NTA (UGC) NET Research Methodology and Aptitude: Sampling and Design

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The process of sampling includes selecting units e. g. people from the population of interest so that by studying the sample we may elaborate our results back to the population from which they were chosen. A response is a particular measurement value that a sampling unit supplies. If you measure the overall population and find out a value like a mean or average, it is called parameter of the population. The distribution of an infinite number of samples of the same size as the sample in your study is called sampling distribution.

In the terms of sampling, the standard error is called sampling error. Sampling error gives us some idea of the precision of our statistical estimate. A low sampling error means that we had relatively less variability or range in the sampling distribution. How do we calculate sampling error? on the standard deviation of our sample. The greater the sample standard deviation, the greater the standard error the sampling error. The standard error is also related to the sample size. The greater your sample size, the smaller the standard error. Because the greater the sample size, the closer your sample is to the actual population itself. If you take a sample that consists of the entire population you actually have no sampling error as you don't have a sample, you have the entire population. The mean you estimate is the parameter.

Probability Sampling Method

Any method of sampling that uses some form of random selection such as picking a name out of a hat is known as probability sampling.

Simple random sampling is the simplest form sampling. Simple random sampling is simple to calculate and is very easy to explain to others. As simple random sampling is a fair way to select a sample, it is reasonable to fundamentalize the results from the sample back to the population. Simple random sampling is not the most statistically effective method of sampling and you may, just because of the luck of the draw, not get outstanding representation of subgroups in a population.

Stratified Random Sampling, also known as proportional or quota random sampling, consists bifurcating your population into homogeneous subgroups and then choosing a simple random sample in each subgroup. It ensure that you will be able to represent not only the entire population, but also key subgroups of the population particularly small minority groups. Second, stratified random sampling will fundamentally have more statistical precision than simple random sampling. This is true only if the strata or groups are homogeneous in nature.

The major difficulty associate with the random sampling methods is when we have to sample a population that's disbursed around a huge geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled. It is for precisely this problem that cluster or area random sampling was formed. In cluster sampling, we follow the steps stated below:

  1. divide population into clusters (Basically along geographic boundaries).

  2. randomly sample clusters.

  3. measure all units within sampled clusters.

Non-Probability Sampling

Non-probability and probability sampling are very different, The difference is that non-probability sampling does not consist random selection and probability sampling does. We can divide nonprobability sampling methods into two broad types: Accidental or purposive. In accidental sampling, sample is chosen accidently and we have no evidence that they are representative of the populations we're interested in fundamentalizing to and in many cases we would clearly suspect that they are not. e. g. College students in some psychological survey. In purposive sampling, we sample with a purpose in mind. We fundamentally would have one or more particular definite groups we are looking for. For example, have you ever run into people in a mall or on the street who are carrying a clipboard and who are stopping various people and asking if they could interview them? Most likely they are conducting a purposive sample. Purposive sampling can be very desired for situations where you need to reach a targeted sample quickly and where sampling for proportionality is not the primary concern. With a purposive sample, you are likely to get the opinions of your target population, but you are also likely to overweight subgroups in your population that are more approachable.

Quota sampling. Quota sampling is a purposive sampling, Where you choose people non-randomly according to some fixed quota. There are two types of quota sampling: Proportional and non proportional. In proportional quota sampling you want to represent the major features of the population by sampling a proportional amount of each. e. g. Choosing 40% females from a population of say 1000.

Snowball sampling. Snowball sampling includes identifying someone who meets the barometer for inclusion in your research study. You then ask them to recommend others who they may know who also fulfill the criteria.

Research Design

Research design acts like a glue that sustain the research project together. A design is used to structure the research, to show how all of the major parts of the research project the samples or groups, measures, treatments or programs and methods of assignment work together to try to address the central research questions. Design can be either experimental or non-experimental.

The last part of the research is data analysis. In most of the social researches, the data that is analyse comprises into three major steps, Which are stated below:

  • Cleaning and organizing the data for analysis (Data Preparation).
  • Describing the data (Descriptive Statistics).
  • Testing Hypotheses and Models (Inferential Statistics).

Data Preparation

It comprises checking or logging the data in; checking the relevancy of data; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.

Types of Statistics

Descriptive Statistics are used to elaborate the fundamental features of the data in a study. They givesimple summaries regarding the sample and the measures. Along with simple graphics analysis, they become the pillars of virtually quantitative analysis of data. With descriptive statistics you are merely describing what it is, what the data represents.

Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. We use inferential statistics and try to refer from the sample data what the population thinks. We sometimes use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus we use inferential statistics to make inferences from our data to more fundamental conditions; we use descriptive statistics simply to elaborate exactly is in our data.

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