One of the most agonizing decision statisticians or researchers while conducting any survey is the sample that they are going to use. The question is always like, “what size of the sample do I need?” it has been found that the sample size to be used in any research survey is not definite but depends on the question of survey itself. Some research survey objectives are to determine the proportion of a given staff while others are focused in determining the sample mean. For instance since the bank survey is a quantitative research study, the researchers prior to determining the most ideal sample size, should consider a number of factors. The factors are;
Even as the above questions are expected to guide the survey on the number of participants to include in the sample, it is advised that researchers should use more and more participants so as to accurately estimate the population (Shein-chung, 2008). If the response rate is achieved then the sample is normally said to be ideal. So a sample size of 21% out of 69,000 employees according to many sampled researches can either be large or small.
Advantages of having a small sample size
Small sample sizes in research come with different advantages. These advantages can be categorized as either technical or economical. In economic perspective, small sample sizes leads to usage of less money and other resources employed in the research. For example in the banking survey, less money would be used to print and send questionnaires to the respondents. To add on, less money would be used to employ staff to undertake the research activities unlike if the sample size was large. On technical grounds, small sample sizes leads to consumption of less time in terms of data collection and data analysis since the research will be dealing with less number of questionnaires similar to if data was being collected through face –to –face interview. Lastly a survey is able to achieve a higher efficiency in terms of the design employed when the sample size is small.
Small sample sizes can influence power. It usually therefore follow that the larger the sample, the higher the power and the smaller the sample, the lower the power. Power in statistics can be described as the capability of a test to establish with high precision that a given attribute in the sample really exists in the population. So if the sample is small, it causes a decline in power which in turn reduces the ability of the test to reliably show attributes that are being looked for in the sample.
The chances of committing a type II error are also very high when a small sample size is used. Type II error is committed when we fail to accept the alternative hypothesis yet it is true and accept the null hypothesis yet it is false. This means that a study can really end up with wrong results.
Large samples in a research enable researchers to easily determine significance difference that exist between data or variables with a lot of certainty. In small sample sizes, this difference is difficult to notice.
It can also be said that large sample size helps a research determine a quality measurement or value about the whole population. For example if the mean weight of 50 students in a class is 52kg and the mean weight of another 150 student of the same is 48kg, then 48kg will be picked as the best population estimator of the mean. Large sample size is also not affected so much by outliers like in the case of small sample sizes. An outlier is a data point that deviates largely from the sample mean.
Large sample size should be used with caution as sometimes it can transform the insignificant to significant. For example in a t-test, a large sample can exaggerate a difference of 0.005 to be significant yet it is not.
Extremely large data are not the best when determining the distribution or data. This is because the large samples make the goodness-of-fit test to be extremely sensitive to even very tiny deviations from the distribution. So it is advisable to employ graphical methods and probability plots to ascertain the distribution of a data set.
If study just selected the bank employees at random, then it employed simple random sampling to select the employees to take part in the study. Simple random sampling is a probability sampling which is also known as equal chance sampling (Altman, 2004). In this method, every member of the population has got an equal chance of being selected. It does not employ any tricks or criteria to select.
One of the advantages of random sampling is that it is usually easy to employ and it is also very accurate compared to other sampling methods when it comes to representing the whole population. Let alone prejudice, simple random sampling has got no room for bias. Another advantage which seems to be one of the major ones is, in random sampling only basic prior knowledge is needed about the population under study. Again, in this method, choosing of participants is guided by randomness. This makes it very possible for any member to be selected from the population. Lastly but not least, simple random sampling facilitates the easiness to establish errors.
The shortcomings that come along with simple random sampling as a sampling method can be economical, experimental or technical. From the economic perspective, it is costly since this method requires an entire list of the population for it to use the sample for it to have an accurate measure of the entire population. On the same note, getting the a complete list of the population for example students in a school can be time consuming as well as challenging due to the bureaucracies that might be there. To add on, effects of biasness might be so pronounced if this method is applied to select a small sample. Therefore it is recommended that when simple random sampling is used, the sample should be made relatively larger to eliminate chances of biasness. On the same note, application of random sampling solely without proper guidelines can lead the survey to obtain wrong result due to inclusion of many irrelevant participants in the survey courtesy of random sampling. For example if a research wanted to test a certain trait among that is only common in women, then it would be proper for the study to purely concentrate on women. If in the selection of the sample the study employs random sampling and ends up with 70% of the sample being men, then the results of such a composition cannot be a representative of the women. It will be quite bias.
The first technique of reducing high cost in data collection associated with simple random sampling is to reduce the sample size or the number of participants from whom data is to be collected. Simple random sampling should be used in online surveys where individuals are selected randomly online. The selected individuals are then made aware of the study then a questionnaire is sent to them via email for them to fill. This can reduce cost of the survey and time as the researcher does not need to travel physically to where the “would be respondents” are. The responses or data received will easily be transferred to the tools of analysis since the responses will be electronic. This also reduces time and labor that would have been used by data entrants who have been hired by the research. This method can also be improved by ensuring that the time period of data collection is shortened so that money is not used on so many number of days while collecting the sample.
One of the major problems in the process of data collection is the issue of time. Sometimes interviewers are compelled to collect first-hand information from respondents within a very short span of time. This not only interferes with the quality and credibility of data collected but also reduces the rate of response to a study. The best remedy to this challenge is for the research to have a prior elaborate plan that allows for more time for data collection while at the same time taking caution of excessive time whose repercussion is high cost.
Another challenge is the issue of the length and complexity of the questions in the questionnaire. Some questionnaires are usually too long. A participant is sometimes forced to take long to complete the questionnaire. The problem with this is that many respondents do not fill all the questions thereby not giving 100% response leading to cases of missing data (Pieto, 2004). The other problem with the questionnaire is complexity or ambiguity of the questions being asked. This makes it very difficult for the respondents to understand the questions and answer them properly. The research should always ensure that the questionnaire is short precise and to the point but at the same time ensuring that the questions in the questionnaire are in line with the research’s objective. As people have got different levels of intellectual capacities, the language in the questionnaire should be such as it is easy to read and understand. To add on, the geographical distance can also make data collection to be very cumbersome and challenging. In cases where the respondents are far much apart, it becomes difficult to distribute and collect the questionnaires back. Researches should come up with ways of both distributing the questionnaires and collecting them back without feeling the pinch of distance and time. I suggest that the questionnaires be sent to respondents via e-mails or sms. The filled up questionnaires should also be sent back by the interviewee to the researcher via the same. Unfortunately to the researcher, some respondents are usually very hostile and rude. Such individuals when asked questions, they either ignore or answer rudely and gives false answers. Language barrier also pose great challenge during data collection. This happens when your respondents understand different languages. It is therefore prudent for the interviewer to understand the respondent first before sending the questionnaire to him or her.
There are two types of data. These are primary data and secondary data. Primary data is data that has been obtained directly from the respondents through interviews, observation and questionnaires while secondary data refers to data obtained from past records such as textbooks, journals among others (Hamlin, 2005). Secondary data can be employed to establish the representativeness of a population by checking of the results of a past study whose results were recorded and stored and therefore can be obtained. Example of such data includes census data. The results of a census are usually the true to the population since in census every element in the area of study takes part in the exercise. So to check the representativeness of a sample, then the sample’s result is usually compared vis a vis the census findings. If it is established that the variation between the two results is too significant, then the sample is still not enough to represent the whole population.
References
Altman, D.G. (2004) Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research. Canadian Medical Association Journal
Peto R. (2004). Why do we need some large, simple randomized trials? Statistics in Medicine.
Hamlin, R.P. (2005).The Rise & Fall of the Latin Square in Marketing, A Cautionary Tale European Journal of Marketing.
Shein-chung, C. (2008). Sample size calculation in clinical research.
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