CROSS SECTIONAL STUDY & SAMPLING METHOD

Transcription

1 CROSS SECTIONAL STUDY & SAMPLING METHOD Prof. Dr. Zaleha Md. Isa, BSc(Hons) Clin. Biochemistry; PhD (Public Health), Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur.

2 Classification of study designs 1. Observational Studies 2. Experimental Studies 3. Meta-Analysis

3 Observational Studies 1. Descriptive or case-series 2. Case-control studies (retrospective) 3. Cross-sectional studies, surveys (prevalence) 4. Cohort studies (prospective) 5. Historical cohort studies

4 Cross-sectional studies Also known as surveys, epidemiologic studies, and prevalence studies. Analyze data collected on a group of subjects at one time rather than over a period of time. What is happening? right now. Advantages Suitable to determine disease condition at one time. Suitable to evaluate a diagnosis method. Results can be obtained quickly. Not expensive.

5 Schematic diagram of a crosssectional study design Subjects selected for the study With outcome Without outcome *No direction of inquiry Onset of study Time

6 Cross-sectional studies Disadvantages Provide just a temporary picture of a disease as a consequence, it gives wrong information. Main problem to obtain sufficiently large response.

7 SAMPLING METHOD

8 Populations & samples A major purpose of doing research is to infer, or generalize, from a sample to a larger population. Process of inference is accomplished by using statistical methods based on probability. Population a large set or collection of items that have something in common. Sample a subset of the population, selected in such a way that it is representative of the larger population.

9 Reasons for sampling 1. Samples can be studied more quickly than populations. 2. A study of a sample is less expensive than a study of an entire population. 3. A study of an entire population is impossible in most situations. 4. Sample results are often more accurate than results based on a population. 5. If samples are properly selected, probability methods can be used to estimate the error in the resulting statistics. 6. Samples can be selected to reduce heterogeneity.

10 Bigger does not always mean better Investigators must plan the sample size appropriate for their study prior to beginning research determining the power of a study.

11 Methods of sampling The best way to ensure that a sample will lead to reliable and valid inferences is to use probability samples. 1. Probability sampling methods 2. Non-probability sampling methods

12 Probability sampling methods The probability of being included in the sample is known for each subject in the population. 1. Simple Random Sampling 2. Systematic Sampling 3. Stratified Sampling 4. Cluster Sampling 5. Multi-stage sampling

13 Non-probability sampling methods The probability that a subject is selected is unknown. 1. Convenience Sampling 2. Quota Sampling

14 Simple random sampling Every subject has an equal probability of being selected for the study. Suitable for homogenous population. Use list of ID numbers sampling frame. Method: 1. Subjects in population are given numbers. 2. Samples are chosen by voting (ballot). 3. Table of random numbers can also be used eg. Fisher random numbers or computer-generated list of random numbers.

15 Random number

16 Systematic sampling Suitable for homogenous population. It is used used for large population or to eliminate clustering. Method: 1. Individuals in the population are given numbers. 2. Sample size is determined. 3. Interval, k is determined by dividing the number of items in the sampling frame by the desired sample size: k = total population / total sample 4. The first sample is chosen randomly from sample number 1 to number k. 5. The subsequent sample is chosen by adding the number of the first sample with the interval. 6. The process is carried out continuously until you obtain the required sample size.

17 Systematic sampling Systematic sampling should not be used when a cyclic repetition is inherent in the sampling frame, eg. selecting months of the year in a study of the frequency of different types of accidents.

18 Stratified sampling The population is first divided into relevant strata (subgroups), and a random sample is then selected from each stratum. The sampling is carried out among the heterogenous population. Commonly used strata in medicine: Age Gender Stage of disease Duration of disease

19 Cluster sampling The sampling is carried out among homogenous population that is widely distributed. A two-stage process in which the population is divided into clusters and a subset of the clusters is randomly selected. Then from each cluster chosen, all its members will become subjects. Commonly based on geographic areas or districts, used more often in epidemiologic research than in clinical studies.

20 Multi-stage sampling The sampling is carried out among a very large and widely distributed homogenous population. Method: 1. The population is divided into its geographical area, i.e. from the largest area to the smallest area. 2. At each level, the samples under it are selected randomly based on the sample size required. 3. As soon as the smallest level is achieved, all members in the unit become samples. e.g. Federal -State-District-Subdistrict-Village

21 Convenience sampling Convenience sampling (sometimes known as grab, accidental sampling or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. A sample population selected because it is readily available and convenient, as researchers are drawing on relationships or networks to which they have easy access.

22 Quota sampling Samples are chosen at a particular time with a certain quota/sub-group, such as sex, age, occupation, etc. Then judgement is used to select the subjects or units from each quota/sub-group based on a specified proportion. This second step makes the technique non-probability sampling. This sampling has many weaknesses because it depends on the situation at the time the sampling is carried out.

23 Random Assignment Used in experimental studies. Subjects are first selected for inclusion in the study on the basis of appropriate criteria, then randomly assigned to different treatment modalities. It helps to ensure that the groups receiving the different treatment modalities are as similar as possible. Thus, any differences in outcome at the conclusion of the study are more likely to be the result of differences in treatments than differences in compositions of the groups.

24 THANK YOU 1. Dawson-Saunders, B. & Trapp, R.G Basic and Clinical Biostatistics. Prentice-Hall International Inc. London. 2. Md. Idris Mohd. Nor Asas Statistik dan Penyelidikan, Dewan Bahasa dan Pustaka, Kuala Lumpur. 3. Elston, R.C. & Johnson, W.D Essential of Biostatistics, F.A. Davis Company, Philadelphia.