Quantification in social research

SUGGESTIONS ARE MOST WELCOME

1. Presented by: Dr. TRIPTI SHARMA. QUANTIFICATION IN SOCIAL RESEARCH

2. INTRODUCTION In mathematics and empirical science, quantification is the act of counting and measuring that maps human sense observations and experiences into quantities. Quantification in this sense is fundamental to the scientific method. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.

3. QUANTIFICATION In the social sciences, quantification is an integral part of economics and psychology. Both disciplines gather data (economics by empirical observation) and (psychology by experimentation) Both use statistical techniques such as regression analysis to draw conclusions from it.

4. STATISTICS

5. INTRODUCTION In the modern world of computers and information technology, the importance of statistics is very well recognized by all the disciplines. Statistics has originated as a science of statehood and found applications slowly and steadily in Agriculture, Economics, Commerce, Biology, Medicine, Industry, planning, education and so on. As on date there is no other human walk of life, where statistics cannot be applied.

6. MEANING Statistics is concerned with scientific methods for collecting, organizing, summarizing, presenting and analyzing data as well as deriving valid conclusions and making reasonable decisions on the basis of this analysis. Statistics is concerned with the systematic collection of numerical data and its interpretation. The word ‘statistic’ is used to refer to 1. Numerical facts, such as the number of people living in particular area. 2. The study of ways of collecting, analyzing and interpreting the facts.

7. STATISTICS: MEANING The practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample is known as Statistics.

8. SCOPE Statistics is not a mere device for collecting numerical data, but as a means of developing sound techniques for their handling, analyzing and drawing valid inferences from them. Statistics is applied in every sphere of human activity social as well as physical like Biology, Commerce, Education, Planning, Business Management, Information Technology, etc. It is almost impossible to find a single department of human activity where statistics cannot be applied. We now discuss briefly the applications of statistics in other disciplines.

9. LIMITATION Statistics is not suitable to the study of qualitative phenomenon: Statistics does not study individuals: Statistical laws are not exact: Statistics table may be misused: Statistics is only, one of the methods of studying a problem:

10. CONCLUSION Statistics is indispensable in this modern age aptly termed as "the age of planning". Statistical data and techniques of statistical analysis are immensely useful in solving economical problems such as wages, price, time series analysis, demand analysis.

11. STATISTICAL TECHNIQUES OF DATA ANALYSIS

12. Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Interviews and observations are forms of qualitative research, while experiments and surveys are quantitative research.

13. TYPES Descriptive  Exploratory Inferential Predictive C ausal. Some, however, are more specific, such as qualitative analysis, which looks for things like patterns and colors, and quantitative analysis, which focuses on numbers.

14. PROCESS Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. The term data analysis is sometimes used as a synonym for data modeling.

15. STATISTICAL METHODS Mean The arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. The mean is useful in determining the overall trend of a data set or providing a rapid snapshot of your data. Another advantage of the mean is that it’s very easy and quick to calculate .

16. Standard Deviation The standard deviation, often represented with the Greek letter sigma, is the measure of a spread of data around the mean. A high standard deviation signifies that data is spread more widely from the mean, where a low standard deviation signals that more data align with the mean. In a portfolio of data analysis methods, the standard deviation is useful for quickly determining dispersion of data points .

17. Regression Regression models the relationships between dependent and explanatory variables, which are usually charted on a scatter plot . The regression line also designates whether those relationships are strong or weak. Regression is commonly taught in high school or college statistics courses with applications for science or business in determining trends over time.

18. Sample Size Determination When measuring a large data set or population, like a workforce, you don’t always need to collect information from every member of that population – a sample does the job just as well. The trick is to determine the right size for a sample to be accurate. Using proportion and standard deviation methods, you are able to accurately determine the right sample size you need to make your data collection statistically significant.

19. Hypothesis Testing Also commonly called  t  testing, hypothesis testing assesses if a certain premise is actually true for your data set or population. In data analysis and statistics, you consider the result of a hypothesis test  statistically significant  if the results couldn’t have happened by random chance. Hypothesis tests are used in everything from science and research to business and economic.