program: Advice for the Ph. D. student in finance. Journal of Financial Education, 12-20. Google Scholar [43] Groen, J. (2020). Perceptions of Transformation and Quality in Higher Education: A Case Study of PhD Student Experiences (Doctoral dissertation, University of Ottawa). Google Scholar [44] Helfer, F., & Drew, S. (2013). A small-scale investigation into Engineering PhD student satisfaction with supervision in an Australian university campus. In 24th Annual Conference of the Australasian Association for Engineering Education (pp. 1-9). Google Scholar [45] Cunningham-Williams, R. M., Wideman, E., & Fields, L. (2019). Ph. D. Student Development: A Conceptual Model for Research-Intensive Social Work PhD Programs. Journal of Evidence- Based Social Work, 16(3), 278-293. Google Scholar [46] Ganesha, H. R. & Aithal, P. S. (2022). Doing Ph.D. in India. A Step-by-Step Guide. First Edition. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 302 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Notion Press. India & Singapore. ISBN: 9798887832005. Google Scholar [47] Ganesha, H. R. & Aithal, P. S. (2022). The ‘8Fs’ Concept for Simplifying the Complications of Ph.D. Journey in India. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 6(2), 320-339. Google Scholar [48] Ganesha, H. R. & Aithal, P. S. (2022). The DDLR Model of Research Process for Designing Robust and Realizable Research Methodology During Ph.D. Program in India. International Journal of Management, Technology, and Social Sciences (IJMTS), 7(2), 400-417. Google Scholar [49] Ganesha, H. R. & Aithal, P. S. (2022). PHDRQ Model for Identifying Research Gaps and Formulating A Research Question During Ph.D. Program in India. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 6(2). 408-421. Google Scholar [50] Ganesha, H. R. & Aithal, P. S. (2022). Why is it Called Doctor of Philosophy and Why Choosing Appropriate Research Philosophical Paradigm is Indispensable During Ph.D. Program in India?. International Journal of Philosophy and Languages (IJPL), 1(1). 42-58. Google Scholar [51] Ganesha, H. R. & Aithal, P. S. (2022). Approaching Research in Different Ways. How to Choose an Appropriate Research Approach/Reasoning During Ph.D. Program in India?. International Journal of Philosophy and Languages (IJPL), 1(1). 59-74. Google Scholar [52] Ganesha, H. R. & Aithal, P. S. (2022). How to Choose an Appropriate Research Data Collection Method and Method Choice Among Various Research Data Collection Methods and Method Choices During Ph.D. Program in India?. International Journal of Management, Technology, and Social Sciences (IJMTS), 7(2), 455-479. Google Scholar [53] Ganesha, H. R. & Aithal, P. S. (2022). When to Collect Data? Choosing an Appropriate Time Frame for Data Collection During Ph.D. Program in India?. International Journal of Applied Engineering and Management Letters (IJAEML), 6(2), 271-287. Google Scholar [54] Lee, Nick, & Lings, Ian. (2008). Doing business research: a guide to theory and st practice. 1 Edition, Sage Publications Ltd., Page 293. Google Scholar [55] Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons. Google Scholar [56] Woolson, R. F., Bean, J. A., & Rojas, P. B. (1986). Sample size for case-control studies using Cochran's statistic. Biometrics, 927-932. Google Scholar [57] Kotrlik, J. W. K. J. W., & Higgins, C. C. H. C. C. (2001). Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Information technology, learning, and performance journal, 19(1), 43. Google Scholar [58] Ahmad, H., & Halim, H. (2017). Determining sample size for research activities. Selangor Business Review, 20-34. Google Scholar [59] Song, J. X., & Wassell, J. T. (2003). Sample size for K 2× 2 tables in equivalence studies using Cochran's statistic. Controlled clinical trials, 24(4), 378-389. Google Scholar [60] Nam, J. M. (1992). Sample size determination for case-control studies and the comparison of stratified and unstratified analyses. Biometrics, 389-395. Google Scholar [61] Milton, S. (1986). A sample size formula for multiple regression studies. Public Opinion Quarterly, 50(1), 112-118. Google Scholar [62] Mehta, C. R., Patel, N. R., & Senchaudhuri, P. (1998). Exact power and sample-size computations for the Cochran-Armitage trend test. Biometrics, 1615-1621. Google Scholar [63] Donner, A. (1992). Sample size requirements for stratified cluster randomization designs. Statistics in medicine, 11(6), 743-750. Google Scholar [64] Nam, J. M. (1987). A simple approximation for calculating sample sizes for detecting linear trend in proportions. Biometrics, 701-705. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 303 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [65] Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management, 2(11), 1-22. Google Scholar [66] Casagrande, J. T., Pike, M. C., & Smith, P. G. (1978). An improved approximate formula for calculating sample sizes for comparing two binomial distributions. Biometrics, 483-486. Google Scholar [67] Cochran, W. G. (1942). Sampling theory when the sampling-units are of unequal sizes. Journal of the American Statistical Association, 37(218), 199-212. Google Scholar [68] Snijders, T. A., & Bosker, R. J. (1993). Standard errors and sample sizes for two-level research. Journal of educational statistics, 18(3), 237-259. Google Scholar [69] Czaplewski, R. L., Crowe, D. M., & McDonald, L. L. (1983). Sample sizes and confidence intervals for wildlife population ratios. Wildlife Society Bulletin (1973-2006), 11(2), 121-128. Google Scholar [70] Kasiulevičius, V., Šapoka, V., & Filipavičiūtė, R. (2006). Sample size calculation in epidemiological studies. Gerontologija, 7(4), 225-231. Google Scholar [71] Wang, J., Ge, G., Fan, Y., Chen, L., Liu, S., Jin, Y., & Yu, J. (2006). The estimation of sample size in multi-stage sampling and its application in medical survey. Applied Mathematics and Computation, 178(2), 239-249. Google Scholar [72] Taherdoost, H. (2016). Sampling methods in research methodology; how to choose a sampling technique for research. How to choose a sampling technique for research (April 10, 2016). Available at SSRN. Google Scholar [73] Etikan, I., Alkassim, R., & Abubakar, S. (2016). Comparision of snowball sampling and sequential sampling technique. Biometrics and Biostatistics International Journal, 3(1), 55. Google Scholar [74] Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752. Google Scholar [75] Trost, J. E. (1986). Statistically nonrepresentative stratified sampling: A sampling technique for qualitative studies. Qualitative sociology, 9(1), 54-57. Google Scholar [76] Vehovar, V., Toepoel, V., & Steinmetz, S. (2016). Non-probability sampling (pp. 329-345). The Sage handbook of survey methods. Google Scholar [77] Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. Indian Journal of Medical Specialties, 4(2), 330-333. Google Scholar [78] Schillewaert, N., Langerak, F., & Duharnel, T. (1998). Non-probability sampling for WWW surveys: a comparison of methods. Market Research Society. Journal., 40(4), 1-13. Google Scholar [79] Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. Google Scholar [80] Barendregt, C., van der Poel, A., & van de Mheen, D. (2005). Tracing selection effects in three non-probability samples. European Addiction Research, 11(3), 124-131. Google Scholar [81] Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226. Google Scholar [82] Schreuder, H. T., Gregoire, T. G., & Weyer, J. P. (2001). For what applications can probability and non-probability sampling be used?. Environmental Monitoring and Assessment, 66(3), 281- 291. Google Scholar [83] DiSogra, C., Cobb, C., Chan, E., & Dennis, J. M. (2011, August). Calibrating non-probability internet samples with probability samples using early adopter characteristics. In Joint Statistical Meetings (JSM), Survey Research Methods (pp. 4501-4515). Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 304 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [84] Lehdonvirta, V., Oksanen, A., Räsänen, P., & Blank, G. (2021). Social media, web, and panel surveys: using non‐probability samples in social and policy research. Policy & internet, 13(1), 134-155. Google Scholar [85] Ayhan, H. Ö. (2011). Non-probability Sampling Survey Methods. International encyclopedia of statistical science, 14, 979-982. Google Scholar [86] Kandola, D., Banner, D., O’Keefe-McCarthy, S., & Jassal, D. (2014). Sampling Methods in Cardiovascular Nursing Research: An Overview. Canadian Journal of Cardiovascular Nursing, 24(3). Google Scholar [87] Buelens, B., Burger, J., & van den Brakel, J. A. (2018). Comparing inference methods for non‐ probability samples. International Statistical Review, 86(2), 322-343. Google Scholar [88] Buelens, B., Burger, J., & van den Brakel, J. (2015). Predictive inference for non-probability samples: a simulation study (Vol. 13, pp. 1-46). The Hague: Statistics Netherlands. Google Scholar [89] Li, P., Church, K., & Hastie, T. (2006). Conditional random sampling: A sketch-based sampling technique for sparse data. Advances in neural information processing systems, 19. Google Scholar [90] Clarkson, K. L., & Shor, P. W. (1989). Applications of random sampling in computational geometry, II. Discrete & Computational Geometry, 4(5), 387-421. Google Scholar [91] Olken, F., & Rotem, D. (1995). Random sampling from databases: a survey. Statistics and Computing, 5(1), 25-42. Google Scholar [92] Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management, 2(11), 1-22. Google Scholar [93] Niemierko, A., & Goitein, M. (1990). Random sampling for evaluating treatment plans. Medical physics, 17(5), 753-762. Google Scholar [94] Drott, M. C. (1969). Random sampling: a tool for library research. College & Research Libraries, 30(2), 119-125. Google Scholar [95] Ding, C. S., Haieh, C. T., Wu, Q., & Pedram, M. (1996, November). Stratified random sampling for power estimation. In Proceedings of International Conference on Computer Aided Design (pp. 576-582). IEEE. Google Scholar [96] Amir, B., & Ralph, P. (2018, May). There is no random sampling in software engineering research. In Proceedings of the 40th international conference on software engineering: companion proceeedings (pp. 344-345). Google Scholar [97] Endo, T., Yamamoto, A., & Watanabe, T. (2016). Bias factor method using random sampling technique. Journal of Nuclear Science and Technology, 53(10), 1494-1501. Google Scholar [98] Martino, L., Luengo, D., & Míguez, J. (2018). Independent random sampling methods (pp. 65- 113). Martino: Springer International Publishing. Google Scholar [99] Chaudhuri, S., Motwani, R., & Narasayya, V. (1998). Random sampling for histogram construction: How much is enough?. ACM SIGMOD Record, 27(2), 436-447. Google Scholar [100] Cooper, S. L. (1964). Random sampling by telephone—An improved method. Journal of Marketing Research, 1(4), 45-48. Google Scholar [101] Kim, J. K., Park, S., Chen, Y., & Wu, C. (2021). Combining non‐probability and probability survey samples through mass imputation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(3), 941-963. Google Scholar [102] Van Haute, E. (2021). SAMPLING TECHNIQUES. Research Methods in the Social Sciences: an AZ of Key Concepts, 247. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 305 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [103] Rahman, M. M., Tabash, M. I., Salamzadeh, A., Abduli, S., & Rahaman, M. S. (2022). Sampling techniques (probability) for quantitative social science researchers: a conceptual guidelines with examples. Seeu Review, 17(1), 42-51. Google Scholar ****** H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 306 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png SSRN Electronic Journal Unpaywall

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International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India 1 2 H. R. Ganesha & Aithal P. S. Research Professor, Institute of Management & Commerce, Srinivas University, Mangaluru, India, and Board Member, Gramss Retail Trading Private Limited, Bengaluru, India, OrcidID: 0000-0002-5878-8844; E-mail: hrganesha@yahoo.co.in Professor & Vice-Chancellor, Srinivas University, Mangaluru, India, OrcidID: 0000-0002-4691-8736; E-mail: psaithal@gmail.com Subject Area: Research Methodology. Type of the Paper: Conceptual Paper. Type of Review: Peer Reviewed as per |C|O|P|E| guidance. Indexed In: OpenAIRE. DOI: https://doi.org/10.5281/zenodo.7304622 Google Scholar Citation: IJAEML How to Cite this Paper: Ganesha, H. R., & Aithal, P. S., (2022). Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India. International Journal of Applied Engineering and Management Letters (IJAEML), 6(2), 288-306. DOI: https://doi.org/10.5281/zenodo.7304622 International Journal of Applied Engineering and Management Letters (IJAEML) A Refereed International Journal of Srinivas University, India. Crossref DOI: https://doi.org/10.47992/IJAEML.2581.7000.0159 Received on: 20/10/2022 Published on: 05/11/2022 © With Authors. This work is licensed under a Creative Commons Attribution-Non-Commercial 4.0 International License subject to proper citation to the publication source of the work. Disclaimer: The scholarly papers as reviewed and published by the Srinivas Publications (S.P.), India are the views and opinions of their respective authors and are not the views or opinions of the S.P. The S.P. disclaims of any harm or loss caused due to the published content to any party. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 288 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Deriving Right Sample Size and Choosing an Appropriate Sampling Technique to Select Samples from the Research Population During Ph.D. Program in India 1 2 H. R. Ganesha & Aithal P. S. Research Professor, Institute of Management & Commerce, Srinivas University, Mangaluru, India, and Board Member, Gramss Retail Trading Private Limited, Bengaluru, India, OrcidID: 0000-0002-5878-8844; E-mail: hrganesha@yahoo.co.in Professor & Vice-Chancellor, Srinivas University, Mangaluru, India, OrcidID: 0000-0002-4691-8736; E-mail: psaithal@gmail.com ABSTRACT Purpose: The purpose of this article is to explain standard formulas available for deriving sample size, the essence of every component of formulas, and available techniques for selecting samples from the research population in turn, guiding the Ph.D. scholars to finalize appropriate sample size and sampling technique. Design/Methodology/Approach: Postmodernism philosophical paradigm; Inductive research approach; Observation data collection method; Longitudinal data collection time frame; Qualitative data analysis. Findings/Result: As long as the Ph.D. scholars can understand an appropriate sample size and available sampling techniques and make mindful choices of sample size and sampling technique across various stages/phases of the research to answer their research questions they will be able to determine (on their own) all the other choices in succeeding steps of doctoral-level research such as i) data collection instrument and iii) data analysis techniques. Originality/Value: There is a vast literature about how to derive the sample size and how to select samples from the research population. However, only a few have explained them together comprehensively which is conceivable to Ph.D. scholars. In this article, we have attempted to explain every component of sample size formulas and capture most of the sampling techniques briefly that would enable Ph.D. scholars in India to glance through and make a scholarly choice of appropriate sample size and sample selection techniques. Paper Type: Conceptual. Keywords: Research Methodology; Research Design; Research Process; PhD; Ph.D.; Coursework; Doctoral Research; Sample Size; Research Population; Population Size; Sample Proportion; Margin of Error; Confidence Interval; Confidence Level; Non-random Sampling; Random Sampling; Non-probability Sampling; Probability Sampling; Judgemental Sampling; Purposive Sampling; Quota Sampling; Dimensional Sampling; Convenience Sampling; Snowball Sampling; Simple Random Sampling; Systematic Sampling; Stratified Sampling; Cluster/Area Sampling; Multistage Sampling; Postmodernism 1. BACKGROUND : Various research studies have identified factors affecting the Ph.D. success rate across the world. “To name a few a) scholar-supervisor/guide relationship; b) mentorship; c) dissertation process; d) role of the department; e) role of peer qualities; f) transformational learning experience provided; g) level of curiosity and interest in reviewing the existing literature; h) planning and time management skills; i) level of creative thinking and writing skills; j) amount of freedom in the research project; k) level of a supportive environment for Ph.D. scholars’ well-being; l) higher-education practices; m) supervisors’ research capabilities and gender; n) expectations set by the research environment; o) Ph.D. scholars’ expectations; p) support network; q) level of Ph.D. scholars’ socialization with the research community; r) Ph.D. scholars’ navigation system; s) different terminologies for various components H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 289 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION of doctoral-level research are given by different disciplines creating undue confusion in scholars’ minds; t) data collection methods which just play the role of data collection and it is just one of the steps of the doctoral-level research process being portrayed as the research methodology/design; u) scholars’ inability to identify their genuine interest in a fact/phenomenon/reality/truth/dependent variable, intensive review of existing literature, locating an important research gap, and finally formulating a research question; v) a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., choosing an appropriate research philosophical paradigm that lays stepping stones toward answering the research question in a scientific and scholarly way; w) a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., choosing an appropriate research approach/reasoning that paves path for decision concerning data collection and analysis; x) a humongous confusion among Ph.D. scholars in India about the difference between research methodology/design and research data collection methods; y) lower level of clarity and the beginning of the Ph.D. journey without a clear understanding of the essence of research data collection time frames” [1-53]. Furthermore, in reality, a majority of stakeholders in the research education system have a lower level of clarity about the most important and indispensable step of the doctoral-level research process i.e., deriving the right sample size and selecting samples that are true representatives of the research population. A majority of them guide the Ph.D. scholars to begin the journey without educating the scholars about the most important aspect/objective/purpose of deriving the right sample size and choosing an appropriate sampling technique to select samples from the research population. They also mandate that scholars use certain standard sample sizes and sampling techniques that are commonly used in a discipline or the one with which they are comfortable. In addition, there is a humongous confusion about the difference between sample size and sample proportion, and the convenience sampling technique being misinterpreted as the one that is most convenient for the scholars to select samples from their research population. This lower level of clarity and the beginning of the Ph.D. journey without a clear understanding of the essence of deriving the right sample size and choosing the appropriate sampling technique used in selecting samples from the research population is making it difficult for Ph.D. scholars to complete the journey successfully and most importantly if some scholars complete their Ph.D. journey successfully, their awareness about the reasons for their decision about the sample size and sampling technique is very low. We believe that if the scholars can begin their Ph.D. journey by allocating a higher level of focus and time toward understanding the right sample size and sample selection techniques their journey will be with a very lower level of complications and with a higher level of awareness about their choice of sample size and sampling technique. But this reality is knowingly or unknowingly, intentionally, or unintentionally suppressed by a majority of stakeholders in the research education system in India. In other words, this suppressed reality has resulted in creating humungous confusion among Ph.D. scholars in India about the key components of the sample size derivation formula viz., sample proportion, the margin of error/confidence interval, and confidence level, and the purpose/objective/deliverables of each sampling techniques. One thing Ph.D. scholars must always remind themselves of throughout their Ph.D. journey is the fact that they will be awarded a Ph.D. degree for doing doctoral-level research. Doing doctoral-level research and generating research outputs such as research articles and a thesis determines the probability of success in getting a Ph.D. degree. The first step of the doctoral-level research process is identifying research gaps and formulating a research question, the second one is choosing an appropriate research philosophical paradigm, the third step is choosing an appropriate research approach/reasoning, the fourth step is choosing the appropriate research data collection method choice, the fifth step is choosing an appropriate data collection time frame, and the sixth and seventh step is to derive the sample size and choosing samples from the research population respectively [46-53]. It is thus inevitable and imperative that Ph.D. scholars understand statistically derive the sample size and choose one of the sampling techniques to select samples from the research population. The doctoral- level research which is the single most important requirement of the Ph.D. program is cognitively demanding and intends to create researchers who can create new knowledge or interpret existing knowledge about reality by using different perspectives, paradigms, and reasoning. Knowledge sharing requires autonomy, good quality time, a stress-free brain for deep thinking, and the freedom to look for more meaningful findings. This is the single most important reason for making doctoral- H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 290 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION level research flexible wherein the scientific and scholarly world gives autonomy to Ph.D. scholars to formulate their question and answer it within 3-6 years using an appropriate research approach/reasoning. Nevertheless, only 50% of scholars admitted to Ph.D. in India completed, and that too in ten years whether or not they are aware of the importance of reasoning in doctoral-level research [46]. Appropriate sample size and selection of samples from the research population depends upon i) the type of the research question (descriptive; relational; causal) [49]; ii) the research philosophical paradigm (positivism; interpretivism; critical realism; postmodernism; pragmatism) [50]; iii) the research approach/reasoning (deductive; inductive; abductive) [51]; iv) time available for scholars to collect data [46]; v) data collection method and method choice [52]; vi) resources that are available for scholars to collect data [46]; vii) data collection time frame choice [53]. Deriving sample size and choosing an appropriate sampling technique for choosing samples from the research population is one of the most important decisions scholars need to make during their Ph.D. journey. We strongly recommend scholars know their competence, research environment, and support system before finalizing the sample size and sampling technique. Do note that the sample size and sampling technique tells us ‘From How Many’ and ‘From Whom’ to collect research data [48]. 2. OBJECTIVE : There is humongous confusion among Ph.D. scholars in India about the difference between two standard formulas for deriving sample size, every component of these two formulas, and various available techniques to select samples from their research population. Furthermore, deciding the right sample size and selecting samples that are representative of the research population is one of the most important choices scholars are required to make during the doctoral-level research process. Owing to such confusion the key objective of this article is to explain standard formulas available for deriving sample size, the essence of every component of formulas, and available techniques for selecting samples from the research population in turn, guiding them to finalize appropriate sample size and sampling technique. 3. DERIVING SAMPLE SIZE : Deriving sample size is required to finalize ‘From How Many’ respondents/participants/subjects/ cases/groups/units of analysis/samples we require to collect the research data [48]. Deriving the sample size step is one of the easiest steps in the doctoral-level research process as the Ph.D. scholars will get the help of a ‘Facilitator’ famously known as Statistical Techniques [47]. Scholars might think about whether they are good at Mathematics/Statistics. However, they need to be cognizant of the fact that, Statistics is not Mathematics! and does not require talent or previous association with subjects concerning Mathematics/Statistics. It just requires hard work, and more than the hard work requires scholars to focus on the purpose of deriving sample size and the role of statistical techniques. Scholars need not be an expert in Mathematics or Statistics and most importantly they are not required to memorize the formulas. They just need to know why they have taken the help of a particular formula. Statistics also uses numbers, but numbers are not the primary focus. It is a form of inductive reasoning that uses mathematics as one of its tools to discover new knowledge. It is a thinking tool and science of learning from data [46]. We know that scholars are interested in studying a population/universe/group of their research question, but unfortunately, it is impossible to collect research data from the entire population of the research question. For example, if the key objective of the research is to understand the relationship between ‘online teaching mode’ (Independent Variable) and the ‘learning levels’ (Dependent Variable) of students studying for a master’s degree in Psychology in India (Units of Analysis) which means the population of the research question is ‘ALL’ the students enrolled in a Master of Psychology program in India. Now one can imagine how difficult it is for scholars to reach out to every student of Psychology (Master’s) admitted across 28 States and 8 Union Territories of India. Hence, experts in the field of Statistics have discovered a ‘Sample’ which is a smaller and more manageable version of a larger population of the research question. It is a subset containing characteristics of a larger population and represents the population as illustrated in figure A (All the white patches in the population are the samples selected from the population). The use of samples allows scholars to conduct research with more manageable data and on time. Statistical techniques help scholars scientifically arrive at an ideal sample size for their research and the only way to avoid Statistics during H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 291 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION this step is to collect data from the entire population (known as Census). However, Statistical techniques can only help scholars derive the sample size through standard formulas, but they need to know and decide on a few components of these formulas as discussed below [55-71]. Fig. A: Population and Sample [54] 3.1. Decision 1 - Population Size : The size of the overall population scholars wish to examine should be taken into consideration when deciding on the sample size. A population is an entire group that scholars want to conclude about, and it is from the population that a sample is selected, using various sampling techniques. The research population size may be known such as the total number of employees in a particular company or the total number of full-time Ph.D. scholars in Uttar Pradesh, and in some cases, the population size is unknown such as the number of working women in Ahmedabad. But there is a need for a close estimate, especially when dealing with relatively small or easy-to-measure groups of people. 3.2. Decision 2 - Sample Proportion : Sample Proportion is required to determine the appropriate sample size for estimating the proportion of the research population that possesses a particular property/character/common element (criteria). Defining the Sample of the research is an important task, and scholars are the only persons who have a better understanding of the criteria of the sample. Sample Proportion can often be determined by using the results from a previous study (similar), or by running a small pilot study. If scholars are unsure, scholars can use 50% as the Sample Proportion (safer side), which is conservative and gives the largest sample size. However, be aware that scholars can only copy the Sample Proportion of a similar previous study and they are not allowed to copy the sample size of any previous studies (read and understand this sentence once again). 50% sample proportions meant that about 50% of the research population is expected to ‘meet the criteria’ of the definition of samples of the research study. For example, if we decide to choose 50% as the sample proportion to calculate the sample size that means we are sure that 50% of the research population is owing a car if we are studying the experience of car owners or 50% of the research population can speak more than one language if we are trying to understand the communication skills of people who speak more than one language or 50% of the research population was isolated during Covid-19 lockdown if we are trying to understand the experience of home isolation. Be aware that this decision is purely left to scholars’ discretion and no one can question this decision as long as scholars can justify/defend their decision on the Sample Proportion. 3.3. Decision 3 - Margin Of Error (MOE) and Confidence Interval (CI) : The MOE/CI tells scholars how confident they can be that the results from a study reflect what they would expect to find if it were possible to survey the entire research population being studied. It is H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 292 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION usually a plus or minus (±) figure. MOE is represented in ± % points, whereas CI is represented in ± absolute value. Let us assume that a scholar has decided on ±5% points MOE while calculating the sample size which means, this scholar is fine in allowing only ±5% points mistake in his/her claim/finding of the research. Usually ±5% points MOE is set by the scholars for sample size calculation. For example, if we have found that about 90% of 50 B.Com students (Samples) we selected out of a total of 350 B.Com students at Srinivas University (Research Population) have agreed that ‘online teaching mode’ (Independent Variable) has a positive impact on the ‘learning levels’ (Dependent Variable) and hence we have concluded our research as ‘Online Teaching Mode has Positive Impact on Learning Levels of B.Com Students at Srinivas University’. Now the meaning of ±5% points MOE is that if another Researcher selects another 50 B.Com students at Srinivas University who were not part of our previous samples, then we are confident that between 90% (-5%: 45 students) and 95% (+5%: 48 students) would also agree that the online teaching mode has a positive impact on their learning levels as during our research 90% of the students agreed. 3.4. Decision 4 - Confidence Level (CL) : The CL is the percentage of probability or certainty that the MOE/CI would contain the true population parameter when we draw a random sample many times. It is expressed as a percentage and represents how often the percentage of the research population who would pick an answer lies within the MOE/CI. For example, a 99% confidence level means that should we repeat an experiment or survey over and over again, 99 percent of the time, our results will match the results we get from a research population. In other words, there is only a 1% chance that the results from the research population will be less or more than the MOE/CI. Usually, a Confidence Level of 95% is acceptable if scholars belong to disciplines other than Basic Sciences, Medical Sciences, Clinical Studies, Engineering, Technology, or Health sciences else it needs to be kept at 99%. Do note that the higher the Confidence Level set during the research higher the reliability and validity of our research claim/finding/conclusion. Formula 1 - Population Size Known : Sample Size; Where, Where, (1)  ‘N’ is Population Size  ‘p’ is Sample Proportion  ‘MOE’ is the Margin of Error  ‘Z’ is a Critical value. It is a mathematical constant defined by the Confidence Level chosen. Standard values for ‘Z’ are; for 85% CL 1.440; for 90% CL 1.645; for 95% CL 1.960; for 99% CL 2.576. Formula 2 - Population Size Unknown : (2) Sample Size  ‘p’ is Sample Proportion  ‘MOE’ is the Margin of Error  ‘Z’ is a Critical value. It is a mathematical constant defined by the Confidence Level chosen. Standard values for ‘Z’ are; for 85% CL 1.440; for 90% CL 1.645; for 95% CL 1.960; for 99% CL 2.576. Once the scholars have made all the above four decisions their work is done. Now they need to enter the numbers of all these decisions into the standard sample size formula to derive the sample size for the research data collection. There are two formulas for calculating the sample size [55] such as i) formula 1 when we know the exact size of the research population (1), and ii) formula 2 when we do not know the research population size (2). Once scholars have derived the sample size, they need to remember to set their sample size as 20% higher than what they got from the formula. The additional sample size is always necessary as there are chances that the samples chosen by the scholars might not respond to all their questions/treatments/interventions or they might answer a few questions without much deliberation, or they might not turn up when scholars start the data collection process. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 293 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION 4. CHOOSING SAMPLES FROM THE RESEARCH POPULATION : Once the scholars have finalized ‘From How Many’ to collect the research data, now in the next step of the doctoral-level research process they need to finalize ‘From Whom’ (respondents/ participants/subjects/cases/groups/units of analysis/samples) to collect the research data that are representing the population of their research question. Selecting appropriate samples from the research population is also one of the easiest steps as the scholars’ task is to only choose one of the nine techniques. Choosing the right samples from the research population is also known as the Sampling/Sampling Technique. Though the procedure of selecting a sample differs according to the type of sample selected, certain fundamental rules remain the same that are listed below.  The research group or universe or population must be defined precisely.  Before choosing the sample, the unit of analysis/sample should be defined. A clear description of the sample based on the scholars’ research questions is mandatory. For example, Gender (Male/Female); Age; Marital Status (Married/Unmarried/Divorced); Occupation (Working/Non-working); Disease (New/Chronic/Hereditary/Non-hereditary); Customer (New/Existing).  The appropriate source list which contains the names of the units of a research group or universe or population from which the sample is to be selected should be prepared beforehand in case it does not already exist.  The size of the sample to be selected should be pre-determined as discussed in the previous step. Fig. 1: Population frame There are two main categories of Sampling Techniques viz, Non-random/Non-probability Sampling and Random/Probability Sampling [72-103]. Assume that our research population is a Research Methodology Classroom with 288 Ph.D. scholars in it. The Sample Size derived using formula 1 (population size is known) is 58 (keeping p=0.95; MOE=0.05; CL=95%). Let us now understand different types of Sampling Techniques with examples using this research population. Firstly, as we know the research population size we can create a frame of the research population as shown in figure 1 by giving each of the Ph.D. scholars a number or code. 4.1. Judgemental/Purposive Sampling : It is a Non-random/Non-probability Sampling Technique. In this type, we purely consider the purpose of our research, along with the understanding of the target population. For instance, when we want to understand the thought process of scholars interested in enrolling in a ‘Post-doc’ program after their Ph.D., our Sample selection criteria will be, asking a simple question to all the 288 scholars i.e., “are you interested in doing a Post-doc program after Ph.D.?” And those who respond with a “no” are excluded from the sampling. We will choose the scholars who said ‘yes’ to our question as our 58 H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 294 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Samples. This technique is illustrated in figure 2 with Samples being selected and highlighted with a grey-colored filling. Fig. 2: Sampling frame for Judgemental/Purposive sampling 4.2. Quota/Dimensional Sampling : It is a Non-random/Non-probability Sampling Technique. Quota sampling is where we take a very tailored sample that is in proportion to some characteristic or trait of the research population. For example, if our research population consists of 50% female and 50% male, our sample should reflect those percentages. If the Research Methodology classroom has 50% Male and 50% Female scholars then firstly we will divide our research population into two parts (Male and Female) as highlighted with red frames in figure 3 and we will select 29 males and 29 females from each part of our sampling frame as illustrated in figure 3. with samples being selected highlighted with a grey-colored filling. 4.3. Convenience Sampling : It is a Non-random/Non-probability Sampling Technique. In situations, wherein we have nearly no authority to select the sample elements, it is purely done based on proximity. Unfortunately, this technique is misunderstood by many Ph.D. scholars in India as choosing samples that are convenient for them. The convenience Sampling Technique must be chosen only in case the distance between the scholar and the sample is very long and it is impossible to collect research data from them. For example, if we are interested in understanding the impact of the Research Methodology class using a face-to-face interview (Survey method) that requires us to meet the sample in person then we might want to choose 58 scholars who are staying very close to (proximity) our place of stay/research/study. In this case, our sampling frame might look like the one illustrated in figure 4. with samples being selected highlighted with a grey-colored filling. Fig. 3: Sampling frame for Quota/Dimensional sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 295 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 4: Sampling frame for Convenience sampling 4.4. Snowball Sampling : It is a Non-random/Non-probability Sampling Technique. The process of Snowball sampling is much like asking our subjects/respondents/participants/groups/units of analysis/samples to nominate another one with the same characteristic/trait as our next Sample. We will then observe the nominated samples and continue in the same way until obtaining a sufficient number of samples. For example, if we are interested in understanding the key objective of scholars in the Research Methodology classroom to enroll in the Ph.D. program then we know that a majority of the scholars will not be giving an honest answer. In this case, Snowball Sampling Technique is the appropriate technique to select samples. Here we will ask the scholar who has honestly answered our question and use this scholar to select other scholars based on the nomination process. In this case, our Sampling frame might look like the one illustrated in figure 5, with samples being selected highlighted with a grey-colored filling. Fig. 5: Sampling frame for Snowball sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 296 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 6: Sampling frame for Simple Random sampling 4.5. Simple Random Sampling : It is a Random/Probability Sampling Technique. A probability sampling in which we simply select samples from the research population randomly. This technique ensures that each sample of the research population gets an equal chance of being selected. For example, if we are interested in understanding the impact of the Research Methodology class using an online questionnaire (Survey method) then we might choose 58 scholars randomly. In this case, our Sampling frame might look like the one illustrated in figure 6. with samples being selected highlighted with a grey-colored filling. 4.6. Systematic Sampling : It is a Random/Probability Sampling Technique. A type of probability sampling method in which samples from a larger research population are selected according to a random starting point but with a fixed, periodic interval. This interval is also called a Sampling Interval which is calculated by dividing the overall research population size by the desired sample size (in this example 288÷5 = 5). We will select a scholar after a sampling interval of 5. In this case, our Sampling frame might look like the one illustrated in figure 7. with samples being selected highlighted with a grey-colored filling. Fig. 7: Sampling frame for Systematic sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 297 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 8: Sampling frame for Stratified sampling 4.7. Stratified Sampling : It is a Random/Probability Sampling Technique. Involves the division of a research population into smaller sub-groups known as Strata. In stratified random sampling or stratification, the strata are formed based on samples' shared attributes or characteristics such as the ‘discipline’ of Ph.D. in the Research Methodology classroom example. All the research population elements are categorized into mutually exclusive and exhaustive groups (5 strata, 11 from each = 58 in this example). For example, if we want to ensure scholars from all the disciplines in the Research Methodology classroom are given an equal chance of being selected, we will first create Strata of each discipline (Allied Health Sciences, Education, Engineering, Social Sciences, and Management) and then randomly select scholars from each Strata. In this case, our Sampling frame might look like the one illustrated in figure 8. with samples being selected highlighted with a grey-colored filling. 4.8. Cluster/Area Sampling : It is a Random/Probability Sampling Technique. Involves the division of a population into smaller sub-groups known as Cluster. The clusters are formed based on samples’ shared attributes or characteristics such as scholars under a Research Supervisor/Guide in addition to a discipline (Allied Health Sciences, Education, Engineering, Social Sciences, and Management). Here we will divide the research population by discipline and then within each discipline, we will choose ‘all’ scholars under a specific Research Supervisor/Guide. Do note that the randomization is only in choosing the Research Supervisor/Guide and not the scholars under a Supervisor/Guide. In this case, our Sampling frame might look like the one illustrated in figure 9, with samples being selected highlighted with a grey- colored filling. Fig. 9: sampling frame for Cluster/Area sampling H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 298 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Fig. 10: sampling frame for Multi-stage sampling 4.9. Multistage Sampling : It is a Random/Probability Sampling Technique. The research population is partitioned into groups, like cluster sampling, but in this design new samples are taken from each cluster sampling. Two-stage sampling is used when the sizes of the clusters are large, making it difficult or expensive to observe all the units inside them. For example, if we are interested in knowing the impact of the Research Methodology course on the Ph.D. scholar and decide to choose Multistage Sampling. We will firstly divide (Stage 1) the entire population by their discipline, secondly, we will divide the scholars in each discipline by their gender (Stage 2), and lastly we will divide the scholars in each gender by their Ph.D. type (Full-time and Part-time; Stage 3). Only after the three stages of division, we will then randomly choose the samples from each stratum. In this case, our Sampling frame might look like the one illustrated in figure 10, with samples being selected highlighted with a grey-colored filling. 4.10. Choosing an Appropriate Sampling Technique : After understanding all the available Sampling Techniques, scholars might be thinking that all of them sound good but how do choose one of them? We recommend scholars consider the following while they choose a Sampling Technique to select samples from their research population.  The level of homogeneity in the population.  Existing knowledge about the variables and units of analysis of the research question.  The level of accuracy and precision required to claim the research findings.  Cost and time required for Sampling Technique chosen. We suggest scholars avoid Non-random/Non-probability sampling techniques unless it is the last resort. Use them during the early/exploratory stages/phases of the research. Do note that the higher the difficulty level of the sampling technique lesser the error in the research findings/claims. And irrespective of the sampling technique scholars decide to choose, always try, and select at least 20% more samples than the derived Sample Size. There are chances that the samples chosen by the scholars might not respond to all their questions/treatments/interventions or they might answer a few questions without much deliberation, or they might not turn up when scholars start the data collection process. 5. CONCLUSION : Among the two main Sampling Techniques available Random/Probability sampling is the most preferred among scholars belonging to the Basic/Natural Science, Engineering, and Technology disciplines, and Non-random/Non-probability sampling is the most preferred for scholars belonging to other disciplines in India. We understand the Ph.D. program is time-bound and hence using one of the Non-random/Non-probability sampling techniques during the Ph.D. program is acceptable. But knowingly or unknowingly, intentionally, or intentionally a significant majority of researchers in India use Non-random/Non-probability sampling techniques even after the completion of the Ph.D. program. The fear among Indian researchers is that Random/Probability sampling techniques require H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 299 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION a lot of time investment, they are complicated, and most importantly the research output in the form of research article publications will slow down drastically. The mere pressure on Ph.D. scholars and Ph.D. holders in India to publish a certain number of research articles which is connected to their performance measurement is also one of the key reasons for this. Ph.D. scholars and Ph.D. holders must be aware that a scholarly description, explanation, or claim about a reality/fact/truth/effect/dependent variable and a piece of complete knowledge about reality is complete only when they are derived from collecting and evaluating data using multiple sampling techniques i.e., ensuring an equal opportunity was given to each sample of the research population to get selected. It is the responsibility of every stakeholder in the research environment and system to ensure that the scholars are made aware of every step involved in carrying out doctoral-level research in addition to the purpose, objective, and key deliverables of various available sampling techniques for them to choose an appropriate one to achieve their key research objective during the Ph.D. journey. Designing robust coursework that is intended to create awareness about the essence of sample size and sampling techniques is an appropriate way of fulfilling this responsibility. As long as the Ph.D. scholars can understand an appropriate sample size and available sampling techniques and make mindful choices of sample size and sampling technique across various stages/phases of the research to answer their research question they will be able to determine (on their own) all the other choices in succeeding steps of doctoral-level research such as i) data collection instrument and iii) data analysis techniques. REFERENCES : [1] Titus, S. L., & Ballou, J. M. (2013). Faculty members’ perceptions of advising versus mentoring: Does the name matter?. Science and Engineering ethics, 19(3), 1267-1281. Google Scholar [2] Ali, A., & Kohun, F. (2006). Dealing with isolation feelings in IS doctoral programs. International Journal of Doctoral Studies, 1(1), 21-33. Google Scholar [3] Ali, A., Kohun, F., & Levy, Y. (2007). Dealing with Social Isolation to Minimize Doctoral Attrition- A Four Stage Framework. International Journal of Doctoral Studies, 2(1), 33-49. Google Scholar [4] Spaulding, L. S., & Rockinson-Szapkiw, A. (2012). Hearing their voices: Factors doctoral candidates attribute to their persistence. International Journal of Doctoral Studies, 7, 199. Google Scholar [5] Golde, C. M., & Dore, T. M. (2001). At cross purposes: What the experiences of today's doctoral students reveal about doctoral education, ERIC Processing and Reference Facility, 1-62. Google Scholar [6] Golde, C. M. (2005). The role of the department and discipline in doctoral student attrition: Lessons from four departments. The Journal of Higher Education, 76(6), 669-700. Google Scholar [7] Golde, C. M., & Walker, G. E. (Eds.). (2006). Envisioning the future of doctoral education: Preparing stewards of the discipline-Carnegie essays on the doctorate (Vol. 3). John Wiley & Sons. Google Scholar [8] Gardner, S. K. (2009). Student and faculty attributions of attrition in high and low-completing doctoral programs in the United States. Higher education, 58(1), 97-112. Google Scholar [9] Gardner, S. K. (2010). Faculty perspectives on doctoral student socialization in five disciplines. International Journal of Doctoral Studies, 5(1), 39-51. Google Scholar [10] Solmon, M. A. (2009). How do doctoral candidates learn to be researchers? Developing research training programs in kinesiology departments. Quest, 61(1), 74-83. Google Scholar [11] Nogueira-Martins, L. A., Fagnani Neto, R., Macedo, P. C. M., Citero, V. D. A., & Mari, J. D. J. (2004). The mental health of graduate students at the Federal University of São Paulo: a preliminary report. Brazilian Journal of Medical and Biological Research, 37, 1519-1524. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 300 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [12] Knox, S., Schlosser, L. Z., Pruitt, N. T., & Hill, C. E. (2006). A qualitative examination of graduate advising relationships: The advisor perspective. The Counseling Psychologist, 34(4), 489-518. Google Scholar [13] Grady, R. K., La Touche, R., Oslawski-Lopez, J., Powers, A., & Simacek, K. (2014). Betwixt and between: The social position and stress experiences of graduate students. Teaching Sociology, 42(1), 5-16. Google Scholar [14] Russell, J., Gaudreault, K. L., & Richards, K. A. R. (2016). Doctoral student socialization: Educating stewards of the physical education profession. Quest, 68(4), 439-456. Google Scholar [15] Russell, J. A. (2015). Rolling with the punches: Examining the socialization experiences of kinesiology doctoral students. Research quarterly for exercise and sport, 86(2), 140-151. Google Scholar [16] Harding-DeKam, J. L., Hamilton, B., & Loyd, S. (2012). The hidden curriculum of doctoral advising. NACADA Journal, 32(2), 5-16. Google Scholar [17] Mansson, D. H., & Myers, S. A. (2012). Using mentoring enactment theory to explore the doctoral student–advisor mentoring relationship. Communication Education, 61(4), 309-334. Google Scholar [18] Robinson, E. M., & Tagher, C. G. (2017). The companion dissertation: Enriching the doctoral experience. Journal of Nursing Education, 56(9), 564-566. Google Scholar [19] Haynes, K. N. (2008). Reasons for doctoral attrition. Health, 8, 17-4. Google Scholar [20] Mazerolle, S. M., Bowman, T. G., & Klossner, J. C. (2015). An analysis of doctoral students' perceptions of mentorship during their doctoral studies. Athletic Training Education Journal, 10(3), 227-235. Google Scholar [21] Holsinger Jr, J. W. (2008). Situational leadership applied to the dissertation process. Anatomical Sciences Education, 1(5), 194-198. Google Scholar [22] McNamara, J. F., Lara-Alecio, R., Hoyle, J., & Irby, B. J. (2010). Doctoral program issues: Commentary on companion dissertations. A Doctoral Issues Presentation at the National Council of Professors of Educational Administration Lexington, KY, August 2, 2006. Google Scholar [23] Carter-Veale, W. Y., Tull, R. G., Rutledge, J. C., & Joseph, L. N. (2016). The dissertation house model: Doctoral student experiences coping and writing in a shared knowledge community. CBE—Life Sciences Education, 15(3), ar34. Google Scholar [24] Devos, C., Boudrenghien, G., Van der Linden, N., Azzi, A., Frenay, M., Galand, B., & Klein, O. (2017). Doctoral students’ experiences leading to completion or attrition: A matter of sense, progress and distress. European journal of psychology of education, 32(1), 61-77. Google Scholar [25] Beatty, S. E. (2001). The doctoral supervisor-student relationship: some American advice for success. The Marketing Review, 2(2), 205-217. Google Scholar [26] Carpenter, S., Makhadmeh, N., & Thornton, L. J. (2015). Mentorship on the doctoral level: An examination of communication faculty mentors’ traits and functions. Communication Education, 64(3), 366-384. Google Scholar [27] Most, D. E. (2008). Patterns of doctoral student degree completion: A longitudinal analysis. Journal of College Student Retention: Research, Theory & Practice, 10(2), 171-190. Google Scholar [28] Stock, W. A., Siegfried, J. J., & Finegan, T. A. (2011). Completion rates and time-to-degree in economics PhD programs (with comments by David Colander, N. Gregory Mankiw, Melissa P. McInerney, James M. Poterba). American Economic Review, 101(3), 176-88. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 301 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [29] Wamala, R., Ocaya, B., & Oonyu, J. C. (2012). Extended Candidature and Non-Completion of a Ph. D. at Makerere University, Uganda. Contemporary Issues in Education Research, 5(3), 175-184. Google Scholar [30] https://academy.pubs.asha.org/2011/12/higher-education-practices-that-promote-phd- completion/. Retrieved in October 2022. [31] Preston, J. P., Ogenchuk, M. J., & Nsiah, J. K. (2014). Peer mentorship and transformational learning: PhD student experiences. Canadian Journal of Higher Education, 44(1), 52-68. Google Scholar [32] Devine, K., & Hunter, K. H. (2017). PhD student emotional exhaustion: the role of supportive supervision and self-presentation behaviours. Innovations in Education and Teaching International, 54(4), 335-344. Google Scholar [33] Van Rooij, E., Fokkens-Bruinsma, M., & Jansen, E. (2021). Factors that influence PhD candidates’ success: the importance of PhD project characteristics. Studies in Continuing Education, 43(1), 48-67. Google Scholar [34] Chenevix-Trench, G. (2006). What makes a good PhD student?. Nature, 441(7090), 252-252. Google Scholar [35] Dericks, G., Thompson, E., Roberts, M., & Phua, F. (2019). Determinants of PhD student satisfaction: the roles of supervisor, department, and peer qualities. Assessment & evaluation in higher education volume 44(7), 1053-1068. Google Scholar [36] Corsini, A., Pezzoni, M., & Visentin, F. (2022). What makes a productive Ph. D. student?. Research Policy 51(10), 104561. Google Scholar [37] Lindvig, K. (2018). The implied PhD student of interdisciplinary research projects within monodisciplinary structures. Higher Education Research & Development, 37(6), 1171-1185. Google Scholar [38] Holbrook, A., Shaw, K., Scevak, J., Bourke, S., Cantwell, R., & Budd, J. (2014). PhD candidate expectations: Exploring mismatch with experience. International Journal of Doctoral Studies, 9, 329. Google Scholar [39] Björkman, B. (2015). PhD supervisor-PhD student interactions in an English-medium Higher Education (HE) setting: Expressing disagreement. European Journal of Applied Linguistics, 3(2), 205-229. Google Scholar [40] Dimitrova, R. (2016). Ingredients of good PhD supervision-evidence from a student survey at Stockholm university. Utbildning och Lärande/Education and Learning, 10(1), 40-52. Google Scholar [41] Sullivan-Bolyai, S., & L'Esperance, S. (2022). Reflections on virtual research conferences and PhD student socialization: The missing link of in-person human connectedness. Applied Nursing Research, 64 (April 2022), 151553. Google Scholar [42] Alpert, F., & Eyssell, T. H. (1995). Getting the most from your doctoral program: Advice for the Ph. D. student in finance. Journal of Financial Education, 12-20. Google Scholar [43] Groen, J. (2020). Perceptions of Transformation and Quality in Higher Education: A Case Study of PhD Student Experiences (Doctoral dissertation, University of Ottawa). Google Scholar [44] Helfer, F., & Drew, S. (2013). A small-scale investigation into Engineering PhD student satisfaction with supervision in an Australian university campus. In 24th Annual Conference of the Australasian Association for Engineering Education (pp. 1-9). Google Scholar [45] Cunningham-Williams, R. M., Wideman, E., & Fields, L. (2019). Ph. D. Student Development: A Conceptual Model for Research-Intensive Social Work PhD Programs. Journal of Evidence- Based Social Work, 16(3), 278-293. Google Scholar [46] Ganesha, H. R. & Aithal, P. S. (2022). Doing Ph.D. in India. A Step-by-Step Guide. First Edition. H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 302 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION Notion Press. India & Singapore. ISBN: 9798887832005. Google Scholar [47] Ganesha, H. R. & Aithal, P. S. (2022). The ‘8Fs’ Concept for Simplifying the Complications of Ph.D. Journey in India. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 6(2), 320-339. Google Scholar [48] Ganesha, H. R. & Aithal, P. S. (2022). The DDLR Model of Research Process for Designing Robust and Realizable Research Methodology During Ph.D. Program in India. International Journal of Management, Technology, and Social Sciences (IJMTS), 7(2), 400-417. Google Scholar [49] Ganesha, H. R. & Aithal, P. S. (2022). PHDRQ Model for Identifying Research Gaps and Formulating A Research Question During Ph.D. Program in India. International Journal of Case Studies in Business, IT, and Education (IJCSBE), 6(2). 408-421. Google Scholar [50] Ganesha, H. R. & Aithal, P. S. (2022). Why is it Called Doctor of Philosophy and Why Choosing Appropriate Research Philosophical Paradigm is Indispensable During Ph.D. Program in India?. International Journal of Philosophy and Languages (IJPL), 1(1). 42-58. Google Scholar [51] Ganesha, H. R. & Aithal, P. S. (2022). Approaching Research in Different Ways. How to Choose an Appropriate Research Approach/Reasoning During Ph.D. Program in India?. International Journal of Philosophy and Languages (IJPL), 1(1). 59-74. Google Scholar [52] Ganesha, H. R. & Aithal, P. S. (2022). How to Choose an Appropriate Research Data Collection Method and Method Choice Among Various Research Data Collection Methods and Method Choices During Ph.D. Program in India?. International Journal of Management, Technology, and Social Sciences (IJMTS), 7(2), 455-479. Google Scholar [53] Ganesha, H. R. & Aithal, P. S. (2022). When to Collect Data? Choosing an Appropriate Time Frame for Data Collection During Ph.D. Program in India?. International Journal of Applied Engineering and Management Letters (IJAEML), 6(2), 271-287. Google Scholar [54] Lee, Nick, & Lings, Ian. (2008). Doing business research: a guide to theory and st practice. 1 Edition, Sage Publications Ltd., Page 293. Google Scholar [55] Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons. Google Scholar [56] Woolson, R. F., Bean, J. A., & Rojas, P. B. (1986). Sample size for case-control studies using Cochran's statistic. Biometrics, 927-932. Google Scholar [57] Kotrlik, J. W. K. J. W., & Higgins, C. C. H. C. C. (2001). Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Information technology, learning, and performance journal, 19(1), 43. Google Scholar [58] Ahmad, H., & Halim, H. (2017). Determining sample size for research activities. Selangor Business Review, 20-34. Google Scholar [59] Song, J. X., & Wassell, J. T. (2003). Sample size for K 2× 2 tables in equivalence studies using Cochran's statistic. Controlled clinical trials, 24(4), 378-389. Google Scholar [60] Nam, J. M. (1992). Sample size determination for case-control studies and the comparison of stratified and unstratified analyses. Biometrics, 389-395. Google Scholar [61] Milton, S. (1986). A sample size formula for multiple regression studies. Public Opinion Quarterly, 50(1), 112-118. Google Scholar [62] Mehta, C. R., Patel, N. R., & Senchaudhuri, P. (1998). Exact power and sample-size computations for the Cochran-Armitage trend test. Biometrics, 1615-1621. Google Scholar [63] Donner, A. (1992). Sample size requirements for stratified cluster randomization designs. Statistics in medicine, 11(6), 743-750. Google Scholar [64] Nam, J. M. (1987). A simple approximation for calculating sample sizes for detecting linear trend in proportions. Biometrics, 701-705. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 303 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [65] Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management, 2(11), 1-22. Google Scholar [66] Casagrande, J. T., Pike, M. C., & Smith, P. G. (1978). An improved approximate formula for calculating sample sizes for comparing two binomial distributions. Biometrics, 483-486. Google Scholar [67] Cochran, W. G. (1942). Sampling theory when the sampling-units are of unequal sizes. Journal of the American Statistical Association, 37(218), 199-212. Google Scholar [68] Snijders, T. A., & Bosker, R. J. (1993). Standard errors and sample sizes for two-level research. Journal of educational statistics, 18(3), 237-259. Google Scholar [69] Czaplewski, R. L., Crowe, D. M., & McDonald, L. L. (1983). Sample sizes and confidence intervals for wildlife population ratios. Wildlife Society Bulletin (1973-2006), 11(2), 121-128. Google Scholar [70] Kasiulevičius, V., Šapoka, V., & Filipavičiūtė, R. (2006). Sample size calculation in epidemiological studies. Gerontologija, 7(4), 225-231. Google Scholar [71] Wang, J., Ge, G., Fan, Y., Chen, L., Liu, S., Jin, Y., & Yu, J. (2006). The estimation of sample size in multi-stage sampling and its application in medical survey. Applied Mathematics and Computation, 178(2), 239-249. Google Scholar [72] Taherdoost, H. (2016). Sampling methods in research methodology; how to choose a sampling technique for research. How to choose a sampling technique for research (April 10, 2016). Available at SSRN. Google Scholar [73] Etikan, I., Alkassim, R., & Abubakar, S. (2016). Comparision of snowball sampling and sequential sampling technique. Biometrics and Biostatistics International Journal, 3(1), 55. Google Scholar [74] Sharma, G. (2017). Pros and cons of different sampling techniques. International journal of applied research, 3(7), 749-752. Google Scholar [75] Trost, J. E. (1986). Statistically nonrepresentative stratified sampling: A sampling technique for qualitative studies. Qualitative sociology, 9(1), 54-57. Google Scholar [76] Vehovar, V., Toepoel, V., & Steinmetz, S. (2016). Non-probability sampling (pp. 329-345). The Sage handbook of survey methods. Google Scholar [77] Acharya, A. S., Prakash, A., Saxena, P., & Nigam, A. (2013). Sampling: Why and how of it. Indian Journal of Medical Specialties, 4(2), 330-333. Google Scholar [78] Schillewaert, N., Langerak, F., & Duharnel, T. (1998). Non-probability sampling for WWW surveys: a comparison of methods. Market Research Society. Journal., 40(4), 1-13. Google Scholar [79] Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 00149. Google Scholar [80] Barendregt, C., van der Poel, A., & van de Mheen, D. (2005). Tracing selection effects in three non-probability samples. European Addiction Research, 11(3), 124-131. Google Scholar [81] Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226. Google Scholar [82] Schreuder, H. T., Gregoire, T. G., & Weyer, J. P. (2001). For what applications can probability and non-probability sampling be used?. Environmental Monitoring and Assessment, 66(3), 281- 291. Google Scholar [83] DiSogra, C., Cobb, C., Chan, E., & Dennis, J. M. (2011, August). Calibrating non-probability internet samples with probability samples using early adopter characteristics. In Joint Statistical Meetings (JSM), Survey Research Methods (pp. 4501-4515). Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 304 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [84] Lehdonvirta, V., Oksanen, A., Räsänen, P., & Blank, G. (2021). Social media, web, and panel surveys: using non‐probability samples in social and policy research. Policy & internet, 13(1), 134-155. Google Scholar [85] Ayhan, H. Ö. (2011). Non-probability Sampling Survey Methods. International encyclopedia of statistical science, 14, 979-982. Google Scholar [86] Kandola, D., Banner, D., O’Keefe-McCarthy, S., & Jassal, D. (2014). Sampling Methods in Cardiovascular Nursing Research: An Overview. Canadian Journal of Cardiovascular Nursing, 24(3). Google Scholar [87] Buelens, B., Burger, J., & van den Brakel, J. A. (2018). Comparing inference methods for non‐ probability samples. International Statistical Review, 86(2), 322-343. Google Scholar [88] Buelens, B., Burger, J., & van den Brakel, J. (2015). Predictive inference for non-probability samples: a simulation study (Vol. 13, pp. 1-46). The Hague: Statistics Netherlands. Google Scholar [89] Li, P., Church, K., & Hastie, T. (2006). Conditional random sampling: A sketch-based sampling technique for sparse data. Advances in neural information processing systems, 19. Google Scholar [90] Clarkson, K. L., & Shor, P. W. (1989). Applications of random sampling in computational geometry, II. Discrete & Computational Geometry, 4(5), 387-421. Google Scholar [91] Olken, F., & Rotem, D. (1995). Random sampling from databases: a survey. Statistics and Computing, 5(1), 25-42. Google Scholar [92] Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce and management, 2(11), 1-22. Google Scholar [93] Niemierko, A., & Goitein, M. (1990). Random sampling for evaluating treatment plans. Medical physics, 17(5), 753-762. Google Scholar [94] Drott, M. C. (1969). Random sampling: a tool for library research. College & Research Libraries, 30(2), 119-125. Google Scholar [95] Ding, C. S., Haieh, C. T., Wu, Q., & Pedram, M. (1996, November). Stratified random sampling for power estimation. In Proceedings of International Conference on Computer Aided Design (pp. 576-582). IEEE. Google Scholar [96] Amir, B., & Ralph, P. (2018, May). There is no random sampling in software engineering research. In Proceedings of the 40th international conference on software engineering: companion proceeedings (pp. 344-345). Google Scholar [97] Endo, T., Yamamoto, A., & Watanabe, T. (2016). Bias factor method using random sampling technique. Journal of Nuclear Science and Technology, 53(10), 1494-1501. Google Scholar [98] Martino, L., Luengo, D., & Míguez, J. (2018). Independent random sampling methods (pp. 65- 113). Martino: Springer International Publishing. Google Scholar [99] Chaudhuri, S., Motwani, R., & Narasayya, V. (1998). Random sampling for histogram construction: How much is enough?. ACM SIGMOD Record, 27(2), 436-447. Google Scholar [100] Cooper, S. L. (1964). Random sampling by telephone—An improved method. Journal of Marketing Research, 1(4), 45-48. Google Scholar [101] Kim, J. K., Park, S., Chen, Y., & Wu, C. (2021). Combining non‐probability and probability survey samples through mass imputation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(3), 941-963. Google Scholar [102] Van Haute, E. (2021). SAMPLING TECHNIQUES. Research Methods in the Social Sciences: an AZ of Key Concepts, 247. Google Scholar H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 305 International Journal of Applied Engineering and Management SRINIVAS Letters (IJAEML), ISSN: 2581-7000, Vol. 6, No. 2, November 2022 PUBLICATION [103] Rahman, M. M., Tabash, M. I., Salamzadeh, A., Abduli, S., & Rahaman, M. S. (2022). Sampling techniques (probability) for quantitative social science researchers: a conceptual guidelines with examples. Seeu Review, 17(1), 42-51. Google Scholar ****** H. R. Ganesha, et al. (2022); www.srinivaspublication.com PAGE 306

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