# Introduction to quantitative data analysis methods

By Muskan & Priya Chetty on November 17, 2021

Quantitative data is information that can be quantified. It can be counted or measured, and given a numerical value. Quantitative data tends to be structured in nature and is suitable for statistical analysis. Quantitative data is used to address “How many?”, “How often?” or “How much?”. It could be discrete i.e. having numerical values or continuous values that can be broken up into parts.

Quantitative data analysis is helpful as it provides quantifiable and easy-to-understand results. Quantitative data can be analyzed in a variety of different ways. SPSS provides a large range of methods to analyze quantitative data. This article discusses the most commonly used methods in small program evaluation with examples.

## Descriptive statistics helps to understand quantitative data

Descriptive statistics gives basic information about a dataset. Understanding these basics is important because the further steps of processing the data depend on it. In this type of data analysis, the information is presented using graphs and tables. The table below shows different methods of performing descriptive statistics.

## Compare means to determine the significant differences in quantitative data

Comparing means can compare the mean of interval/ratio (scale) data with a hypothesized value or between different groups and determine if there is any significant difference.

## Compare predicted and observed quantitative data with Chi-square

A chi-square statistic is a test that measures the comparison between the model’s predicted data to the actual observed data. These tests are often used in hypothesis testing. The chi-square statistic is what compares the size of the difference between the expected and the observed data, given the sample size and the number of variables in the relationship.

## Understand the relationship between variables with correlation analysis

A correlation analysis tests the relationship between two continuous variables in terms of

1. how strong the relationship is, and
2. in what direction the relationship goes.

The strength of the relationship is given as a coefficient (Pearson’s r) which can be anything between -1 and 1.

## Factor analysis reduces the number of dimensions in large quantitative datasets

Exploratory factor analysis (hereafter referred to as factor analysis) helps to investigate the underlying structure in the pattern of correlations between several variables (often referred to as items). In a dataset with a large number of variables, factor analysis can help to investigate if the variables represent a smaller number of factors or dimensions.

## Regression analysis helps understand the relationship between variables

Regression analysis majorly is the type of quantitative data analysis focused on building a relationship between variables by examining the impact of one or more variables on others. Regression is of different types as shown in the table below.

The nature of quantitative data is diverse. It can be said that no two datasets are the same. However, using basic methods of analysis simplifies the understanding of a complex dataset.