Question

Do you think Statistics can be used to solve societal problem? Justify your answer with a practical example. Please note that the answer should not be less than 2 pages and support your answer with References

Answer 1

Yes , Statistics can be used to solve societal problem .

Social problem-solving might also be called ‘problem-solving in real life’. In other words, it is a rather academic way of describing the systems and processes that we use to solve the problems that we encounter in our everyday lives.

The word ‘social ’ does not mean that it only applies to problems that we solve with other people, or, indeed, those that we feel are caused by others. The word is simply used to indicate the ‘real life ’ nature of the problems, and the way that we approach them.

A social problem: e.g., Unemployment. Basic statistic is used to calculate the level of unemployment 4.7%, the level 10 years ago 9%, the correlations between unemployment and education, or health, or the cost of incarceration, or eye color. Statistics is used extensively to describe social problems and their correlations to various other aspects of social structure and individuals. This is the meta-level understanding hints at the structure of the problem — e.g., more education leads to less unemployment, chronic unemployment increases the chances of incarceration, … and finally eye-color does not correlate to any aspect of this social problem. Once strategies are suggested for solving the problem, statistical measurement techniques are used to identify the most successful, or the least expensive strategy.

"Everything in statistics is a missing data problem; inferring about things we don’t know from things we know, with measures of uncertainty and come up with methods to fill in the gaps ."

We use statistical methods generally in two cases:

1)When our problem is of a deterministic nature, but we want to get some estimations, predictions, confidence intervals and other helpful issues.

2)When our problem is a pure stochastic one, so we cannot explain it with traditional causality deterministic methods. Then statistical methods is a 'must'.

But as everything in life our real problem in the beginning of the day does not belong neither to 1) nor to 2) case above, so situation is getting a little bit complex...

That's the reason why statistics is used to solve societal problem.

A practical example that will fulfill the above explanation.

The knowledge we gain through basic research expands our understanding of the causes and consequences of a social problem, for example, identifying the predictors of HIV/AIDS or examining the rate of homelessness among AIDS patients. Conversely, applied research involves the pursuit of knowledge for pro-gram application or policy evaluation; effective program practices documented through applied research can be incorporated into social and medical programs serving HIV/AIDS patients.

Variables are a property of people or objects that can take on two or more values. For example, as we try to explain HIV/AIDS, we may have a specific explanation about the relationship between two variables: social class and HIV infection. Social class could be measured according to household or individual income, whereas HIV infection could be measured as a positive test for the HIV antibodies. The relationship between these variables can be stated in a hypothesis, a tentative statement about how the variables are related to each other. We could predict that HIV infection would be higher among lower-income men and women. In this hypothesis statement, we’ve identified a dependent variable (the variable to be explained, HIV infection) along with an independent variable (the variable expected to account for the cause of the dependent variable, social class). Data, the information we collect, may confirm or refute this hypothesis.

Research methods (i.e., how sociologists collect data) can include quantitative or qualitative approaches or a combination. Quantitative methods rely on the collection of statistical data. They require the specification of variables and scales collected through surveys, interviews, or questionnaires. Qualitative methods are designed to capture social life as participants experience it. These methods involve field observation, depth interviews, or focus groups. Following are definitions of each specific method.

Survey research: This is data collection based on responses to a series of questions. Surveys can be offered in several formats: a self-administered mailed survey, group surveys, in-person interviews, or telephone surveys. For example, information from HIV/AIDS patients may be collected by a survey sent directly in the mail or by a telephone or in-person interview .

Qualitative methods: This category includes data collection conducted in the field, emphasizing the observations about natural behavior as experienced or witnessed by the researcher. Methods include participant observation (a method for gathering data that involves developing a sustained relationship with people while they go about their normal activities), focus groups (unstructured group interviews in which a focus group leader actively encourages discussion among participants on the topics of interest), or intensive (depth) interviewing (open-ended, relatively unstructured questioning in which the interviewer seeks in-depth information on the interviewee’s feelings, experiences, and perceptions). Sociologists have used various qualitative methods in HIV/AIDS research—collecting data through participant observation at clinics or support groups and focus groups or depth interviews with patients, health care providers, or key informants.

Historical and comparative methods: This is research that focuses on one historical period (historical events research) or traces a sequence of events over time (historical process research). Comparative research involves multiple cases or data from more than one time period. For example, researchers have examined the effectiveness of HIV/AIDS treatments over time and compared infection rates between men and women.

Secondary data analysis: Secondary data analysis usually involves the analysis of previously collected data that are used in a new analysis. Large public survey data sets, such as the U.S. Census, the General Social Survey, the National Election Survey, or the International Social Survey Program, can be used, as can data collected in experimental studies or with qualitative data sets. For HIV/AIDS research, a secondary data analysis could be based on existing medical records or a routine health survey . The key to secondary data analysis is that the data were not originally collected by the researcher but were collected by another researcher and for a different purpose.