Dear Irene,
Many criteria are used to ascertain sample size such as types of scales used in measurement, availability of population size, use of pilot study results, or the possible scope of errors, and also the use of various analytical techniques. The sample size can be based on;
1) Sample size table
Even tables are developed that help in easily finding out the sample size. Krejcie & Morgan (1970) have developed a table that helps the researcher determine (with 95 percent certainty) the sample size, and as per his table, a sample size of 384 is sufficient for a population size of more than 1000000.
2) Based on types of scales used in the instrument designing:
Based on the type of scale used for measuring variables, sample size can be ascertained. If study primarily uses 7-point likert scale, as per Cochran’s sample size formula for a seven point scale likert, sample size of 118 is deemed sufficient.
3) Based on number of factors to be analyzed:
Literature (MacCallum, Widaman, Zhang, & Hong, 1999) reveals that for factor analysis, adequate sample size is partly determined by the strength of data, i.e. if the data strength is good, even for the smaller sample, results can be accurate and reliable. However, strength of data cannot be ascertained in advance, and thus it is important to have criteria for ascertaining the sample size.
In this regard, Comrey and Lee (1992) offered a rough rating scale for factor analysis and gave rough rating scale for sample size in factor analysis as: 100=poor; 200=fair; 300=good; 500=Very Good; and 1000 or more excellent. They urged the researchers to obtain samples of 500 or more observations whenever factor analysis was being used.
The best method of determining the sample size for Factor Analysis is subject to item ratio, and in this regard, the very common method approved by a number of leading names (David Garson, 2008; Everitt, 1975; Everitt, 1975, Nunnally, 1978, Arrindell & van der Ende, 1985) is called the rule of 10, wherein, it is suggested that there should be at least 10 cases for each item in the instrument being used.
Thus, if we go by this assessment, an overall sample size of around 450 would be deemed sufficient as in totality we deal with around 45 items.
Hope it helps.
Dear Irene,
Many criteria are used to ascertain sample size such as types of scales used in measurement, availability of population size, use of pilot study results, or the possible scope of errors, and also the use of various analytical techniques. The sample size can be based on;
1) Sample size table
Even tables are developed that help in easily finding out the sample size. Krejcie & Morgan (1970) have developed a table that helps the researcher determine (with 95 percent certainty) the sample size, and as per his table, a sample size of 384 is sufficient for a population size of more than 1000000.
2) Based on types of scales used in the instrument designing:
Based on the type of scale used for measuring variables, sample size can be ascertained. If study primarily uses 7-point likert scale, as per Cochran’s sample size formula for a seven point scale likert, sample size of 118 is deemed sufficient.
3) Based on number of factors to be analyzed:
Literature (MacCallum, Widaman, Zhang, & Hong, 1999) reveals that for factor analysis, adequate sample size is partly determined by the strength of data, i.e. if the data strength is good, even for the smaller sample, results can be accurate and reliable. However, strength of data cannot be ascertained in advance, and thus it is important to have criteria for ascertaining the sample size.
In this regard, Comrey and Lee (1992) offered a rough rating scale for factor analysis and gave rough rating scale for sample size in factor analysis as: 100=poor; 200=fair; 300=good; 500=Very Good; and 1000 or more excellent. They urged the researchers to obtain samples of 500 or more observations whenever factor analysis was being used.
The best method of determining the sample size for Factor Analysis is subject to item ratio, and in this regard, the very common method approved by a number of leading names (David Garson, 2008; Everitt, 1975; Everitt, 1975, Nunnally, 1978, Arrindell & van der Ende, 1985) is called the rule of 10, wherein, it is suggested that there should be at least 10 cases for each item in the instrument being used.
Thus, if we go by this assessment, an overall sample size of around 450 would be deemed sufficient as in totality we deal with around 45 items.
Hope it helps.
G-Power is a good tool to calculate sample sizes - and it's free:
http://www.gpower.hhu.de/
well sample size is determined by a variety of factors. first i would say you need to detemine the type of research done and why. is it a quantitative survey or a qualitative type of research, experimental or non-experimental. How many areas are you looking at in the same study, for example in child care, you may be wanting to look at the number of children wanting care by who? where? and each of these entities needing to be meaningfully quantitfied.
you also need to look at the sampling technique itself (probability or non-probability). again depending on the study and its design. in probability studies samples are large and in non probability these can be small, but in big studies you may need to stratify your sample so that you reach saturation at all levels.
you also need to consider the resources. in some instances you need to consider the money to be spent on the activities. you also need to consider how many people can be involved in a particular research and what it takes to recruit them and train them.But whatever size it must be enough to generalize the findings, otherwise you should be able to replicate the study.
Dear Irene
Thanks a lot for such an excellent question.
Mr. Anantjyoti Badgaiyan is very right.
I only want to add few thing into this answer to make it more comprehensive.
Sample size is a basic influence on statistical significance (Thompson, 1992).
Virtually any study can have statistically significant results if a large enough sample size is used. For example, with a standard deviation of 10 and a sample size of 20, a difference of 9.4 between two independent means is necessary for statistical significance at the .05 level in a non-directional test; with a sample size of 100, a difference of only 4.0 is required, and with a sample size of 1000, a difference of only 1.2 is required (Shaver, 1992).
An overly large sample size was not a problem in study. More commonly, a very small sample size might prevent the researcher from obtaining statistically significant results.
References:
1.Thompson, B. (1992, April). The use of statistical significance tests in research: Source criticisms and alternatives. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
2. Shaver, J.P. (1992, April). What statistical significance testing is, and what it is not. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
Hope you find additional help from this.
Hi Irene, You may look at the attached file. Hope this helps you a bit...
Sekaran (2003) wrote:
Roscoe (1975) proposes the following rules of thumb for determining sample size:
1. Sample sizes larger than 30 and less than 500 are appropriate for most research.
2. Where samples are to be broken into subsamples; (male/females, juniors/
seniors, etc.), a minimum sample size of 30 for each category is necessary.
3. In multivariate research (including multiple regression analyses), the sample size should be several times (preferably 10 times or more) as large as the number of variables in the study.
4. For simple experimental research with tight experimental controls (matched pairs, etc.), successful research is possible with samples as small as 10 to 20 in size.
References
Sekaran, U., 2003. Research methods for business: A skill building approach. John Wiley & Sons.
Dear RG colleague,
Determining the sample sizes involve resource and statistical issues. Usually, researchers regard 100 participants as the minimum sample size when the population is large. However, In most studies the sample size is determined effectively by two factors: (1) the nature of data analysis proposed and (2) estimated response rate.
For example, if you plan to use a linear regression a sample size of 50+ 8K is required, where K is the number of predictors. Some researchers believes it is desirable to have at least 10 respondents for each item being tested in a factor analysis, Further, up to 300 responses is not unusual for Likert scale development according to other researchers.
Another method of calculating the required sample size is using the Power and Sample size program (www.power-analysis.com).
Regards,
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