Universe Population: Refers to anything regarded as a whole.
It is the total from which sample is taken. It can be as small as hundreds (department), or thousands (district) or millions (city or country) or even billions, trillions or can go to infinity.
Random sampling is the way of sampling by which all members of the population has the same chance to appear in the sample
Random sample is a representative sample.
Random Sample types: Simple, Systematic, Stratified, Cluster, Multi-stage.
Simple random sample: A simple random sample is actually one of the least used techniques. In theory, it’s easy to understand. However, in practice it’s tough to perform. Is collected by using lottery method or by using simple random tables.
Lottery method: Technically, a simple random sample is a set of n objects in a population of N objects where all possible samples are equally likely to happen. Put 100 numbered bingo balls into a bowl (this is the population N) Select 10 balls from the bowl without looking (this is the sample n).
Systematic Random Sample: Is collected by fixing the random selection according to certain sequence as for example selecting the 1st, 3rd, 5th, 7th members, etc. The first member of the sample is chosen randomly.
Two important items: Starting number & Interval.
Starting number: The researcher selects an integer that must be less than the total number of individuals in the population
This integer will correspond to the first subject.
Interval: The researcher picks another integer which will serve as the constant difference between any two consecutive numbers in the progression. The integer is typically selected so that the researcher obtains the correct sample size. If the researcher has a population total of 100 individuals and needs 12 subjects, He first picks his starting number randomly, let us say it is number 5 Then the researcher picks his interval = 100 / 12 = 8 The members of his sample will be individuals 5, 13, 21, 29, 37, 45, 53, 61, 69, 77, 85, 93
Stratified Random Sample: Sample is collected randomly from certain strata as males, females, manual workers, skilled workers, high class, low class, literate, illiterate, then a random sample is drawn from each stratum.
Cluster Sample: Total population is divided into groups or clusters.
Each cluster to be as homogenous as possible. Examples of clusters are: household, farmers in a village, workers in same factory department, etc. Then all population in each cluster are to be included in the sample.
Multistage Random Sample: Is collected randomly from different levels or stages as for example sample from the Governorate, sample from the district, sample from the town, etc.
Sample size: Deciding on sample size depend on: Availability of resources, Prevalence of the condition under study and Number of variables required to be studied.
To reduce the non-differential chance error, try to make sample size as big as possible. There is a direct relationship between variability and sample size. As variability of the population increase, sample size is required to increase and vice versa. Variability is demonstrated by the coefficient of variation.
Bias means that not all variables are equally represented (over or under representation).
Sampling Bias: Distortions that occur when some members of a population are systematically excluded from the sample selection process. For example, if interviews are conducted over the phone, only individuals with telephones will be in the sample.
Selection Bias: Error due to systematic differences in the characteristics of those who are selected for a study and those who are not. For example, if Bill Gates is included in the study sample, it will obviously reveal very high income of the sample.
Social desirability Bias: The tendency for respondents to give answers that are socially desirable or acceptable, that may not be accurate.
Dropouts & defaulters: Attrition refers to the rate at which participants drop out of a longitudinal study. It can introduce bias and threaten the validity of the study.