Basic Statistics

What Is Data Analysis?

Data analysis is the process of systematically examining data with the purpose of spotlighting useful information. Data analysis is the foundation of scientific research. Conducting a complete analysis of the data you have collected will enable you to

• Determine the impact of your work
• Assess the quality of your programming
• Communicate results to your stakeholders

Qualitative or Quantitative Data?

There are two types of data: qualitative and quantitative. Both types of data have strengths and limitations and may be appropriate for different settings, evaluation designs, and evaluation questions.

Qualitative data consist of words and narratives. The analysis of qualitative data can come in many forms including highlighting key words, extracting themes, and elaborating on concepts.

Quantitative data are numerical information, the analysis of which involves statistical techniques. The type of data you collect guides the analysis process.

One example of qualitative data would be the color of the sky.

An example of quantitative data would be the age of your car,the number of pennies in your pocket.

The two tables below detail the strengths and limitations of both types of data.

Levels Of Measurement

Types of Variables

Surveys can contain many kinds of questions; these questions are often called variables. There are some basic types of variables. It is important to understand the different types of variables, because the type of variable can lead to different kinds of data and guide your analysis.

The following are types of variables:

Categorical

Qualitative data are often termed categorical data. Data that can be added into categories according to their characteristics.

Nominal Variable (Unordered list)

A variable that has two or more categories, without any implied ordering.

Examples : 

• Gender - Male, Female
• Marital Status - Unmarried, Married, Divorcee
• State – Karnataka, Andhra Pradesh, Tamil Nadu, Kerala
• Name of a book
• How do you describe yourself? (select all that apply)

1. African American, not of Hispanic origin
2. American Indian or Alaskan Native
3. Asian/Pacific Islander
4. Hispanic/Latino
5. White, not of Hispanic origin
6. Other, please specify ______________ 

Ordinal Variable (Ordered list)

A variable that has two or more categories, with clear ordering.

Examples : 

• Scale - Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree
• Rating - Very low, Low, Medium, Great, Very great
• The order of runners finishing a race
• What is your highest level of education completed?

1. Less than high school
2. High school diploma/GED
3. Some college
4. Associate’s degree
5. Bachelor’s degree
6. Graduate degree

Interval

An interval variable is similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. In other words, it has order and equal intervals.

Examples : 

• Temperature in Celsius - Temperature of 30°C is higher than 20°C, and temperature of 20°C is higher than 10°C. The size of these intervals is the same.
• Annual Income in Dollars - Three people who make $5,000, $10,000 and $15,000. The second person makes $5,000 more than the first person and $5,000 less than the third person, and the size of these intervals is the same.
• What is the average daytime temperature during the summer in Bengaluru?

1. 29 degrees
2. 30 degrees
3. 31 degrees
4. 32 degrees
5. 33 degrees
6. 34 degrees
7. 35 degrees

Ratio

It is interval data with a natural zero point. When the variable equals 0.0, there is none of that variable.

Examples : 

• Time is ratio since 0 time is meaningful.
• Temperature in Kelvin - It is a ratio variable, as 0.0 Kelvin really does mean 'no temperature.
• Please select your child’s weight.

1. 65 lbs
2. 70 lbs
3. 80 lbs
4. 90 lbs
5. 95 lbs

When can we apply ?

Analysis of Quantitative Data

Interpreting data through analysis is key to communicating results to your stakeholders. The type of analysis you use depends on the research design, the type of variables you have, and the distribution of the data.

In this section we will focus on the two types of analysis: descriptive and inferential.

Descriptive Analysis

Descriptive analysis tells us about the basic qualities of the data. Descriptive analysis includes descriptive statistics such as the range, minimum, maximum, and frequency. It also includes measures of central tendency such as mean, median, mode, and standard deviation that tell us what our data look like.

There are many ways to describe data, and we can use descriptive analysis to tell us what the data look like. Below are some common ways to describe data.

Using the set of scores below, the following table lists examples of descriptive statistics.

Measures of Central Tendency

The measures of central tendency can give a snapshot of how participants are responding in general. These measures include the mean, median, and mode.

Standard Deviation

A measure of how close the scores are centered around the mean score. The standard deviation tells us how well the mean represents all of the data. A standard deviation represents the average amount that a given score deviates from the mean score.

Will continue further concepts of Basic statistics in the next article, all your feedback and suggestions are welcome.................