# Quantitative Data Basics

## What is Quantitative Data?

Quantitative data is information that can be counted or measured and given a numerical value. Quantitative data can be used for mathematical calculations and statistical analysis. Program impact can be evaluated, and programming decisions can be made based on these mathematical derivations. Quantitative data can be used to determine:

• How many
• How much
• How often

## Quantitative Methods

Quantitative data is usually collected using surveys, experiments, phone interviews, polls, or questionnaires. Questionnaires and surveys are standard methods for collecting quantitative data.

## Quantitative Data Analysis

Quantitative data should be properly analyzed to report program impact and make future programming decisions. Analyzing quantitative data can involve the following steps:

#### Step 1: Transform raw data into quantifiable data.

The data will need to be converted into quantifiable data by quantitative analysis. This involves organizing data properly to give it meaning. Data must be entered into a spreadsheet, organized, and coded.

#### Step 2: Relate measurement scales with variables.

This step involves associating measurement scales such as nominal, ordinal, interval, and ratio with the variables. This step is essential and helps arrange data in proper order within your spreadsheet.

#### Step 3: Descriptive statistics.

It can be challenging to establish a pattern in the raw data; therefore, descriptive statistics help researchers find patterns within the data. Some commonly used descriptive statistics include:

• Mean: an average of values for a variable
• Median: the midpoint of the value scale for a variable
• Mode: the most common value of a variable
• Frequency: the number of times a particular value is observed in the scale
• Minimum and Maximum: the lowest and highest scale values
• Percentages: a format to express scores and set of values for variables.

#### Step 4: Inferential Statistics.

These complex forms of analysis show the relationships between multiple variables to generalize results and make predictions. This can include:

• Correlation: describes the relationship between two variables
• Regression: helps to determine whether one variable is the predictor of another variable
• Analysis of variance: use it to determine if there is any difference between the means of different groups.