Data Analysis: Quantitative : Data Analysis: Quantitative By Lucell Larawan Introduction : Introduction Data analysis is the process of summarizing trends and patterns observed in the data, determining major differentials or relationships among variables used in the study and the application of appropriate statistical tests on a set of data to answer the objectives of the study. Slide 3: Data analysis depends on:
The objective of the study
The kind of scales of measurement of the variables being dealt with Slide 4: Types of data analysis:
3) Multivariate Univariate analysis : Univariate analysis This refers to the analysis of one variable at a time. Techniques for univariate method : Techniques for univariate method A frequency table provides the number of people and the percentage belonging to each of the categories for the variable in question. Slide 7: Example of frequency table:
Table1. Reasons for using the gym Diagrams : Diagrams Bar chart Slide 9: Pie chart Slide 10: Measures of central tendency
Mode Slide 11: Measures of dispersion
Standard deviation Bivariate analysis : Bivariate analysis Bivariate analysis is concerned with the analysis of two variables at a time in order to uncover whether or not the two variables are related.
Example of objective: “To determine whether or not stress is associated with being a learning organization at company X.” Methods of bivariate analysis : Methods of bivariate analysis Contingency table : Contingency table Table 1. Relationship between gender and visiting the gym Slide 15: Pearson’s r is a method for examining relationships between interval/ratio variables.
1) the coefficient will almost certainly lie between 0 and 1—this indicates the strength of relationship Slide 16: Cont…
2) the closer the coefficient is to 1, the stronger the relationship; the closer it is to 0, the weaker the relationship
3) the coefficient will be either positive or negative—this indicates the direction of a relationship Slide 17: Spearman’s rho, represented by the Greek symbol ρ, is designed for the use of pairs of ordinal variables; but it is also used when one variable is ordinal and the other is interval/ratio.
The same as Pearson’s r in terms of the outcome of calculation of value. Slide 18: Phi coefficient is used for the analysis of the relationship between two dichotomous variables. The value is computed in the same way as the Pearson’s r value. Slide 19: Cramer’s V uses a similar formula to phi and can be employed in nominal variables. This statistic, however, can take only a positive value. Multivariate analysis : Multivariate analysis Multivariate analysis entails the simultaneous analysis of three or more variables. There are three contexts within which multivariate analysis might be employed:
Could the relationship of variables be spurious?
Could there be an intervening variable?
Could a third variable moderate the relationship?