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==> If you’re a researcher/student…needing sample determination guide, ==>Scroll down to Point IV
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You have been asking yourself many questions about sample size determination, haven’t you? Well, today I’m going to help you answer some of your questions.
Q1: What is a survey? what is survey in research?
==>Popular or dictionary answer:
To survey is “To ask people questions in order to find out about their opinions or behaviour”
e.g. – The researchers surveyed the attitudes of 2500 college students.
– Many of the listeners surveyed said that they were not satisfied with the station’s programmes.
A survey is a type of research whereby a researcher seeks information about individuals’ self-reports of their knowledge, attitudes, or behaviors. One reports about themselves and information given is about the “self” of someone. But surveys can also be used to collect information about other one’s friends’ or other people’s knowledge, attitudes, or behaviors as well, (Mertens, 2010; Wagiran, 2014).
To be elaborate, survey research is used to:
answer questions that have been raised, to solve problems that have been posed or observed, to assess needs and set goals, to determine whether or not specific objectives have been met, to establish baselines against which future comparisons can be made, to analyze trends across time, and generally, to describe what exists, in what amount, and in what context (Glasow, 2005, p.1).
==>A survey research is a type of research while a survey can simply mean a data collection device used in such a type of research. But if you read Mertens (2010, p.173-204), you’ll discover that a survey is not exactly a device to collect data as there are many data collection devices used in such research study that are familiar to you: questionnaire, interview, mailing (remember to enclose a transmittal letter if you choose to mail a survey to subjects), and web-based survey (practical e.g. is to send a google survey form to people on your email list, WeChat/FB/WhatsApp/ etc. Groups), and many more data collection instruments.
Q2. what is a research sample?
Wagiran (2015) defines a sample as a small group that is taken from the population (and representative of the population) to be observed/researched called sample.
Other definitions of sample:
- Part of a population that can represent the entire population
- Part of the population that is used as research objects;
- Miniature (microcosm) population
- Samples that present/have the same (or relatively) the population (=’all members of a well define class of people, events, or object’, Ary & at al., 1985, p. 138; Wagiran, 2014, p. 167) characteristics are called representative samples.
Q3: What are the different sampling methods and techniques?
Probability sampling method roots its name in the law probability. The sample determined using this method results in selecting sample subjects in such a way that every member of the population actually has a possibility of being included in the sample. This because probability-based samples can be rigorously analyzed to determine possible bias and likely error. There is no such advantage for nonprobability samples
Non-probability sampling method does not provide equal to any element or member of the population to be selected as a sample subject. This is because the technique is too confident in the personal opinion of the researcher rather than in equalizing chances to all members to be selected as sample subjects. Some subjects have a greater chance than others to be selected which is likely to practically prevent the results of a study using this sampling method to be used to predict the population’s characteristics or behaviours. Such sample method does not allow the results to be generalized as the sample is not representative of the population.
- Probabilistic Sampling techniques
- Simple random sampling
- Stratified random sampling
- Proportional random Sampling
- Disproportional stratified sampling
- Cluster random sampling
- Systematic random sampling
- Multi-stage-random Sampling
- Non- Probabilistic Sampling techniques
Commonly made mistakes in in sampling for surveys include coverage, nonresponse, and sampling. Coverage errors arise in two circumstances: (a) people who should be in the sampling frame are not there, or (b) people who are truly ineligible (i.e., they are not members of the target population) are in the sampling frame, (Braverman, 1996; Mertens, 2010).
According to Krathwohl in Mertens (2010), the sample size optimally relates directly to the type of research that is to be carried out. For different types of research, there are practical rules that can be used to determine the appropriate sample size.
Accomplished researchers suggest the use of larger sample sizes. Larger samples have more possibilities to be a representative for the population. Besides, with a large sample, data can be more accurate. In other words, the larger the sample, the smaller the sample error standard.
Sample size is the number of individuals, subjects or elements that are representative of the population. Both too large or too small sample pose problems: a good sample is a sample that reflects optimally its population (representativeness). Representativeness cannot be proved, but it can be theoretically and experimentally known and acknowledged for its goodness.
There are four factors to be considered in determining the sample size in a study:
- Degree of uniformity (degree of homogeneity) of the population. The more uniform the population, the smaller the sample to be taken. If the population is completely uniform, then one elementary unit of the entire population is representative enough to be researched. Conversely, if the population is perfect not completely heterogeneous then only the complete counting can give a representative picture.
- The desired precision of the research. The higher the level of precision desired, the bigger the sample size. So, a large sample tends to give an interview closer to true value. In other words, there is an inverse relationship between the size of the sample and the magnitude of the sampling error: the greater the sample, the fewer errors/irregularities in the population representativeness of the sample.
- Analysis plan. Sometimes the size of the sample is sufficient according to the desired precision, but when associated with the analysis plan, the sample amount becomes insufficient.
- Energy, cost and time. If you want high precision then the sample size should be larger. But if the funds, power and time are limited then it is not possible to take a large sample, and this means the level of precision will decrease.
Two commonly (easy) sample size determination: Two experts’ opinions/ways:
Sevila (1993) in Wagiran (2014: 172) suggests, in determining the sample size, the following formula:
N = Population
n = Sample size
e = Error Limit
Let’s consider a research with population N=9000 and a desired error limit is 2%. What size of sample size would be representative of N?
n= 9000/ (1 + 9000 (0.02)2) = 1957
Keep in mind that assuming normal distribution of the population should remain considered. When the distribution is not normal, this formula should not be used. The determination of sample should be based on non-parametric statistics.
Another sample size determination is inherent to Alain Bouchar who argues that:
when the universe of a survey is inferior or equal to 1,000,000 of individuals that correspond to the sample of 96 within a marginal errors of 10% and a precision of 95% times out of 100 as stated below:
Given N=the height of finished universe;
n=the height of sampling for the finished universe 96;
nc= The corrected sampling (Hakizimana, 2017)
Following such a formula, in a N=641, the S (=Sample, nc in this formula) is 84:
Glasow, P. A. (2005). Fundamentals of survey research methodology. Retrieved January, 18, 2013
Mertens, M. D. (2010). Research and Evaluation in Education and Psychology. Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd Ed). London: Sage
Sugiyono. (2015). Metode Penelitian Pendidikan: Pendekatan Kuantitatif, Kualitatif, dan R&D. Bandung: ALFABETA, CV
Course Notes by Prof. Okebukola, 2018-2019
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