Chapter 5 Data Collection Methods (PPT slides)

Find here the PPT for Chapter 5 Data Dollection Methods

4.7 The importance of High response rate and the actual sample size required

The importance of a high response rate

The most important aspect of a probability sample is that it represents the population. A perfect representative sample is one that exactly represents the population from which it is taken. You, therefore, need to obtain as high a response rate as possible to ensure that your sample is representative. This requires you to determine the  minimum adjusted sample size , and finally the actual sample size required.

This note gives you a brief discussion on how to compute the adjusted sample size by considering the non responses , and also the  actual sample size you require for your research.
click here for the note. 

4.6 What sample size do I need for my research?

 What sample size do I need for my research?

 Perhaps the most frequently asked question concerning the determination of sample size is    “What should be my sample size?” The article by Isreal (2013) provides you a summary of the most important points that one needs top consider to answer such a question.

In fact ,you need to consider a number of factors to determine the appropriate sample size. These include , the purpose of the study, population size, the level of precision, the level of confidence or risk, and the degree of variability in the attributes being measured. For full discussion of these issues, Read the article below  DETERMINING SAMPLE SIZE. It discusses all these factors and how to use published tables, and applying formulas to calculate a sample size.

Article: Determining the sample size 

More precision, more sample size

The table below provides sample sizes for different sizes of population at a 95% confidence level and margin of error ranging from 5% to 1%. Note that the smaller the margin of error ( which means when we need more precise measurement of the variable ) , we need to have more sample size. 

Table: sample sizes for different margin of errors

4.5 Impact of factors to choose sampling techniques

Impact of factors to choose sampling techniques

The impacts of factors to choose probabilistic and non-probabilistic sampling techniques are tabulated below.

Table: 1  

Table: 2 

4.4 Comparison of probability sampling designs

Comparison of probability sampling designs

The table below depicts the comparison of probability sampling.

Table: 

Furthermore, a detail comparison between the cluster and stratified sampling is presented in the table below.

Table: 

4.3 Most frequently used sampling techniques

Most frequently used sampling techniques

The diagram below shows the most commonly used sampling techniques.

Diagram: 

The decision trees below helps you to make a decision about which  type of sampling techniques to use for your research.  

Decision Tree 1: 

Decision Tree 2:  

4.2 Characteristics of a good sample: accuracy and precision

Characteristics of a good sample: accuracy and precision

  

Two conditions are appropriate for a census study. These are when a census is (1) feasible     (meaning when the population is small),  and (2) necessary (meaning  when the elements are quite different from each other.  However , there are several compelling reasons for sampling, including (1) lower cost, (2) greater accuracy of results, (3) greater speed of data collection, and (4) availability of population elements.

What is a good sample then? The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represent. In measurement terms, the sample must be valid. Validity of a sample depends on two considerations:  accuracy and precision.

Accuracy is the degree to which bias is absent from the sample. When the sample is drawn properly, the measure of behaviour, attitudes, or knowledge (or the measurement variables) of some sample elements will be less than (thus, underestimate) the measure of those same variables drawn from the population. Also, the measure of the behavior, attitudes, or knowledge of other sample elements will be more than the population values (thus, overestimate them). Variations in these sample values offset each other, resulting in a sample value that is close to the population value. For these offsetting effects to occur, however, there must be enough elements in the sample, and they must be drawn in a way that favors neither overestimation nor underestimation.

Therefore , an accurate (unbiased) sample is one in which the underestimators offset the overestimators. Systematic variance has been defined as “the variation in measures due to some known or unknown influences that ‘cause’ the scores to lean in one direction more than another.”

Precision is measured by the standard error of estimate, a type of standard deviation measurement; the smaller the standard error of estimate, the higher is the precision of the sample. The ideal sample design produces a small standard error of estimate. However, not all types of sample design provide estimates of precision, and samples of the same size can produce different amounts of error.