Major data analysis techniques to use in leisure or social science research
Following are the major techniques of data analysis that will help do social science or leisure science research.
Chi-square test
The Chi-square test is denoted by X2symbol and helps show the relation between 2 nominal variables. The nominal variables are the ones that give descriptions related to an individual’s age or gender. This test is developed to prove any significance in the relationship. If there is relevant significance, then the null hypothesis of no difference will be rejected. This test is performed by carefully examining cell counts or percentages of a table. Then the comparison of actual counts occurs with an expected count that will happen when as per the null hypothesis, there won’t be any difference, just like there are similar no. of individuals of two distinct racial groups in a participation study of two distinct leisure activities. This test includes a summation of differences between the percentages or counts and the expected percentages or counts so that the more the total, the larger the value of Chi-square will be. When you sum up the difference in their squared value, you obtain a Chi-square value. Pursue a career in Data Analytics with the number one training institute 360DigiTMG. Enroll in the Best Data Analyst Courses in Bangalore to start your journey.
T-test
T-test includes a comparison of 2 means to know whether the distinction between the means have any significance or not based on the rejection of the null hypothesis of no difference and taking the substitute hypothesis that there’s a point of difference. For instance, this test may include the average salary of individuals who participated in various recreational activities like bowling vs golf to determine whether differences between the two are fairly expected. Bowling is not an expensive sport, but golf is quite costly. T-test may be used as an independent samples test or paired samples test. In independent samples test, the means of two groups in samples are differentiated in respect to single variable to detect differences like time spent by children and parents in browsing internet whereas in paired sample test the means of two subgroups in whole samples are differentiated such as time spent on browsing the internet as well as watching television. Also, check out these Best Data Analytics Courses to start a career in Data Analytics.
Linear regression
This technique is utilized where there’s consistency in the two variable’s correlation. This helps researchers to forecast one variable by determining the other. The researcher creates an equation for developing a model of this relationship that determines the type. This equation is written as y=a+ by where b is the slope of the line, which determines the correlation between 2 variables, whereas a refers to the constant. Don’t delay your career growth, kickstart your career by enrolling in this Data Analyst Course in Pune.
Non-linear regression
This approach is used in circumstances when variables are unrelated in a linear manner, such as one straight line won’t express the relationship between the two variables. It happens in case of curved relations like rapid interest growth in one activity combined by enthusiasm rate and followed by a plateau of interests. It also occurs in cyclical relations such as the interesting pattern of any activity both times a year or up/down in interest rate. Wish to pursue a career in data analytics? Enroll in this Data Analyst Course fee in Hyderabad to start your journey.
Factorial analysis of variance
It is one type of ANOVA test in which the basis of the test depends on more than one variable analysis. For example, detecting the relation between activity participation and the age/gender of participants. It is used in cross-tabulating the means of distinct groups to know their significance by differentiating both means of groups and spread degrees of the groups. To produce an F score and mean square, the sum of squares and degrees of freedom are considered.
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