How do you determine the effective sample size?

How do you determine the effective sample size?

A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000.

What factors influence sample size?

Sample size estimation

• The sample size is the number of participants or specimen required in a study and its estimation is important for both in vivo and in vitro studies.
• The factors affecting sample sizes are study design, method of sampling, and outcome measures – effect size, standard deviation, study power, and significance level.

What is a good sample size in quantitative research?

If the research has a relational survey design, the sample size should not be less than 30. Causal-comparative and experimental studies require more than 50 samples. In survey research, 100 samples should be identified for each major sub-group in the population and between 20 to 50 samples for each minor sub-group.

Does sample size affect correlation coefficient?

It depends on the size of your sample. All other things being equal, the larger the sample, the more stable (reliable) the obtained correlation. Correlations obtained with small samples are quite unreliable.

What if correlation coefficient is greater than 1?

What Is the Correlation Coefficient? A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement. A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation.

Can a residual be greater than 1?

Its value is never greater than 1.0, but it can be negative when you fit the wrong model (or wrong constraints) so the SSe (sum-of-squares of residuals) is greater than SSt (sum of squares of the difference between actual and mean Y values).

What does it mean when a residual is positive?

If you have a positive value for residual, it means the actual value was MORE than the predicted value. The person actually did better than you predicted. Under the line, you OVER-predicted, so you have a negative residual. Above the line, you UNDER-predicted, so you have a positive residual.

What does a residual of 0 mean?

A residual is the vertical distance between a data point and the regression line. They are positive if they are above the regression line and negative if they are below the regression line. If the regression line actually passes through the point, the residual at that point is zero.