Sampling Techniques, Data Collection and tabulation in the field of Social Science
- 1. Sampling Techniques, Data Collection and tabulation in the field of Social Science Vipan Kumar Rampal
- 2. Sample It is a unit that is selected from population Represents the whole population Purpose to draw the inference A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” Sampling Frame Listing of population from which a sample is chose Sampling is the process of selecting observations (a sample) to provide an adequate description and inferences of the population. Purpose of Sampling To gather data about the population in order to make an inference that can be generalized to the population
- 3. Sampling Techniques Non-probability sampling and probability sampling Under non- probability sampling Convenience sampling the process of including whoever happens to be available at the time …called “accidental” or “haphazard” sampling Purposive sampling the process whereby the researcher selects a sample based on experience or knowledge of the group to be sampled …called “judgment” sampling Quota sampling the process whereby a researcher gathers data from individuals possessing identified characteristics and quotas
- 4. Under probability sampling (Simple random sampling, Systematic sampling, Stratified sampling and Cluster sampling) 1. Simple Random sampling Selecting subjects so that all members of a population have an equal and independent chance of being selected Selection process Identify and define the population Determine the desired sample size List all members of the population Assign all members on the list a consecutive number Select an arbitrary starting point from a table of random numbers and read the appropriate number of digits
- 5. 2. Systematic sampling Selecting every Kth subject from a list of the members of the population Selection process Identify and define the population Determine the desired sample size Obtain a list of the population Determine what K is equal to by dividing the size of the population by the desired sample size Start at some random place in the population list Take every Kth individual on the list
- 6. 3. Stratified random sampling The population is divided into two or more groups called strata, according to some criterion, such as geographic location, grade level, age, or income, and subsamples are randomly selected from each strata Selection process Identify and define the population Determine the desired sample size Identify the variable and subgroups (i.e., strata) for which you want to guarantee appropriate representation Classify all members of the population as members of one of the identified subgroup The following questions for stratified random sampling How to form strata? How should items be selected from each stratum? How many items be selected from each stratum?
- 7. 4. Cluster sampling The process of randomly selecting intact groups, not individuals, within the defined population sharing similar characteristics Clusters are locations within which an intact group of members of the population can be found Selection process Identify and define the population Determine the desired sample size Identify and define a logical cluster List all clusters that make up the population of clusters Estimate the average number of population members per cluster Determine the number of clusters needed by dividing the sample size by the estimated size of a cluster Randomly select the needed numbers of clusters Include in the study all individuals in each selected cluster
- 8. Sample Size According to Uma Sekaran in Research Method for Business 4th Edition, Roscoe (1975) proposed the rules of thumb for determining sample size where sample size larger than 30 and less than 500 are appropriate for most research, and the minimum size of sample should be 30% of the population. The size of the sample depends on a number of factors and the researchers have to give the statistically information before they can get an answer. For example, these information like (confidence level, standard deviation and population size) to determine the sample size
- 9. Methods of data Collection What is Data Collection It is the process by which the researcher collects the information needed to answer the research problem The task of data collection begins after a research problem has been defined and research design chalked out There are two of data Primary Data Primary data are those which are collected for the first time and are original in character. Secondary Data Secondary data are those which have already been collected by someone else and which have through some statistical analysis Primary Data may be collected through: Observation Interviews Questionnaires Schedules
- 10. Observation method is a method under which data from the field is collected with the help of observation by the observer or by personally going to the field. In the words “Observation may be defined as systematic viewing, coupled with consideration of seen phenomenon Structured Observation When the observation is characterized by a careful definition of the units to be observed, the style of recording the observed information, standardized conditions of observation and the selection of related data of observation. Unstructured Observation When it takes place without the above characteristics. Uncontrolled Observation When the observation takes place in natural condition i.e., uncontrolled observation. It is done to get spontaneous picture of life and persons. Controlled Observation When observation takes place according to pre arranged plans, with experimental procedure then it is controlled observation generally done in laboratory under controlled condition.
- 11. The Interview Method of collecting data involves presentation of oral-verbal stimuli and reply in terms of oral- verbal responses. Interviewer asks questions (which are aimed to get information required for study) to respondent. Structured Interviews : In this case, a set of pre-decided questions are there. Unstructured Interviews : In this case, we don’t follow a system of pre-determined questions. Focused Interviews : Attention is focused on the given experience of the respondent and its possible effects
- 12. A Questionnaire is sent ( by post or by mail ) to the persons concerned with a request to answer the questions and return the Questionnaire. A Questionnaire consists of a number of questions printed in a definite order on a form Open-ended questions This gives the respondents the ability to respond in their own words. Close-ended or fixed alternative questions This allows the respondents to choose one of the given alternatives. Types:- Dichotomous questions and Multiple Questions Good Questionnaire has following points Should be short and simple Follow a sequence of questions from easy to difficult one Technical terms should be avoided Should provide adequate space for answers in questionnaire Directions regarding the filling of questionnaire should be given Physical Appearance – Quality of paper, Color Sequence must be clear
- 13. Schedules Very similar to Questionnaire method The main difference is that a schedule is filled by the enumerator who is specially appointed for the purpose. Enumerator goes to the respondents, asks them the questions from the Questionnaire in the order listed, and records the responses in the space provided. Enumerator must be trained in administering the schedule
- 14. PROCESSING OF DATA The collected data in research is processed and analyzed to come to some conclusions or to verify the hypothesis made. Processing of data is important as it makes further analysis of data easier and efficient. Processing of data technically means 1.Editing of the data 2.Coding of data 3.Classification of data 4.Tabulation of data.
- 15. EDITING: Data editing is a process by which collected data is examined to detect any errors or omissions and further these are corrected as much as possible before proceeding further. Editing is of two types: 1.Field Editing This is a type of editing that relates to abbreviated or illegible written form of gathered data. Such editing is more effective when done on same day or the very next day after the interview. The investigator must not jump to conclusion while doing field editing. 2.Central Editing Such type of editing relates to the time when all data collection process has been completed. Here a single or common editor corrects the errors like entry in the wrong place, entry in wrong unit etc. As a rule all the wrong answers should be dropped from the final results.
- 16. CODING: Classification of responses may be done on the basis of one or more common concepts. In coding a particular numeral or symbol is assigned to the answers in order to put the responses in some definite categories or classes. The classes of responses determined by the researcher should be appropriate and suitable to the study. Coding enables efficient and effective analysis as the responses are categorized into meaningful classes. Coding decisions are considered while developing or designing the questionnaire or any other data collection tool. Coding can be done manually or through computer.
- 17. CLASSIFICATION: Classification of the data implies that the collected raw data is categorized into common group having common feature. Data having common characteristics are placed in a common group. The entire data collected is categorized into various groups or classes, which convey a meaning to the researcher. Classification is done in two ways: 1.Classification according to attributes. 2.Classification according to the class intervals.
- 18. TABULATION: The mass of data collected has to be arranged in some kind of concise and logical order. Tabulation summarizes the raw data and displays data in form of some statistical tables. Tabulation is an orderly arrangement of data in rows and columns. OBJECTIVE OF TABULATION: 1.Conserves space & minimizes explanation and descriptive statements. 2.Facilitates process of comparison and summarization. 3.Facilitates detection of errors and omissions. 4.Establish the basis of various statistical computations.
- 19. ANALYSIS OF DATA The important statistical measures that are used to analyze the research or the survey are: 1.Measures of central tendency(mean, median & mode) 2.Measures of dispersion(standard deviation, range, mean deviation) 3.Measures of asymmetry(skew ness) 4.Measures of relationship etc.( correlation and regression) 5.Association in case of attributes. 6.Time series Analysis
- 20. TESTING THE HYPOTHESIS Several factor are considered into the determination of the appropriate statistical technique to use when conducting a hypothesis tests. The most important are as: 1.The type of data being measured. 2.The purpose or the objective of the statistical inference. Hypothesis can be tested by various techniques. The hypothesis testing techniques are divided into two broad categories: 1.Parametric Tests. 2.Non- Parametric Tests.
- 21. PARAMETRIC TESTS: These tests depends upon assumptions typically that the population(s) from which data are randomly sampled have a normal distribution. Types of parametric tests are: 1.t- test 2.z- test 3.F- test 4.2- test NON PARAMETRIC TESTS The various types of Non Parametric Tests are: 1.Wilcox on Signed Rank Test ( for comparing two population) 2.Kolmogorov Smirnov Test( to test whether or not the sample of data is consistent with a specified distribution function) 3.Runs Tests (in studies where measurements are made according to some well defined ordering, either in time or space, a frequent question is whether or not the average value of the measurement is different points in the sequence. This test provides a means of testing this. 4.Sign Test (this is single sample test that can be used instead of the single sample t- test or paired t- test.
- 22. INTERPRETATION: Interpretation is the relationship amongst the collected data, with analysis. Interpretation looks beyond the data of the research and includes researches, theory and hypothesis. Interpretation in a way act as a tool to explain the observations of the researcher during the research period and it acts as a guide for future researches. WHY Interpretation? -the researcher understands the abstract principle underlying the findings. -Interpretation links up the findings with those of other similar studies. -The researcher is able to make others understand the real importance of his research findings.