Quantitative Analysis:
Quantitative Data analysis can be defined as an economic tool for the management and investors to analyze financial events and make investment and business decisions. It uses complex financial and statistical models to quantify objective business data for determining the after-effects of a decision on the business operations. The worthiness of investments is found using it to identify the correlation between the variables.
Applications of quantitative analysis:
· Performance evaluation
· Measurement
· Evaluating a financial instrument
· Helps in predicting world events like changes in the price of a share
Transform raw data to quantifiable data:
Raw information is converted to quantitative data by quantitative analysis. The obtained quantified data is further analyzed to provide evidential data to help in research processes. Thus, for transforming raw information to evidential data for research process the following are the steps of Quantitative data analysis.
Step 1: Quantitative Data Analysis:
It is an essential step of quantitative analysis as it will help to determine the best way to organize data. For the data to give a meaning, it has to be entered into a spreadsheet and organized or coded.
· Nominal
· Ordinal
· Interval
· Ratio
Step 2: Descriptive statistics:
The second step is to summarize or describe the raw data to make it easy to identify the patterns or to visualize what is the data it is showing. The most commonly used descriptive statistics include, among others:
· Mean:
A numerical average of the scores or values of a specific variable
· Median:
It is the numerical midpoint of the values or scores for a variable at the center of distribution of the scores
· Mode:
The most common value or score of a specific variable
· Minimum & Maximum values:
It is the range or the highest and lowest values or scores for a particular variable
· Percentages:
It is the format to express the set of values or scores for variables as a percentage of the whole
· Frequency:
It is the count of the number of times a particular value or score is found or observed in the scale or the data set.
Step 3: Inferential Statistics:
If the data for research needs to make inferences or predictions about the population, it needs inferential statistics. It is applied for the following functions:
· It examines the differences and relationships between two or more samples of the population.
· It is employed for complex analysis
· It helps in finding the vital differences between variables and the sample groups of the population
· It allows testing hypotheses and generalizing results.
There are many necessary inferential statistical tests which include, among others:
· Correlation
Correlation pursues to describe the nature of the relationship between two variables of the following:
ü Strong
ü Weak
ü Positive
ü Negative
ü Statistically significant
· ANOVA
Analysis of Variance seeks to determine if the means of two sampled groups are statistically significant or only by random chance. It only confirms the difference is substantial and not the reason.
Regression helps in determining whether one variable is the predictor of another variable. Regression analysis is similar to correlations, where the causation is unable to be inferred from the analysis.
· The number of variables
The quantitative analysis also depends on the number of variables in the study which include
· Univariate
· Bivariate
· Multivariate
