Today i will teach you types of sampling methods

Random Sampling

A random sample is a sample in which all members of the population have an equal chance of being selected.

Random samples are selected by using chance methods or random numbers. One such method is to number each subject in the population. Then place numbered cards in a bowl, mix them thoroughly, and select as many cards as needed. The subjects whose numbers are selected constitute the sample. Since it is difficult to mix the cards thoroughly, there is a chance of obtaining a biased sample. For this reason, statisticians use another method of obtaining numbers. They generate random numbers with a computer or calculator. Before the invention of computers, random numbers were obtained from tables.

Some five-digit random numbers are shown in Table D in Appendix A. A section of Table D is shown on page 13. To select a random sample of, say, 15 subjects out of 85 subjects, it is necessary to number each subject from 01 to 85. Then select a starting number by closing your eyes and placing your finger on a number in the table. (Although this may sound somewhat unusual, it enables us to find a starting number at random.) In this case, suppose your finger landed on the number 88948 in the fourth column, the fifth number down from the top. Since you only need two-digit numbers, you can use the last two digits of each of these numbers. The first random number then is 48. Then proceed down until you have selected 15 different numbers between and including 01 and 85. When you reach the bottom of the column, go to the top of the next column. If you select a number 00 or a number greater than 85 or a duplicate number, just omit it.

In our example, we use the numbers (which correspond to the subjects) 48, 43, 44, 19, 07, 27, 57, 24, 68, and so on. Use Table D in the Appendix to get all the random numbers.

Systematic Sampling

A systematic sample is a sample obtained by selecting every k^th member of the population where k is a counting number.

Researchers obtain systematic samples by numbering each subject of the population and then selecting every kth subject. For example, suppose there were 2000 subjects in the population and a sample of 50 subjects was needed. Since 2000 ÷ 50 = 40, then k = 40, and every 40th subject would be selected; however, the first subject (numbered between 1 and 40) would be selected at random. Suppose subject 12 were the first subject selected; then the sample would consist of the subjects whose numbers were 12, 52, 92, etc., until 50 subjects were obtained. When using systematic sampling, you must be careful about how the subjects in the population are numbered. If subjects were arranged in a manner such as wife, husband, wife, husband, and every 40th subject were selected, the sample would consist of all husbands. Numbering is not always necessary. For example, a researcher may select every 10th item from an assembly line to test for defects.Page 13 SPEAKING OF STATISTICSThe Worst Day for Weight Loss

©Jacobs Stock Photography/Getty Images RF

Many overweight people have difficulty losing weight. Prevention magazine reported that researchers from Washington University School of Medicine studied the diets of 48 adult weight loss participants. They used food diaries, exercise monitors, and weigh-ins. They found that the participants ate an average of 236 more calories on Saturdays than they did on the other weekdays. This would amount to a weight gain of 9 pounds per year. So if you are watching your diet, be careful on Saturdays.

Are the statistics reported in this study descriptive or inferential in nature? What type of variables are used here?TABLE DRandom Numbers

51455	02154	06955	88858	02158	76904	28864	95504	68047	41196	88582	99062	21984	67932
06512	07836	88456	36313	30879	51323	76451	25578	15986	50845	57015	53684	57054	93261
71308	35028	28065	74995	03251	27050	31692	12910	14886	85820	42664	68830	57939	34421
60035	97320	62543	61404	94367	07080	66112	56180	15813	15978	63578	13365	60115	99411
64072	76075	91393	88948	99244	60809	10784	36380	5721	24481	86978	74102	49979	28572
14914	85608	96871	74743	73692	53664	67727	21440	13326	98590	93405	63839	65974	05294
93723	60571	17559	96844	88678	89256	75120	62384	77414	24023	82121	01796	03907	35061
86656	43736	62752	53819	81674	43490	07850	61439	52300	55063	50728	54652	63307	83597
31286	27544	44129	51107	53727	65479	09688	57355	20426	44527	36896	09654	63066	92393
95519	78485	20269	64027	53229	59060	99269	12140	97864	31064	73933	37369	94656	57645
78019	75498	79017	22157	22893	88109	57998	02582	34259	11405	97788	37718	64071	66345
45487	22433	62809	98924	96769	24955	60283	16837	02070	22051	91191	40000	36480	07822
64769	25684	33490	25168	34405	58272	90124	92954	43663	39556	40269	69189	68272	60753
00464	62924	83514	97860	98982	84484	18856	35260	22370	22751	89716	33377	97720	78982
73714	36622	04866	00885	34845	26118	47003	28924	98813	45981	82469	84867	50443	00641
84032	71228	72682	40618	69303	58466	03438	67873	87487	33285	19463	02872	36786	28418
70609	51795	47988	49658	29651	93852	27921	16258	28666	41922	33353	38131	64115	39541
37209	94421	49043	11876	43528	93624	55263	29863	67709	39952	50512	93074	66938	09515
80632	65999	34771	06797	02318	74725	10841	96571	12052	41478	50020	59066	30860	96357

Systematic sampling has the advantage of selecting subjects throughout an ordered population. This sampling method is fast and convenient if the population can be easily numbered.

Stratified Sampling

A stratified sample is a sample obtained by dividing the population into subgroups or strata according to some characteristic relevant to the study. (There can be several subgroups.) Then subjects are selected at random from each subgroup.Page 14

Samples within the strata should be randomly selected. For example, suppose the president of a two-year college wants to learn how students feel about a certain issue. Furthermore, the president wishes to see if the opinions of first-year students differ from those of second-year students. The president will randomly select students from each subgroup to use in the sample.

Cluster Sampling

A cluster sample is obtained by dividing the population into sections or clusters and then selecting one or more clusters at random and using all members in the cluster(s) as the members of the sample.

Here the population is divided into groups or clusters by some means such as geographic area or schools in a large school district. Then the researcher randomly selects some of these clusters and uses all members of the selected clusters as the subjects of the samples. Suppose a researcher wishes to survey apartment dwellers in a large city. If there are 10 apartment buildings in the city, the researcher can select at random 2 buildings from the 10 and interview all the residents of these buildings. Cluster sampling is used when the population is large or when it involves subjects residing in a large geographic area. For example, if one wanted to do a study involving the patients in the hospitals in New York City, it would be very costly and time-consuming to try to obtain a random sample of patients since they would be spread over a large area. Instead, a few hospitals could be selected at random, and the patients in these hospitals would be interviewed in a cluster. See Figure 1–3.FIGURE 1–3Sampling Methods

The main difference between stratified sampling and cluster sampling is that although in both types of sampling the population is divided into groups, the subjects in the groups for stratified sampling are more or less homogeneous, that is, they have similar characteristics, while the subjects in the clusters form “miniature populations.” That is, they vary in characteristics as does the larger population. For example, if a researcher wanted to use the freshman class at a university as the population, he or she might use a class of students in a freshman orientation class as a cluster sample. If the researcher were using a stratified sample, she or he would need to divide the students of the freshman class into groups according to their major field, gender, age, etc., or other samples from each group.

Cluster samples save the researcher time and money, but the researcher must be aware that sometimes a cluster does not represent the population.

The four basic sampling methods are summarized in Table 1–3.TABLE 1–3Summary of Sampling Methods

Random	Subjects are selected by random numbers.
Systematic	Subjects are selected by using every kth number after the first subject is randomly selected from 1 through k.
Stratified	Subjects are selected by dividing up the population into subgroups (strata), and subjects are randomly selected within subgroups.
Cluster	Subjects are selected by using an intact subgroup that is representative of the population.