Quantitative data analysis comes with the challenge of analyzing large datasets consisting of numeric variables and statistics. Researchers often get overwhelmed by various techniques, methods, and data sources.
At the same time, the importance of data collection and analysis drastically increases. It helps improve current products/services, identify the potential for a new product, understand target market psychology, and plan upcoming campaigns.
We have compiled this in-depth guide to ensure you get over the complexities of quantitative data analysis. Let’s begin with the basic meaning and importance of quantitative data analysis.
Quantitative data analysis meaning
Quantitative data analysis evaluates quantifiable and structured data to obtain simplified results. Analysts aim to interpret and draw conclusions from numeric variables and statistics. The entire analytical process works on algorithms and data analytics software, helping gain valuable insights. Continuous values are broken into parts for easy understanding using various tools and software. Such data is extracted through surveys and questionnaires.
However, data analytics software also helps extract quantitative data through email campaigns, websites, and social media.
Qualitative Vs. Quantitative research: major differences
Qualitative research aims at extracting valuable insights through non-numerical data like the psychology of customers. The research aims at obtaining solid results and confirming assumptions on general ideas. Furthermore, the collected data presentation remains descriptive instead of numerical-centric.
On the other hand, quantitative research focuses on numbers and statistics to identify gaps in current marketing and operational methods. It successfully answers questions like how many leads are converted in a specific email campaign.
Collecting quantitative data includes surveys, polls, questionnaires, etc. It remains efficient in identifying trends and patterns in the collected data. However, the obtained results aren’t always accurate as there are chances of numerical errors.
Note: both quantitative and qualitative data can be obtained with surveys. However, qualitative data collection focuses on asking open-ended questions.
In contrast, quantitative research focuses on close-ended ones.
4-Step Process of Quantitative data analysis
Now that we understand the meaning of quantitative data analysis, let’s proceed with four simple steps for conducting it.
Step 1: Identifying your goals and objectives.
Start by analyzing current business problems and the ones you plan to address with your analysis.
For example, your customer churn rate significantly increased in the last month.
Do you want to identify the reasons behind it? Being clear with the objective helps in collecting and analyzing relevant data.
Step 2: Data collection
Now you are clear with the issue you plan to address. Let’s identify and collect data from all relevant sources.
For example, conducting a survey of MCQs targeting all possible reasons behind the increase in the churn rate. Identify all relevant data sources and collect data for further analysis.
Step 3: Data cleaning
As discussed earlier, quantitative data doesn’t remain highly accurate as they are always chances of errors. Due to this, quantitative data analysis goes through many stages of cleaning.
Firstly, analysts start with data validation to identify if the data was collected based on defined procedures. Secondly, large datasets require a lot of editing to identify errors like empty fields or wrongly inserted digits.
Remember that the collected data consists of many duplications, unwanted data points, a lack of structure, and major gaps you must eliminate. Lastly, the collected data is presented in structured formats like tables for easy analysis.
Step 4: Data Analysis and interpretation
Now, you are equipped with fairly accurate data sets required for analysis. Using tools and software, data analysts interpret the collected data to draw valuable conclusions. Many techniques are used for quantitative data analysis, including time-series analysis, regression analysis, etc. However, applying the techniques correctly play a greater role than the type of technique used.
What are the methods for Quantitative research data collection?
Now that we know the meaning of quantitative data collection, let’s look at some methods of collecting it:
Surveys: close-ended questions
Surveys remain one of the most common methods of quantitative research data collection. These surveys include super-specific questions where respondents answer yes/no or multiple-choice questions. Most companies are going with rating questions or checklist-type survey questions.
Conducting interviews
Interviews remain another commonly used method for quantitative data collection. The interviews remain structured with specific questions. Telephone interviews were generally preferred until the introduction of video interviews using Skype, Zoom, etc. Some researchers also go with face-to-face interviews to collect quality data.
Analytical tools
Manually collecting and analyzing large datasets remain inconvenient. Many different analytical tools are available to collect, analyze, interpret, and visualize a large amount of data. For instance, tools like GrowthNirvana remain effective in efficient marketing analytics and provide relevant data without delays. All valuable insights required for business growth are easily extracted to make quicker decisions.
Document review: analyzing primary data
Researchers often analyze the available primary data like public records. The findings support the results generated from other quantitative data collection methods.
Methods and techniques of quantitative data analysis
The two common methods used for quantitative data analysis are descriptive and inferential statistics. Analysts use both methods to generate valuable insights. Here’s how:
Descriptive statistics
This method describes a dataset and provides an initial idea to help researchers identify potential trends or patterns. It generally focuses on analyzing single variables and explains the details of specific datasets. There are two ways of analyzing data using the descriptive statistics technique- numerical and graphical representation. Let’s start with the numerical method of quantitative data analysis.
- Numerical Method
The numerical method organizes and evaluates data arithmetically to obtain simpler answers to complex problems. Describing data remains the easiest using the measure of central tendency and dispersion.
The measures of central tendency like mean, median, and mode help identify the collected data’s central position. On the flip side, the measures of dispersion, like range, standard deviation, variance, etc., help understand the extent of data distribution concerning the central point.
- Graphical method
The graphical method provides a better understanding of data through visual representation. Evaluating large data sets becomes easier if presented using a bar chart, pie chart, histogram, boxplot, etc.
Inferential statistics
Conducting only descriptive analytics isn’t enough to draw valuable conclusions from the collected data. It only provides limited information on the datasets, emphasizing inferential statistics’ importance.
Inferential statistics make predictions using the data generated through descriptive statistics. It helps establish relationships between different variables to make relevant predictions. The technique remains suitable for large datasets.
Certain samples of the data are taken to represent the entire set as evaluating large data remains hectic. Therefore, the summarized samples generated with descriptive statistics are used to draw valuable conclusions.
Let’s now focus on some commonly used methods in inferential statistics:
- Regression analysis
The method establishes a relationship between a dependent and independent variable(s). It assesses their current strength and predicts future possibilities to devise enhanced strategies. The most commonly used regression models are simple linear and multiple linear.
- Cross tabulations
Cross tabulation or contingency table method is one of the most used methods for market research. It assists in the easy analysis of two or more variables through systematic rows and columns. Furthermore, the major goal of cross tabs remains intact in showing the changes in a dependent variable based on different subgroups.
- Monte Carlo method
The method focuses on weighing all possible outcomes of specific scenarios. Analyzing the pros and cons helps in predicting advanced risks before taking action. Therefore, forecasting future risks based on changing scenarios improves decision-making.
- SWOT analysis
A SWOT analysis identifies an organization’s strengths, weaknesses, opportunities, and threats. It takes into consideration internal and external factors to make better business plans. Companies often conduct a SWOT analysis to improve their products and services or while initiating a new project.
- Time Series analysis
Time series or trend analysis evaluates data sets recorded within specific intervals. Instead of taking random samples, the data is recorded in given time frames. Companies often use time series analysis for forecasting demand and supply.
What are examples of Quantitative data?
Let’s now look at some examples of quantitative data:
- Total number of app downloads in a month
- The total number of people who loved a newly introduced product feature
- Number of users converted with a marketing campaign
- Total number of website conversions in six months
- Number of customers residing in a specific location
What does a data analyst do?
A data analyst collects and interprets data to answer key questions like the potential of new product development, changes in customer purchase behavior, gaps in current marketing campaigns, etc. Many data analysts conduct exploratory analysis to identify trends and patterns during the data cleaning process.
The job also includes communicating the findings to team members to create better strategies. One can thrive in this position with the right technical and leadership skills. Therefore, a data analyst’s two key roles include knowing how to collect and use the collected data for business growth.
What is the typical process that a data analyst follows?
The process followed by a data analyst includes the following steps:
- Setting objectives
Understanding the goals behind collecting data help target the right areas for data collection. Next, identify the type of data you need to collect to conduct a specific analysis.
- Collecting data
Now you are clear with the goals and data requirements. Focus on collecting data from identified sources using different methods of data collection. Some of the top sources include surveys, polls, interviews, etc.
- Data cleaning
The data collection process includes collecting a large amount of data which requires further cleaning for analysis. This step removes duplicate records and identifies omissions and other numerical errors.
- Data analytics and interpretation
Now, data analysts focus on analyzing the collected data using various tools and software. Based on the analysis, they draw relevant conclusions to make the most of their findings.
- Data visualization
Data analysts must also convey the acquired information to relevant team members. Data visualization refers to the graphical interpretation of collected data for easy understanding. The process also helps analysts identify hidden insights for detailed reporting.
What techniques and tools do data analytics use?
Data analysts use various techniques, including regression analysis, the Monte Carlo method, factor analysis, cohort analysis, etc. The right blend of techniques to suit specific situations helps achieve the required results.
The tools used for data analysis reduce the manual burden of analysts and improve overall decision-making. There are varied categories of tools in data analysis, including business intelligence, ETL tools, automation tools, data mining, data visualization tools, etc.
Some popular choices for data analysis include Google Analytics, Growth Nirvana (marketing analytics), Improvado, Datapine, etc.
What are the skills required to become a data analyst?
A data analyst requires the following skills to thrive in the field:
- Complete knowledge of python programming
- Mathematical and statistical understanding
- Data decluttering, organizing, and analyzing
- SQL knowledge
- Data visualization
- Problem-solving
- Logical reasoning and critical thinking
- Sharp communication skills
- Collaboration
What are some of the best data analytics course?
Let’s now look at some of the best data analytics courses in 2022 to help you gain all relevant skills:
- Detailed learning: Data Analyst Nanodegree program by Udacity
A 4-month program helping people develop advanced programming skills to handle complex data-related issues. It covers everything from data analysis, and visualization, to exploration.
- Best data analytics course for beginners: Become A Data Analyst by LinkedIn.
The course consists of beginner-friendly lessons suitable for people with no prior understanding of data analysis. The experts in the industry take all sessions. Furthermore, you can easily complete the course within the free 30 days LinkedIn Learning period.
- Bite-sized learning: Data Analyst with R by Datacamp
The entire learning experience breaks down into multiple courses to help you keep up the pace. Industry experts curate about 19 different courses with a duration of 4 hours for every course. Furthermore, it also helps students gain practical exposure by working with real-life datasets.
What does the future hold for data analytics?
Data analytics remains a constantly evolving area that will become increasingly important for businesses in the coming future. Extracting real-time insights will help enhance business operations for continuous growth. Furthermore, the increasing growth of business analytics tools makes it easier for businesses to analyze data and draw conclusions without complex coding knowledge.
Key Takeaways
- Analysts must use quantitative (number-oriented) and qualitative (non-numeric) data to devise and modify business strategies.
- Surveys are used for obtaining both quantitative and qualitative research data. However, quantitative surveys include only close-ended questions.
- Data cleaning remains one of the most crucial steps of data analysis. It ensures the collected data doesn’t contain duplications, omissions, unwanted data points, etc.
- The top skills possessed by data analysts include python programming, statistical knowledge, data decluttering, SQL knowledge, collaboration, and communication skills.
The top two data analytics techniques — descriptive and inferential statistics- are complementary.
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