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10 Quantitative Data Analysis Software for Data Scientists

Are you curious about digging into data but not sure where to start? Don’t worry; we’ve got you covered! As a data scientist, you know that having the right tools can make all the difference in the world. When it comes to analyzing quantitative data, having the right quantitative data analysis software can help you extract insights faster and more efficiently. 

From spotting trends to making smart decisions, quantitative analysis helps us unlock the secrets hidden within our data and chart a course for success.

In this blog post, we’ll introduce you to 10 quantitative data analysis software that every data scientist should know about.

What is Quantitative Data Analysis?

Quantitative data analysis refers to the process of systematically examining numerical data to uncover patterns, trends, relationships, and insights. 

Unlike analyzing qualitative data, which deals with non-numeric data like text or images, quantitative research focuses on data that can be quantified, measured, and analyzed using statistical techniques.

What is Quantitative Data Analysis Software?

Quantitative data analysis software refers to specialized computer programs or tools designed to assist researchers, analysts, and professionals in analyzing numerical data. 

These software applications are tailored to handle quantitative data, which consists of measurable quantities, counts, or numerical values. Quantitative data analysis software provides a range of features and functionalities to manage, analyze, visualize, and interpret numerical data effectively.

Key features commonly found in quantitative data analysis software include:

• Data Import and Management: Capability to import data from various sources such as spreadsheets, databases, text files, or online repositories. 
• Descriptive Statistics: Tools for computing basic descriptive statistics such as measures of central tendency (e.g., mean, median, mode) and measures of dispersion (e.g., standard deviation, variance).
• Data Visualization: Functionality to create visual representations of data through charts, graphs, histograms, scatter plots, or heatmaps. 
• Statistical Analysis: Support for conducting a wide range of statistical tests and analyses to explore relationships, test hypotheses, make predictions, or infer population characteristics from sample data.
• Advanced Analytics: Advanced analytical techniques for more complex data exploration and modeling, such as cluster analysis, principal component analysis (PCA), time series analysis, survival analysis, and structural equation modeling (SEM).
• Automation and Reproducibility: Features for automating analysis workflows, scripting repetitive tasks, and ensuring the reproducibility of results. 
• Reporting and Collaboration: Tools for generating customizable reports, summaries, or presentations to communicate analysis results effectively to stakeholders.

Benefits of Quantitative Data Analysis

Quantitative data analysis offers numerous benefits across various fields and disciplines. Here are some of the key advantages:

Making Confident Decisions

Quantitative data analysis provides solid, evidence-based insights that support decision-making. By relying on data rather than intuition, you can reduce the risk of making incorrect decisions. This not only increases confidence in your choices but also fosters buy-in from stakeholders and team members.

Cost Reduction

Analyzing quantitative data helps identify areas where costs can be reduced or optimized. For instance, if certain marketing campaigns yield lower-than-average results, reallocating resources to more effective channels can lead to cost savings and improved ROI.

Personalizing User Experience

Quantitative analysis allows for the mapping of customer journeys and the identification of preferences and behaviors. By understanding these patterns, businesses can tailor their offerings, content, and communication to specific user segments, leading to enhanced user satisfaction and engagement.

Improving User Satisfaction and Delight

Quantitative data analysis highlights areas of success and areas for improvement in products or services. For instance, if a webpage shows high engagement but low conversion rates, further investigation can uncover user pain points or friction in the conversion process. Addressing these issues can lead to improved user satisfaction and increased conversion rates.

Best 10 Quantitative Data Analysis Software

1. questionpro.

Known for its robust survey and research capabilities, QuestionPro is a versatile platform that offers powerful data analysis tools tailored for market research, customer feedback, and academic studies. With features like advanced survey logic, data segmentation, and customizable reports, QuestionPro empowers users to derive actionable insights from their quantitative data.

Features of QuestionPro

• Customizable Surveys
• Advanced Question Types:
• Survey Logic and Branching
• Data Segmentation
• Real-Time Reporting
• Mobile Optimization
• Integration Options
• Multi-Language Support
• Data Export
• User-friendly interface.
• Extensive question types.
• Seamless data export capabilities.
• Limited free version.

Pricing : 

Starts at $99 per month per user.

2. SPSS (Statistical Package for the Social Sciences

SPSS is a venerable software package widely used in the social sciences for statistical analysis. Its intuitive interface and comprehensive range of statistical techniques make it a favorite among researchers and analysts for hypothesis testing, regression analysis, and data visualization tasks.

• Advanced statistical analysis capabilities.
• Data management and manipulation tools.
• Customizable graphs and charts.
• Syntax-based programming for automation.
• Extensive statistical procedures.
• Flexible data handling.
• Integration with other statistical software package
• High cost for the full version.
• Steep learning curve for beginners.

Pricing: 

• Starts at $99 per month.

3. Google Analytics

Primarily used for web analytics, Google Analytics provides invaluable insights into website traffic, user behavior, and conversion metrics. By tracking key performance indicators such as page views, bounce rates, and traffic sources, Google Analytics helps businesses optimize their online presence and maximize their digital marketing efforts.

• Real-time tracking of website visitors.
• Conversion tracking and goal setting.
• Customizable reports and dashboards.
• Integration with Google Ads and other Google products.
• Free version available.
• Easy to set up and use.
• Comprehensive insights into website performance.
• Limited customization options in the free version.
• Free for basic features.

Hotjar is a powerful tool for understanding user behavior on websites and digital platforms. Hotjar enables businesses to visualize how users interact with their websites, identify pain points, and optimize the user experience for better conversion rates and customer satisfaction through features like heatmaps, session recordings, and on-site surveys.

• Heatmaps to visualize user clicks, taps, and scrolling behavior.
• Session recordings for in-depth user interaction analysis.
• Feedback polls and surveys.
• Funnel and form analysis.
• Easy to install and set up.
• Comprehensive insights into user behavior.
• Affordable pricing plans.
• Limited customization options for surveys.

Starts at $39 per month.

While not a dedicated data analysis software, Python is a versatile programming language widely used for data analysis, machine learning, and scientific computing. With libraries such as NumPy, pandas, and matplotlib, Python provides a comprehensive ecosystem for data manipulation, visualization, and statistical analysis, making it a favorite among data scientists and analysts.

• The rich ecosystem of data analysis libraries.
• Flexible and scalable for large datasets.
• Integration with other tools and platforms.
• Open-source with a supportive community.
• Free and open-source.
• High performance and scalability.
• Great for automation and customization.
• Requires programming knowledge.
• It is Free for the beginners.

6. SAS (Statistical Analysis System)

SAS is a comprehensive software suite renowned for its advanced analytics, business intelligence, and data management capabilities. With a wide range of statistical techniques, predictive modeling tools, and data visualization options, SAS is trusted by organizations across industries for complex data analysis tasks and decision support.

• Wide range of statistical procedures.
• Data integration and cleansing tools.
• Advanced analytics and machine learning capabilities.
• Scalable for enterprise-level data analysis.
• Powerful statistical modeling capabilities.
• Excellent support for large datasets.
• Trusted by industries for decades.
• Expensive licensing fees.
• Steep learning curve.
• Contact sales for pricing details.

Despite its simplicity compared to specialized data analysis software, Excel remains popular for basic quantitative analysis and data visualization. With features like pivot tables, functions, and charting tools, Excel provides a familiar and accessible platform for users to perform tasks such as data cleaning, summarization, and exploratory analysis.

• Formulas and functions for calculations.
• Pivot tables and charts for data visualization.
• Data sorting and filtering capabilities.
• Integration with other Microsoft Office applications.
• Widely available and familiar interface.
• Affordable for basic analysis tasks.
• Versatile for various data formats.
• Limited statistical functions compared to specialized software.
• Not suitable for handling large datasets.
• Included in Microsoft 365 subscription plans, starts at $6.99 per month.

8. IBM SPSS Statistics

Building on the foundation of SPSS, IBM SPSS Statistics offers enhanced features and capabilities for advanced statistical analysis and predictive modeling. With modules for data preparation, regression analysis, and survival analysis, IBM SPSS Statistics is well-suited for researchers and analysts tackling complex data analysis challenges.

• Advanced statistical procedures.
• Data preparation and transformation tools.
• Automated model building and deployment.
• Integration with other IBM products.
• Extensive statistical capabilities.
• User-friendly interface for beginners.
• Enterprise-grade security and scalability.
• Limited support for open-source integration.

Minitab is a specialized software package designed for quality improvement and statistical analysis in manufacturing, engineering, and healthcare industries. With tools for experiment design, statistical process control, and reliability analysis, Minitab empowers users to optimize processes, reduce defects, and improve product quality.

• Basic and advanced statistical analysis.
• Graphical analysis tools for data visualization.
• Statistical methods improvement.
• DOE (Design of Experiments) capabilities.
• Streamlined interface for statistical analysis.
• Comprehensive quality improvement tools.
• Excellent customer support.
• Limited flexibility for customization.

Pricing:  

• Starts at $29 per month.

JMP is a dynamic data visualization and statistical analysis tool developed by SAS Institute. Known for its interactive graphics and exploratory data analysis capabilities, JMP enables users to uncover patterns, trends, and relationships in their data, facilitating deeper insights and informed decision-making.

• Interactive data visualization.
• Statistical modeling and analysis.
• Predictive analytics and machine learning.
• Integration with SAS and other data sources.
• Intuitive interface for exploratory data analysis.
• Dynamic graphics for better insights.
• Integration with SAS for advanced analytics.
• Limited scripting capabilities.
• Less customizable compared to other SAS products.

Choose QuestionPro as Your Right Quantitative Data Analysis Software

QuestionPro offers a range of features specifically designed for quantitative data analysis, making it a suitable choice for various research, survey, and data-driven decision-making needs. Here’s why it might be the right fit for you:

Comprehensive Survey Capabilities

QuestionPro provides extensive tools for creating surveys with quantitative questions, allowing you to gather structured data from respondents. Whether you need Likert scale questions, multiple-choice questions, or numerical input fields, QuestionPro offers the flexibility to design surveys tailored to your research objectives.

Real-Time Data Analysis 

With QuestionPro’s real-time data collection and analysis features, you can access and analyze survey responses as soon as they are submitted. This enables you to quickly identify trends, patterns, and insights without delay, facilitating agile decision-making based on up-to-date information.

Advanced Statistical Analysis

QuestionPro includes advanced statistical analysis tools that allow you to perform in-depth quantitative analysis of survey data. Whether you need to calculate means, medians, standard deviations, correlations, or conduct regression analysis, QuestionPro offers the functionality to derive meaningful insights from your data.

Data Visualization

Visualizing quantitative data is crucial for understanding trends and communicating findings effectively. QuestionPro offers a variety of visualization options, including charts, graphs, and dashboards, to help you visually represent your survey data and make it easier to interpret and share with stakeholders.

Segmentation and Filtering 

QuestionPro enables you to segment and filter survey data based on various criteria, such as demographics, responses to specific questions, or custom variables. This segmentation capability allows you to analyze different subgroups within your dataset separately, gaining deeper insights into specific audience segments or patterns.

Cost-Effective Solutions

QuestionPro offers pricing plans tailored to different user needs and budgets, including options for individuals, businesses, and enterprise-level organizations. Whether conducting a one-time survey or needing ongoing access to advanced features, QuestionPro provides cost-effective solutions to meet your requirements.

Choosing the right quantitative data analysis software depends on your specific needs, budget, and level of expertise. Whether you’re a researcher, marketer, or business analyst, these top 10 software options offer diverse features and capabilities to help you unlock valuable insights from your data.

If you’re looking for a comprehensive, user-friendly, and cost-effective solution for quantitative data analysis, QuestionPro could be the right choice for your research, survey, or data-driven decision-making needs. With its powerful features, intuitive interface, and flexible pricing options, QuestionPro empowers users to derive valuable insights from their survey data efficiently and effectively.

So go ahead, explore QuestionPro, and empower yourself to unlock valuable insights from your data!

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

Related Articles

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods 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 collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources (40+ free sources), we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.

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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 to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

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 obtained 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 calculates 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 values in a dataset.
• 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.

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, a 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.

There 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 help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows 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 impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect 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. Data analysts use it to perform advanced risk analyses to help forecast future events and make 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 higher 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 times, 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

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng is a dynamic Machine Learning Engineer at Braln Ltd, where he pioneers the implementation of Deep Learning solutions and explores emerging technologies. His 9 years experience spans across roles such as System Analyst (DevOps) at Dagbs Nigeria Limited, and as a Full Stack Developer at Pedoquasphere International Limited. With a passion for bridging the gap between intricate technical concepts and accessible understanding, Ofem's work resonates with readers seeking insightful perspectives on data science, analytics, and cutting-edge technologies.

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The IBM® SPSS® Statistics software puts the power of advanced statistical analysis at your fingertips. Whether you are a beginner, an experienced analyst, a statistician or a business professional it offers a comprehensive suite of advanced capabilities, flexibility and usability that are not available in traditional statistical software.

With the user-friendly and intuitive interface of SPSS Statistics, you can easily manage and analyze large datasets, gaining actionable insights for fact-base decisions. Its advanced statistical procedures and modeling techniques enable you to optimize organizational strategies, including predicting customer behaviors, forecasting market trends, detecting fraud to minimize business risk and conducting reliable research to drive accurate conclusions.

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Prepare and analyze data through an intuitive user interface with drag-and-drop functionality, eliminating the need for writing codes.

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A Review of Software Tools for Quantitative Data Analysis

How to get started with statistical analysis

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
• Recommended Reading
• Archaeology

If you're a  sociology student or budding social scientist and have started to work with quantitative (statistical) data, analytic software will be very useful.

These programs force researchers to organize and clean their data and offer pre-programmed commands that allow everything from very basic to quite advanced forms of statistical analysis .

They even offer useful visualizations that will be useful as you seek to interpret data, and that you may wish to use when presenting it to others.

There are many programs on the market that are quite expensive. The good news for students and faculty is that most universities have licenses for at least one program students and professors can use.

Also, most programs offer a free, pared-down version of the full software package which will often suffice.

Here's a review of the three main programs that quantitative social scientists use.

Statistical Package for Social Science (SPSS)

SPSS is the most popular quantitative analysis software program used by social scientists.

Made and sold by IBM, it is comprehensive, flexible, and can be used with almost any type of data file. However, it is especially useful for analyzing large-scale survey data .

It can be used to generate tabulated reports, charts, and plots of distributions and trends, as well as generate descriptive statistics such as means, medians, modes and frequencies in addition to more complex statistical analyses like regression models.

SPSS provides a user interface that makes it easy and intuitive for all levels of users. With menus and dialogue boxes, you can perform analyses without having to write command syntax, like in other programs.

It is also simple and easy to enter and edit data directly into the program.

There are a few drawbacks, however, which might not make it the best program for some researchers. For example, there is a limit on the number of cases you can analyze. It is also difficult to account for weights, strata and group effects with SPSS.

STATA is an interactive data analysis program that runs on a variety of platforms. It can be used for both simple and complex statistical analyses.

STATA uses a point-and-click interface as well as command syntax, which makes it easy to use. STATA also makes it simple to generate graphs and plots of data and results.

Analysis in STATA is centered around four windows:

• command window
• review window
• result window
• variable window

Analysis commands are entered into the command window and the review window records those commands. The variables window lists the variables that are available in the current data set along with the variable labels, and the results appear in the results window.

SAS, short for Statistical Analysis System, is also used by many businesses.

In addition to statistical analysis, it also allows programmers to perform report writing, graphics, business planning, forecasting, quality improvement, project management and more.

SAS is a great program for the intermediate and advanced user because it is very powerful; it can be used with extremely large datasets and can perform complex and advanced analyses.

SAS is good for analyses that require you to take into account weights, strata, or groups.

Unlike SPSS and STATA, SAS is run largely by programming syntax rather than point-and-click menus, so some knowledge of the programming language is required.

Other Programs

Other programs popular with sociologists include:

• R: Free to download and use. You can add your own programs to it if you are familiar with statistics and programming.
• NVio: "It helps researchers organize and analyze complex non-numerical or unstructured data, both text and multimedia," according to UCLA Library .
• MATLAB: Provides "Simulations, Multidimensional Data, Image and Signal Processing," according to NYU Libraries .
• Null Hypothesis Examples
• Understanding Path Analysis
• Data Cleaning for Data Analysis in Sociology
• The 7 Best Programming Languages to Learn for Beginners
• What Is Quantitative Data?
• Cluster Analysis and How Its Used in Research
• Pros and Cons of Secondary Data Analysis
• Qualitative Data Definition and Examples
• An Overview of Qualitative Research Methods
• 7 Graphs Commonly Used in Statistics
• Correlation Analysis in Research
• Linear Regression Analysis
• Glossary of Visual Basic Terms
• Your Comprehensive Guide to a Painless Undergrad Econometrics Project
• Benefits of the Graphical User Interface

Quantitative research

Objectives and applications

Quantitative research methods, choosing a quantitative research design, software for quantitative research.

Quantitative and qualitative research are commonly considered differing fundamentally. Yet, their objectives, as well as their applications, overlap in numerous ways. Quantitative Research is considered to have as its main purpose the quantification of data. This allows generalizations of results from a sample to an entire population of interest and the measurement of the incidence of various views and opinions in a given sample.

Yet, quantitative research is not infrequently followed by qualitative research, which aims to explore select findings further. Qualitative research is considered particularly suitable for gaining an in-depth understanding of underlying reasons and motivations. It provides insights into the setting of a problem. At the same time, it frequently generates ideas and hypotheses for later quantitative research.

Quantitative research measures the frequency or intensity of a phenomenon or its distribution, hypotheses can be tested, and insights inferred. At the beginning of the research process, theories about the facts under investigation have already been proposed, from which hypotheses are derived. The actual data are then collected by quantitative methods. In the social sciences, often, these are surveys using questionnaires or experiments. Statistical methods are used to dissect and evaluate the data, often using control groups. The research process results are then, in turn, related to the previously established theories and interpreted.

The advantages of quantitative research are high reliability, fast processing of large amounts of data, and high comparability. There are several methods of quantitative research:

• standardized surveys
• standardized observations
• experiments and trials
• quantitative content analysis

Analysis of ideas, actions, and values made possible with ATLAS.ti.

Turn your data into key insights with our powerful tools. Download a free trial today.

The research design is composed of:

• Type of research
• Data collection
• Data description
• Method of analysis

Which method of data collection and analysis is suitable depends on the research questions.

A distinction can be made between dependent and independent variables in quantitative research. Independent variables are considered to have an effect on other variables in the research context. They influence the dependent variable(s). Regression analysis can be run to determine whether an independent variable has an effect. For example, one can examine the bathing time (dependent variable) of swimming pool guests as a function of the water temperature (independent variable).

Correlational analysis can be used to determine whether two variables are related, but no cause and effect relationship can be established. For example, it has been observed that more children are born in places where many storks live. This however does not mean that storks deliver babies. The simple explanation for this observation is that birth rates are higher in the countryside, and storks also prefer to live in this environment.

Quantitative research, predominantly statistical analysis, is common in the social sciences. Many software programs designed for use with quantitative data are available today. The main requirements for such packages are that they are comprehensive and flexible. A useful statistical software tool can generate tabulated reports, charts, and plots of distributions and trends and generate descriptive statistics and more complex statistical analyses. Lastly, a user interface that makes it very easy and intuitive for all levels of users is a must.

Examples of statistical analysis software are SPSS, Excel, SAS, or R. The presentation of results of studies usually takes place in the form of tables or graphs.

Suppose you have used ATLAS.ti for analyzing qualitative data. If your sample is sufficiently large, and you want to confirm results via statistical procedures, you can export your ATLAS.ti data for use in SPSS, Excel, SAS, or R. ATLAS.it offers two output options - an SPSS syntax file or a generic Excel file for input in any statistical software. Each coded data segment becomes a case, and each code and code group a variable.

Analyze transcripts, notes, and more with ATLAS.ti

Intuitive tools to help you with your research. Check them out with a free trial of ATLAS.ti.

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How to Choose Data Analysis Software

Quantitative analysis software.

• Qualitative Analysis Software

UO Available Software

Open source software.

• Data Resources

Librarian for Research Data Management and Reproducibility

The University of Oregon provides access to a number of quantitative analysis software for current students and faculty. Some software may be limited by department. More information may be found by viewing the UO Available Software page. Current software includes: 

• Microsoft Power BI
• SPSS and SPSS Amos
• STATA (on PLC computers and possibly through some departments)
• Power BI Desktop

In addition to UO Available Software, we would like to recommend the use of open source (or free) quantitative analysis software. More information may be found by viewing the Open Source Software page. Feedback is provided on the following:

• Python and JupyterLab
• R and R-Studio
• Tableau Public

Quantitative Analysis Resources

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The ultimate guide to quantitative data analysis

Numbers help us make sense of the world. We collect quantitative data on our speed and distance as we drive, the number of hours we spend on our cell phones, and how much we save at the grocery store.

Our businesses run on numbers, too. We spend hours poring over key performance indicators (KPIs) like lead-to-client conversions, net profit margins, and bounce and churn rates.

But all of this quantitative data can feel overwhelming and confusing. Lists and spreadsheets of numbers don’t tell you much on their own—you have to conduct quantitative data analysis to understand them and make informed decisions.

Last updated

Reading time.

This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis

Quantitative Analysis Guide: R

• Finding Data
• Which Statistical Software to Use?
• Merging Data Sets
• Reshaping Data Sets
• Choose Statistical Test for 1 Dependent Variable
• Choose Statistical Test for 2 or More Dependent Variables

• Data Services Home Page
• Statistical Software Comparison
• What statistical test to use?
• Data Visualization Resources
• Data Analysis Examples External (UCLA) examples of regression and power analysis
• Supported software
• Request a consultation
• Making your code reproducible

R Software Access

• R is a free open source statistical software which can be downloaded through  CRAN . RStudio is a popular interface which runs R code and can be be  downloaded  to be used as an alternative to the R interface. To run RStudio, R needs to be downloaded first.
• R is installed in several  computer labs  on campus, including  Data Services  workstations located on the 5th floor of Bobst Library.
• NYU students have free access to R through NYU  Virtual Computing Lab  (VCL).

Data Services R Tutorials

R learning resources.

• Video Instruction
• Official Documentation
• Featured Web Resources
• Library Books
• Learning R An almost 3 hour introductory tutorial to R
• R Statistics Essential Training A 6 hour detailed tutorial focusing on using R for basic statistics
• Code Clinic: R Explore solutions to common R programming challenges
• CRAN: The R Manuals
• Quick-R A great quick reference that covers many common topics in R.
• RStudio Online Learning Resources provided by the RStudio team which cover the basics of R programming and other tools the RStudio team has developed.
• UCLA Statistical Computing (R) A variety of learning modules, FAQ and case examples of using R for statistical computing.
• Rdocumentation.org Search through all available R packages on CRAN, Github and Bioconductor.
• R Reference Card A pdf guide that highlights important commands under several main topics in R programming.
• Cookbook for R Provides examples of common problems and their solutions in R.
• Advanced R Hadley Wickham's reference for more advanced R users who want to improve their R programming skills.
• R-bloggers A collection of articles and blogs from around the R community.
• Code School Interactive exercises for R beginners.
• Handling and Processing Strings in R Gaston Sanchez's guide to handling strings in R.
• swirl Interactive courses through the swirl package.
• R Studio Cheatsheets Cheatsheets created by the R Studio team which go over topics such as shiny, R Markdown and dplyr.
• R Tutorial R tutorial from Clarkson University.
• R for Data Science An online book with examples and exercises comprehensively covering basic and intermediate topics in R.

NYU Library databases likely to contain relevant resources:

• Ebook Central This link opens in a new window Ebook Central is NYU's preferred ebook provider. Users can search, read, highlight, and annotate full-text books in many subject areas, including the social sciences and humanities.
• Skillsoft Books (formerly Books24x7) This link opens in a new window Skillsoft Books (formerly Books24x7) is an online collection of computer technology-related ebooks. It contains hundreds of books and videos from respected IT publishers such as MIT Press, Microsoft Press, Osborne/McGraw-Hill, Que, Sams, Sybex and Wiley. Use it to search for a wide variety of books and videos, ranging from beginners level to advanced (Microsoft Word for beginners or an advanced programming language).
• O'Reilly Online Learning This link opens in a new window O'Reilly's Safari Books Online provides access to ebooks related to technology, coding, developing, web design, and data visualization. More information less... If database is asking for "Sign In" information for content access, please refresh browser cache and cookies, and try the link again.

Selected books on the topic (available online):

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R & Web Accessibility

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Quantitative Data Analysis

How to install, learning resources.

• Quant Data & Methods Resources

SPSS is a software package used for statistical analysis. It can

• take data from almost any type of file
• generate tabulated reports
• plot distributions and trends
• create charts and graphs
• perform descriptive and complex statistical analyses

JMU community members can download SPSS to their computing resources.

• PC Instructions
• Mac Instructions
• SPSS Statistics Essential Training In this course, Barton Poulson takes a practical, visual, and non-mathematical approach to SPSS Statistics, explaining how to use the popular program to analyze data. This course is ideal for first-time researchers and those who want to make the most of data in their professional and academic work.
• SPSS for Academic Research Topics include t-tests, analysis of variance (ANOVA), and understanding the statistical measurements behind academic research. Yash Patel provides some general guidelines and assumptions, along with a challenge and solution exercise to practice what you've learned.
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• Our foundation

Quantitative Data Analysis Software

Quantitative data analysis software is a tool that market researchers need to gather, analyse, and present the results of their research in an easy-to-understand manner. Without this type of software, any resulting graphs or charts wouldn’t be able to communicate their information effectively. The bottom line is that quantitative data analysis software gives the user the ability to conduct accurate and precise research so they can make well-informed decisions based on hard facts rather than speculation. If you’re interested in learning more about this type of software and why it’s essential to use, continue reading to learn more!

What is quantitative data analysis?

One of the most common quantitative data analysis techniques is market research, where statistical software analyses your current customer base and market trends to predict where your company should invest its time and resources. By quantifying information, researchers can answer questions such as what the company’s marketing budget should be, how many people have stopped coming to the store, or why people think the product isn’t working for them. 

Related Post : Quantitative Market Research Tools

Furthermore, quantitative research analysis software provides answers much faster than qualitative studies. Qualitative surveys can take days, weeks, or even months to return results because they require more research by humans than quantitative surveys do. The more specific the question you want answered, the quicker you’ll get an answer with quantitative data analysis. Quantitative research analysis software also allows for analyses that span several years’ worth of data instead of just focusing on one year at a time like qualitative research does.

What do you need to use it for?

A person’s analytical skill set is just as important to the success of a company as their marketing knowhow. Without analysing qualitative data, it is difficult to get a complete understanding of how well your product performs in the market. To do this, it is necessary to use software for analysing quantitative data.  That said, there are two types of software for analysing quantitative data: software’s that are designed to collect and analyse the raw data by themselves, and software’s that require additional analysis tools (such as Microsoft Excel) before being able to process them. When choosing software applications for quantitative analysis, you should consider what type of computer system you’re using and what features it has; in some cases, you may need software that supports 64-bit architecture or has built-in programming languages. You also need to consider if the software can automatically filter out low-value participants from your dataset – if not, then filtering out low-value participants manually can be time consuming and complicated.

Related Post : Insights Communities

Which type of report do you need?

Statistical software packages can come in handy for conducting a market research study, or to evaluate survey data. Quantitative analysis software offers different types of reports that allow you to quickly digest and analyse your data. The following are some pros and cons for using this type of software when conducting a market research study.

Statistical software packages offer many benefits over paper-based analysis methods, such as automatically calculating statistics and generating custom summaries. However, these packages often require a large amount of upfront investment and training before they can be used efficiently. Despite the initial cost, statistical software is more cost effective than other data collection techniques such as focus groups because it does not require any additional resources. 

The ability to conduct sophisticated analyses without leaving the office makes software used in quantitative research an excellent choice for those who work remotely. Additionally, quantitative analysis software can help businesses save time by speeding up data processing. In addition to providing advantages for businesses, this type of software also has advantages for academic researchers due to its compatibility with Excel spreadsheets and tools like R programming language. 

However, there are still some drawbacks to consider with this type of quantitative analysis software – namely their high price tag and the difficulty in finding qualified professionals who know how to use them properly. These disadvantages make it difficult for those on a budget or less experienced users.

Excited to start using a software application for quantitative analysis? Schedule a demo here and learn how quantitative data analysis software can improve your research projects.

Discover the power of online qualitative research. Order the free demo

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Statistical Software Guides and Tutorials

• Sage Research Methods Core Collection This link opens in a new window A collection of e-books and other resources covering research methods in the social and behavioral sciences. It contains the popular Little Green Book series as well as other titles on quantitative analysis
• NUIT Research Data Services: Training and Learning NUIT offers data analysis training through workshops and online learning.
• LinkedIn Learning Northwestern provides faculty, staff, and students with access to this suite of online courses.

Online Courses

• IBM's SPSS User Guide
• SPSS Tutorials

Online Tutorials

• Stata Documentation The official user guide, along with manuals and examples for using specific statistical methods in Stata.
• Stata Learning Modules Beginner-friendly guide to Stata from UCLA's Advanced Research Computing.

• SAS Learning Modules Beginner-friendly guide to SAS from UCLA's Advanced Research Computing.

• Google's Python Class Unlike R, Python is a general-purpose programming language. This site offers a more general introduction to Python, which you may want for background knowledge before moving on to using Python for data analysis.

Accessing Software

Open-source software.

Both R and Python are free and open source. NUIT's Research Data Services offers installation guidelines:

• I nstalling R and RStudio
• Installing Python and Jupyter

Proprietary Software

Northwestern provides access to licensed software in the  library computer labs and on NUWorkspace , a virtual desktop. NUIT also makes  free or discounted software licenses available. In addition to these campus-wide resources, your department may have software licenses you can access.

Sarah Thorngate

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Data Analysis: Quantitative Data Analysis Software

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Page Contents

Books on SPSS are categorized in the library catalog under the following subject heading classifications:

• SPSS for Windows
• SPSS Computer File

Online SPSS Tutorial

• Resources to help you learn and use SPSS This web site by UCLA provides resources to learn SPSS. It includes a SPSS Starter Kit, which has a movie tutorial or class showing SPSS in action. There are also more advanced resources.
• IBM SPSS Support Home Official support and documentation from IBM.

Sample titles from the library catalog:

Books on Stata are categorized in the library catalog under the following subject classifications:

• Stata Handbooks Manuals Etc

Online Stata Tutorial:

• Resources to help you learn and use Stata This web site by UCLA provides resources to learn Stata. It includes a Stata Starter Kit, which has a movie tutorial or class showing Stata in action. There are also more advanced resources.

Books on SAS are categorized in the library catalog under the following subject classifications:

• SAS (Computer file)
• SAS (Computer program)
• SAS (Computer program language)

Online SAS Tutorial:

• Resources to help you learn and use SAS This web site by UCLA provides resources to learn SAS. It includes a SAS Starter Kit, which has a movie tutorial or class showing SAS in action. There are also more advanced resources.

Books on R are categorized in the library catalog under the following subject classifications:

• R (Computer program language)
• R (Computer program language) -- Handbooks, manuals, etc.

Online R Tutorial:

• R Workshop Materials This guide from Rutgers University Libraries provides helpful materials to help you learn R, learning links, and other tips.
• Resources to help you learn and use R This web site by UCLA provides resources to learn R. It includes a R Starter Kit, which has a movie tutorial or class showing R in action. There are also more advanced resources.

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Quantitative Data Analysis Software

How to use quantitative data analysis software.

Data analysis differentiates the scientist from the general population. That could be because it guides him from making largely unsubstantiated claims or remarks resulting perhaps from chance occurrence. Therefore, use the tools for analyzing both quantitative data. It is essential for a researcher to present a data as a meaningful piece of information.

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As your all-in-one Questionnaire Analysis Software, MAXQDA can be used to manage your entire research project. Whether your survey contains standardized or open-ended questions, with MAXQDA you can easily import and analyze both types. Import your questionnaire as a text document, spreadsheet, SPSS file, or from your online survey tool with just one click. For analyzing your survey MAXQDA offers a variety of tools that are tailor-made for your questionnaire analysis. Because MAXQDA is a Questionnaire Analysis Software designed by and for researchers, it offers an entire add-on module, called MAXQDA Stats, with tools for performing statistical analyses, making your questionnaire analysis surprisingly easy.

Code and retrieve questionnaire data

MAXQDA is a Questionnaire Analysis Software that is easy-to-use and can be used to mark important information in your data with different codes, colors, symbols, and emoticons. Because time is precious, you can create codes with just one click and apply them to your data quickly via drag & drop. MAXQDA as the #1 Questionnaire Analysis Software offers Text Search tools that allow you to explore your material without coding or reading them first. Search for keywords and automatically code them with just a few clicks. Organize your thoughts and theories in memos that can be linked to any element of your project. Retrieve your coded segments with one click or use MAXQDA’s powerful summary tools to test and develop new theories.

Easily import your questionnaire data

As an all-in-one Questionnaire Analysis Software, MAXQDA facilitates each step of your questionnaire analysis – starting with the data import. Connect MAXQDA to SurveyMonkey to directly import your online survey into your MAXQDA project in order to analyze the data with MAXQDA’s tools. You can also import your questionnaire data as a text document, spreadsheet, or SPSS file. Of course, MAXQDA works with both types of answers, open-ended as well as standardized, and offers a variety of tools that are tailor-made for analyzing open-ended as well as standardized survey responses.

Organize your data and codes

When conducting a questionnaire analysis, you can easily get lost. But with MAXQDA as your questionnaire analysis software, you will never lose track of the bigger picture. Organize your questionnaire data in groups, link relevant quotes to each other, and share and compare work with your team members. Organize your thoughts and theories in memos and link these memos to any element of your project. Retrieve your coded segments with one click or use MAXQDA’s powerful summary tools to test and develop new theories. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Categorize questionnaire data

Analyzing questionnaires can be overwhelming but with MAXQDA you have an easy-to-use software solution. Because MAXQDA is a Questionnaire Analysis Software developed by and for a researcher it offers a variety of tools that are tailor-made for your questionnaire analysis. Use the categorize survey data tool to analyze responses to open questions efficiently and effectively with its interactive table window for categorizing survey data, or use MAXQDA’s add-on module, called MAXQDA Stats, with tools for performing statistical analyses. As the number one software for mixed methods research, MAXQDA includes tools with which you can easily combine qualitative and quantitative methods to get an even deeper insight into your data.

Quantitative evaluation of themes

Questionnaire analyses are completed by researchers all around the world. Because MAXQDA is a Questionnaire Analysis Software designed by and for researchers, it offers an entire add-on module with tools specifically designed to facilitate quantitative content analyses. Included are tools for word frequency analysis, visual text exploration, content analysis, vocabulary analysis, dictionary-based analysis, and more that facilitate the quantitative analysis of your survey responses. You can display frequencies of individual words or word combinations in tables or visualize them in the Interactive Wordtree, use the Keyword-in-Context function to transfer the textual contexts of selected keywords into a clear table, and much more.

Visualize your questionnaire data

As an all-in-one Questionnaire Analysis Software, MAXQDA offers a variety of visual tools that are tailor-made for analyzing open-ended as well as standardized surveys. Create stunning visualizations to analyze your material. Of course, you can export your visualizations in various formats to enrich your final report. Visualize the progression of an interview with the Codeline, use the Code Cloud to illustrate and explore your categories, or make use of the graphical representation possibilities of MAXMaps, which in particular permit the creation of concept maps. Thanks to the interactive connection between your visualizations with your MAXQDA data, you’ll never lose sight of the big picture.

AI Assist: questionnaire analysis meets AI

AI Assist – your virtual research assistant – supports your questionnaire analysis with various tools. Besides automatic transcription of audio and video recordings in different languages, AI Assist simplifies your work by automatically analyzing and summarizing elements of your research project and by generating suggestions for subcodes. No matter which AI tool you use – you can customize your results to suit your needs.

Free tutorials and guides on questionnaire analysis

MAXQDA offers a variety of free learning resources for questionnaire analysis, making it easy for both beginners and advanced users to learn how to use the software. From free video tutorials and webinars to step-by-step guides and sample projects, these resources provide a wealth of information to help you understand the features and functionality of MAXQDA for questionnaire analysis. For beginners, the software’s user-friendly interface and comprehensive help center make it easy to get started with your data analysis, while advanced users will appreciate the detailed guides and tutorials that cover more complex features and techniques. Whether you’re just starting out or are an experienced researcher, MAXQDA’s free learning resources will help you get the most out of your questionnaire analysis.

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Introduction

Listed below are some good examples of high-performance CAQDAS platforms that are free of charge. Some have graphical user interfaces (GUI) and others do not. Some investigation will be required to determine what will best suit your needs. Note that two of these free programs, Aquad and RQDA, make use of the powerful statistical analysis package R. While there is only a plug-in for Aquad, you will need to install R to use RQDA. R is free to download and supported by a vast user community. 

Free CAQDAS Software

• AQUAD Aquad is a platform developed in Germany (you will have to translate the website, which is also in German) that supports text of any kind, audio, video, and image files. There is a plugin available to use with R, the open source statistical analysis software.
• qcoder qcoder is an R library for qualitative analysis hosted on GitHub.
• QDA Miner Lite A "lite" version of the proprietary software, QDA Miner Lite has a slightly limited functionality. You can still perform the basic functions of the full version but don't have some of the more advanced options. You can see all the differences in the side by side comparison.
• RQDA RQDA is another package from the R Project, a statistical analysis platform that has developed a constantly growing community of users and developers that is designing all sorts of software for use in data analysis. What a plugin to such a power statistics program means is that implementing quantitative data to your project will be seamless and effective.
• Taguette Taguette is an open source qualitative analysis program that works on Windows, Mac, and Linux computers, as well as in-browser. A quick tutorial on how to get started can be found at their website, linked above. You must have Python 3 and Calibre on your computer in order to successfully download and use Taguette. The online version requires you to register for a free account.
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• Last Updated: May 9, 2024 2:24 PM
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Release Summary

At ASMS 2024, SCIEX, in collaboration with Mass Analytica, presents artificial intelligence quantitation (AI quant) software

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