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This essay explores various non-probability sampling techniques within the context of social research, focusing on their merits, demerits, and practical applications. Non-probability sampling, characterized by the absence of random selection, relies on the researcher's judgment for sample selection, making it integral in studies where random sampling is not feasible. Through the analysis of convenience sampling, quota sampling, purposive sampling, snowball sampling, and respondent-driven sampling, we aim to elucidate the effectiveness and limitations of these methods in capturing diverse societal phenomena.
Non-Probability Sampling it is a sampling process where there are no particular criteria for evaluating the probability for including each population in the sample.
Non-probability sampling is also known as deliberate sampling, judgement sampling, etc. In this type of sampling the researcher chooses the items in the sample intentionally and those items in the sample are considered to be predominant.
It is a non-probability sampling method where the researcher chooses samples from such a population that are very conveniently and readily available to him/her.
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Here the researcher chooses the data which is readily available at any place may be parks, malls, etc. This method does not have any kind of criteria for selection of the data. It is also known as accidental sampling.
Merits:
Demerits:
Example: A chocolate company wants to study the response of its new product in the market within a day, after the new product has been launched in the market.
The interviewer goes to malls, shops, etc. to get response from the consumer.
It’s a sampling technique where samples are collected from a homogenous group. In this method, the population is divided into different groups or strata according to the various categories of the population. It is very similar to stratified random sampling but there is no random sampling done in this method. It can be divided into two groups one is controlled quota sampling and the other one is uncontrolled quota sampling.
Here, controlled quota sampling includes specific limitations or restrictions to limit the choice of samples taken by the researcher. Whereas, in uncontrolled quota sampling there is no such limitation. The researcher can choose samples according to his/her convenience.
Merits:
Demerits:
Example: A researcher wants to study the socio-economic condition of the petty traders that are affected by Covid-19. The petty traders are assigned/ categorised into : age group like 20-30, 30-40 & 40 plus and further categorised according to cities and towns where they are trading.
It is also known as judgement sampling or selective sampling or subjective sampling. Here, while choosing the sample the researcher depends on his/her judgement.
Merits:
Demerits:
Examples: A researcher conducting a study on school students who choose college to study which are outside their native places. The interviewer collects data by going to a school based on his/her purpose of research.
It is also known as chain-referral sampling. In this process samples have a unique feature and that are hard to find. Therefore, the researcher assigns initial subject who gives data and further that initial respondent recruit other respondents for the research or study.
Merits:
Demerits:
Example: A research on Non Heterosexual Women, as there is no accurate data on non heterosexual women. These people do not confront about their sexual orientation due to the society and the pressure of the pre-existing framework of sexuality. Only people belonging to that category would know each other. Chain- referral research can be done here on this case as one respondent refers to the other respondent and in this manner we reach the potential respondent.
This is similar to Snow-ball sampling, as it tries to study the hidden traits of a population. Here, in this method the researcher recruits one set of people from the target population who act as seed of the research. Then these group of people are given some recruit coupons to use while they recruit other people from the target population. This process is carried out until the research comes to an end.
Merits:
Demerits:
Examples: Study on the mental condition of the drug abusers in a particular state.
To understand the relationship between various socio-economic factors, hypothesis testing using the Chi-Square test and T-test was conducted. For instance, examining the association between social category and income category revealed a significant relationship, suggesting that social stratification affects income levels. Similarly, the analysis between agricultural land ownership and family size indicated a correlation, pointing towards the influence of land ownership on family structure in rural settings.
For calculation of the value of Chi- Square calculated, the formula used is:
χ2= sum [(observed frequency−expected frequency )^2)/(𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦)] Now,Chi- square, χ2 value = 21.63751
Degrees of freedom (df) = (n1-1)*(n2-1)
Where, n1 is number of rows
n2 is number of columns
df = (4-1)*(5-1)
df = 3*4 df = 12
Occupational Category
For calculation of the value of Chi- Square calculated, the formula used is:
χ2= sum [(observed frequency−expected frequency )^2)/(𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦)]
Now,Chi- square, χ2 value = 7.87931
Degrees of freedom (df) = (n1-1)*(n2-1)
Where, n1 is number of rows
n2 is number of columns
df = (3-1)*(4-1)
df = 2* 3
df = 6
Decision for Social Category:
Degree of freedom at 5% level of significance is 12
The tabulated value at 5% level of significance for social category –
tabulated with 12 df = 21.03
χ2 calulated value = 21.63751
The calculated χ2 value> tabulated χ2 value with df,0.05.
Therefore, we can say that null hypothesis is rejected at 5% level of significance.
Thus, the alternative hypothesis is accepted at 5% level of significance.
Hence, the social category is associated with the income category, any change in the social category will affect or have any impact on the income of an individual or a household.
Decision for Occupational Category:
Degree of freedom at 5% level of significance is 6.
The tabulated value at 5% level of significance for occupational category tabulated with 6 df = 12.58
χ2 calulated value = 7.87931
The calculated χ2 value< tabulated χ2 value with df,0.05.
Therefore, we can say that null hypothesis is accepted at 5% level of significance.
Hence, the occupational structure of the household is not association with the change in the standard of living during the last five years. If there is any change in the occupational structure of a household it will not affect the standard of living of a household.
Here the degree of sample is 198.
And, t tabulated = 1.972017478
t calculated = 0.104588281
From, the above test we get to know that,
t tabulated > t calculated
As t tabulated value is greater than ta calculated value, we can say the null hypothesis is accepted.
Therefore, family size and agricultural land owners are related with each other. Any change in one variable will affect the other variable.
Non-probability sampling offers valuable insights into specific segments of the population, particularly in exploratory research or when dealing with inaccessible groups. While these methods are prone to bias and cannot guarantee the representativeness of the population, their strategic use in social research can uncover nuanced understandings of complex social phenomena. Future research should aim to combine these techniques with probability sampling methods or advanced statistical analysis to mitigate biases and enhance the validity of research findings.
Analyzing Non-Probability Sampling Methods in Social Research. (2024, Feb 17). Retrieved from https://studymoose.com/document/analyzing-non-probability-sampling-methods-in-social-research
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