Quantitative Data Analysis: Methods & Techniques Simplified 101

on Data Analytics, Data Integration, Data Warehouse, ETL, ETL Tutorials • May 18th, 2022 • Write for Hevo

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Data Analysis is an important part of research as a weak analysis will produce an inaccurate report that will cause the findings to be faulty, invariably leading to wrong and poor decision-making. It is, therefore, necessary to choose an adequate data analysis method that will ensure you obtain reliable and actionable insights from your data.

Finding patterns, connections, and relationships from your data can be a daunting task but with the right data analysis method and tools in place, you can run through the chunk of data you have to come up with information regarding it. There are different data analysis methods available, this article is going to focus on quantitative data analysis and discuss the methods and techniques associated with it.

You will learn about Quantitative Data Analysis in this article. You will also obtain a comprehensive understanding of Quantitative Data Analysis, including the methods and techniques involved. Continue reading to learn more about Quantitative Data Analysis.

Table of Contents

What is Quantitative Data Analysis?

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Data Analysis can be explained as the process of discovering useful information by evaluating data whereas quantitative data analysis can be defined as the process of analyzing data that is number-based or data that can easily be converted into numbers. It is based on describing and interpreting objects statistically and with numbers as it aims to interpret the data collected through numeric variables and statistics.

Quantitative data analysis techniques typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually gotten from avenues like surveys, questionnaires, polls, etc. data can also come from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

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Data Preparation Steps for Quantitative Data Analysis

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Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. This step is very important and has to be discussed before mentioning the methods and techniques involved because, if the data is not gathered correctly and cleaned, the analysis may not be carried out properly leading to wrong findings, wrong judgments on the hypothesis, and misinterpretation, therefore, leading to decisions made upon statistics that did not accurately represent the dataset.

To prepare data for quantitative data analysis simply means to convert it to meaningful and readable formats, below are the steps to achieve this:

  • Data Validation: This is to evaluate if the data was collected correctly through the required channels and to ascertain if it meets the set-out standards stated from the onset. This can be done by checking if the procedure was followed, making sure that the respondents were chosen based on the research criteria, and checking for completeness in the data.
  • Data Editing: Large datasets may include errors where fields may be filled incorrectly or left empty accidentally. To avoid having a faulty analysis, data checks should be done to identify and clear out anything that may lead to an inaccurate result.
  • Data Coding: This involves grouping and assigning values to data. It might mean forming tables and structures to represent the data accurately.

Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article which is the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis involves the use of computational and statistical methods that focuses on the statistical, mathematical, or numerical analysis of datasets. It starts with a descriptive statistical phase and is followed up with a closer analysis if needed to derive more insight such as correlation, and the production of classifications based on the descriptive statistical analysis. 

As can be deduced from the statement above, there are two main commonly used quantitative data analysis methods namely the descriptive statistics used to explain certain phenomena and inferential statistics used to make predictions. Both methods are used in different ways having techniques unique to them. An explanation of both methods is done below.

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers gotten from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

  • Mean:  This is used to calculate the numerical average of a set of values.
  • Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
  • Mode: This is used to find the most commonly occurring value in a dataset.
  • Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
  • Frequency: This indicates the number of times a value is found.
  • Range: This shows the highest and lowest value in a set of values.
  • Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
  • Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

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2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values and descriptive statistics is all about explaining details of a specific dataset using numbers, but, it does not explain the motives behind the numbers hence, the need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

They are various statistical analysis methods used within inferential statistics, a few are discussed below.

  • Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs are helpful in understanding the nuances of a dataset and factors that may influence a data point.
  • Regression Analysis: Regression analysis is used to estimate the relationship between a set of variables. It is used to show the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may have an impact on the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might have an effect on a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
  • Monte Carlo Simulation: Monte Carlo simulation also known as the Monte Carlo method is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. It is used by data analysts to perform an advanced risk analysis to help in forecasting future events and taking decisions accordingly.
  • Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
  • Factor Analysis:  A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
  • Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis where these groups or cohorts usually have common characteristics or similarities within a defined period.
  • MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank highest than the others in the process. 
  • Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different, that is, data points within a cluster will look like each other and different from data points in other clusters.
  • Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different points in time like weekly, and monthly email sign-ups to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
  • SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

Conclusion

This write-up has talked about quantitative data analysis showing that it is all about analyzing number-based data or converting data into the numerical format by using various statistical techniques to deduce useful insights. It went further to show that there are two methods used in quantitative analysis, descriptive and inferential stating when and how each of these methods can be used by giving techniques associated with them. 

Finally, to carry out effective quantitative data analysis, one has to consider the type of data you are working with, the purpose of carrying out such analysis, and the hypothesis or outcome that may be gotten from the analysis.

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