tools used for research analysis

Data Analysis Techniques in Research – Methods, Tools & Examples

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Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Top 21 must-have digital tools for researchers

Last updated

12 May 2023

Reviewed by

Jean Kaluza

Research drives many decisions across various industries, including:

Uncovering customer motivations and behaviors to design better products

Assessing whether a market exists for your product or service

Running clinical studies to develop a medical breakthrough

Conducting effective and shareable research can be a painstaking process. Manual processes are sluggish and archaic, and they can also be inaccurate. That’s where advanced online tools can help. 

The right tools can enable businesses to lean into research for better forecasting, planning, and more reliable decisions. 

• Why do researchers need research tools?

Research is challenging and time-consuming. Analyzing data , running focus groups , reading research papers , and looking for useful insights take plenty of heavy lifting. 

These days, researchers can’t just rely on manual processes. Instead, they’re using advanced tools that:

Speed up the research process

Enable new ways of reaching customers

Improve organization and accuracy

Allow better monitoring throughout the process

Enhance collaboration across key stakeholders

• The most important digital tools for researchers

Some tools can help at every stage, making researching simpler and faster.

They ensure accurate and efficient information collection, management, referencing, and analysis. 

Some of the most important digital tools for researchers include:

Research management tools

Research management can be a complex and challenging process. Some tools address the various challenges that arise when referencing and managing papers. 

.css-10ptwjf{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;background:transparent;border:0;color:inherit;cursor:pointer;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-text-decoration:underline;text-decoration:underline;}.css-10ptwjf:disabled{opacity:0.6;pointer-events:none;} Zotero

Coined as a personal research assistant, Zotero is a tool that brings efficiency to the research process. Zotero helps researchers collect, organize, annotate, and share research easily. 

Zotero integrates with internet browsers, so researchers can easily save an article, publication, or research study on the platform for later. 

The tool also has an advanced organizing system to allow users to label, tag, and categorize information for faster insights and a seamless analysis process. 

Messy paper stacks––digital or physical––are a thing of the past with Paperpile. This reference management tool integrates with Google Docs, saving users time with citations and paper management. 

Referencing, researching, and gaining insights is much cleaner and more productive, as all papers are in the same place. Plus, it’s easier to find a paper when you need it. 

Acting as a single source of truth (SSOT), Dovetail houses research from the entire organization in a simple-to-use place. Researchers can use the all-in-one platform to collate and store data from interviews , forms, surveys , focus groups, and more. 

Dovetail helps users quickly categorize and analyze data to uncover truly actionable insights . This helps organizations bring customer insights into every decision for better forecasting, planning, and decision-making. 

Dovetail integrates with other helpful tools like ​Slack, Atlassian, Notion, and Zapier for a truly efficient workflow.

Putting together papers and referencing sources can be a huge time consumer. EndNote claims that researchers waste 200,000 hours per year formatting citations. 

To address the issue, the tool formats citations automatically––simultaneously creating a bibliography while the user writes. 

EndNote is also a cloud-based system that allows remote working, multiple-user interaction and collaboration, and seamless working on different devices. 

Information survey tools

Surveys are a common way to gain data from customers. These tools can make the process simpler and more cost-effective. 

With ready-made survey templates––to collect NPS data, customer effort scores , five-star surveys, and more––getting going with Delighted is straightforward. 

Delighted helps teams collect and analyze survey feedback without needing any technical knowledge. The templates are customizable, so you can align the content with your brand. That way, the survey feels like it’s coming from your company, not a third party. 

SurveyMonkey

With millions of customers worldwide, SurveyMonkey is another leader in online surveys. SurveyMonkey offers hundreds of templates that researchers can use to set up and deploy surveys quickly. 

Whether your survey is about team performance, hotel feedback, post-event feedback, or an employee exit, SurveyMonkey has a ready-to-use template. 

Typeform offers free templates you can quickly embed, which comes with a point of difference: It designs forms and surveys with people in mind, focusing on customer enjoyment. 

Typeform employs the ‘one question at a time’ method to keep engagement rates and completions high. It focuses on surveys that feel more like conversations than a list of questions.

Web data analysis tools

Collecting data can take time––especially technical information. Some tools make that process simpler. 

For those conducting clinical research, data collection can be incredibly time-consuming. Teamscope provides an online platform to collect and manage data simply and easily. 

Researchers and medical professionals often collect clinical data through paper forms or digital means. Those are too easy to lose, tricky to manage, and challenging to collaborate on. 

With Teamscope, you can easily collect, store, and electronically analyze data like patient-reported outcomes and surveys. 

Heap is a digital insights platform providing context on the entire customer journey . This helps businesses improve customer feedback , conversion rates, and loyalty. 

Through Heap, you can seamlessly view and analyze the customer journey across all platforms and touchpoints, whether through the app or website. 

Another analytics tool, Smartlook, combines quantitative and qualitative analytics into one platform. This helps organizations understand user behavior and make crucial improvements. 

Smartlook is useful for analyzing web pages, purchasing flows, and optimizing conversion rates. 

Project management tools

Managing multiple research projects across many teams can be complex and challenging. Project management tools can ease the burden on researchers. 

Visual productivity tool Trello helps research teams manage their projects more efficiently. Trello makes product tracking easier with:

A range of workflow options

Unique project board layouts

Advanced descriptions

Integrations

Trello also works as an SSOT to stay on top of projects and collaborate effectively as a team. 

To connect research, workflows, and teams, Airtable provides a clean interactive interface. 

With Airtable, it’s simple to place research projects in a list view, workstream, or road map to synthesize information and quickly collaborate. The Sync feature makes it easy to link all your research data to one place for faster action. 

For product teams, Asana gathers development, copywriting, design, research teams, and product managers in one space. 

As a task management platform, Asana offers all the expected features and more, including time-tracking and Jira integration. The platform offers reporting alongside data collection methods , so it’s a favorite for product teams in the tech space.

Grammar checker tools

Grammar tools ensure your research projects are professional and proofed. 

No one’s perfect, especially when it comes to spelling, punctuation, and grammar. That’s where Grammarly can help. 

Grammarly’s AI-powered platform reviews your content and corrects any mistakes. Through helpful integrations with other platforms––such as Gmail, Google Docs, Twitter, and LinkedIn––it’s simple to spellcheck as you go. 

Another helpful grammar tool is Trinka AI. Trinka is specifically for technical and academic styles of writing. It doesn’t just correct mistakes in spelling, punctuation, and grammar; it also offers explanations and additional information when errors show. 

Researchers can also use Trinka to enhance their writing and:

Align it with technical and academic styles

Improve areas like syntax and word choice

Discover relevant suggestions based on the content topic

Plagiarism checker tools

Avoiding plagiarism is crucial for the integrity of research. Using checker tools can ensure your work is original. 

Plagiarism checker Quetext uses DeepSearch™ technology to quickly sort through online content to search for signs of plagiarism. 

With color coding, annotations, and an overall score, it’s easy to identify conflict areas and fix them accordingly. 

Duplichecker

Another helpful plagiarism tool is Duplichecker, which scans pieces of content for issues. The service is free for content up to 1000 words, with paid options available after that. 

If plagiarism occurs, a percentage identifies how much is duplicate content. However, the interface is relatively basic, offering little additional information.  

Journal finder tools

Finding the right journals for your project can be challenging––especially with the plethora of inaccurate or predatory content online. Journal finder tools can solve this issue. 

Enago Journal Finder

The Enago Open Access Journal Finder sorts through online journals to verify their legitimacy. Through Engao, you can discover pre-vetted, high-quality journals through a validated journal index. 

Enago’s search tool also helps users find relevant journals for their subject matter, speeding up the research process. 

JournalFinder

JournalFinder is another journal tool that’s popular with academics and researchers. It makes the process of discovering relevant journals fast by leaning into a machine-learning algorithm.

This is useful for discovering key information and finding the right journals to publish and share your work in. 

Social networking for researchers

Collaboration between researchers can improve the accuracy and sharing of information. Promoting research findings can also be essential for public health, safety, and more. 

While typical social networks exist, some are specifically designed for academics.

ResearchGate

Networking platform ResearchGate encourages researchers to connect, collaborate, and share within the scientific community. With 20 million researchers on the platform, it's a popular choice. 

ResearchGate is founded on an intention to advance research. The platform provides topic pages for easy connection within a field of expertise and access to millions of publications to help users stay up to date. 

Academia is another commonly used platform that connects 220 million academics and researchers within their specialties. 

The platform aims to accelerate research with discovery tools and grow a researcher’s audience to promote their ideas. 

On Academia, users can access 47 million PDFs for free. They cover topics from mechanical engineering to applied economics and child psychology. 

• Expedited research with the power of tools

For researchers, finding data and information can be time-consuming and complex to manage. That’s where the power of tools comes in. 

Manual processes are slow, outdated, and have a larger potential for inaccuracies. 

Leaning into tools can help researchers speed up their processes, conduct efficient research, boost their accuracy, and share their work effectively. 

With tools available for project and data management, web data collection, and journal finding, researchers have plenty of assistance at their disposal.

When it comes to connecting with customers, advanced tools boost customer connection while continually bringing their needs and wants into products and services.

What are primary research tools?

Primary research is data and information that you collect firsthand through surveys, customer interviews, or focus groups. 

Secondary research is data and information from other sources, such as journals, research bodies, or online content. 

Primary researcher tools use methods like surveys and customer interviews. You can use these tools to collect, store, or manage information effectively and uncover more accurate insights. 

What is the difference between tools and methods in research?

Research methods relate to how researchers gather information and data. 

For example, surveys, focus groups, customer interviews, and A/B testing are research methods that gather information. 

On the other hand, tools assist areas of research. Researchers may use tools to more efficiently gather data, store data securely, or uncover insights. 

Tools can improve research methods, ensuring efficiency and accuracy while reducing complexity.

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Statistical Methods for Data Analysis: a Comprehensive Guide

In today’s data-driven world, understanding statistical methods for data analysis is like having a superpower.

Whether you’re a student, a professional, or just a curious mind, diving into the realm of data can unlock insights and decisions that propel success.

Statistical methods for data analysis are the tools and techniques used to collect, analyze, interpret, and present data in a meaningful way.

From businesses optimizing operations to researchers uncovering new discoveries, these methods are foundational to making informed decisions based on data.

In this blog post, we’ll embark on a journey through the fascinating world of statistical analysis, exploring its key concepts, methodologies, and applications.

Introduction to Statistical Methods

At its core, statistical methods are the backbone of data analysis, helping us make sense of numbers and patterns in the world around us.

Whether you’re looking at sales figures, medical research, or even your fitness tracker’s data, statistical methods are what turn raw data into useful insights.

But before we dive into complex formulas and tests, let’s start with the basics.

Data comes in two main types: qualitative and quantitative data .

Quantitative data is all about numbers and quantities (like your height or the number of steps you walked today), while qualitative data deals with categories and qualities (like your favorite color or the breed of your dog).

And when we talk about measuring these data points, we use different scales like nominal, ordinal , interval , and ratio.

These scales help us understand the nature of our data—whether we’re ranking it (ordinal), simply categorizing it (nominal), or measuring it with a true zero point (ratio).

In a nutshell, statistical methods start with understanding the type and scale of your data.

This foundational knowledge sets the stage for everything from summarizing your data to making complex predictions.

Descriptive Statistics: Simplifying Data

Imagine you’re at a party and you meet a bunch of new people.

When you go home, your roommate asks, “So, what were they like?” You could describe each person in detail, but instead, you give a summary: “Most were college students, around 20-25 years old, pretty fun crowd!”

That’s essentially what descriptive statistics does for data.

It summarizes and describes the main features of a collection of data in an easy-to-understand way. Let’s break this down further.

The Basics: Mean, Median, and Mode

• Mean is just a fancy term for the average. If you add up everyone’s age at the party and divide by the number of people, you’ve got your mean age.
• Median is the middle number in a sorted list. If you line up everyone from the youngest to the oldest and pick the person in the middle, their age is your median. This is super handy when someone’s age is way off the chart (like if your grandma crashed the party), as it doesn’t skew the data.
• Mode is the most common age at the party. If you notice a lot of people are 22, then 22 is your mode. It’s like the age that wins the popularity contest.

Spreading the News: Range, Variance, and Standard Deviation

• Range gives you an idea of how spread out the ages are. It’s the difference between the oldest and the youngest. A small range means everyone’s around the same age, while a big range means a wider variety.
• Variance is a bit more complex. It measures how much the ages differ from the average age. A higher variance means ages are more spread out.
• Standard Deviation is the square root of variance. It’s like variance but back on a scale that makes sense. It tells you, on average, how far each person’s age is from the mean age.

Picture Perfect: Graphical Representations

• Histograms are like bar charts showing how many people fall into different age groups. They give you a quick glance at how ages are distributed.
• Bar Charts are great for comparing different categories, like how many men vs. women were at the party.
• Box Plots (or box-and-whisker plots) show you the median, the range, and if there are any outliers (like grandma).
• Scatter Plots are used when you want to see if there’s a relationship between two things, like if bringing more snacks means people stay longer at the party.

Why Descriptive Statistics Matter?

Descriptive statistics are your first step in data analysis.

They help you understand your data at a glance and prepare you for deeper analysis.

Without them, you’re like someone trying to guess what a party was like without any context.

Whether you’re looking at survey responses, test scores, or party attendees, descriptive statistics give you the tools to summarize and describe your data in a way that’s easy to grasp.

This approach is crucial in educational settings, particularly for enhancing math learning outcomes. For those looking to deepen their understanding of math or seeking additional support, check out this link:  https://www.mathnasium.com/ math-tutors-near-me .

Remember, the goal of descriptive statistics is to simplify the complex.

Inferential Statistics: Beyond the Basics

Let’s keep the party analogy rolling, but this time, imagine you couldn’t attend the party yourself.

You’re curious if the party was as fun as everyone said it would be.

Instead of asking every single attendee, you decide to ask a few friends who went.

Based on their experiences, you try to infer what the entire party was like.

This is essentially what inferential statistics does with data.

It allows you to make predictions or draw conclusions about a larger group (the population) based on a smaller group (a sample). Let’s dive into how this works.

Probability

Inferential statistics is all about playing the odds.

When you make an inference, you’re saying, “Based on my sample, there’s a certain probability that my conclusion about the whole population is correct.”

It’s like betting on whether the party was fun, based on a few friends’ opinions.

The Central Limit Theorem (CLT)

The Central Limit Theorem is the superhero of statistics.

It tells us that if you take enough samples from a population, the sample means (averages) will form a normal distribution (a bell curve), no matter what the population distribution looks like.

This is crucial because it allows us to use sample data to make inferences about the population mean with a known level of uncertainty.

Confidence Intervals

Imagine you’re pretty sure the party was fun, but you want to know how fun.

A confidence interval gives you a range of values within which you believe the true mean fun level of the party lies.

It’s like saying, “I’m 95% confident the party’s fun rating was between 7 and 9 out of 10.”

Hypothesis Testing

This is where you get to be a bit of a detective. You start with a hypothesis (a guess) about the population.

For example, your null hypothesis might be “the party was average fun.” Then you use your sample data to test this hypothesis.

If the data strongly suggests otherwise, you might reject the null hypothesis and accept the alternative hypothesis, which could be “the party was super fun.”

The p-value tells you how likely it is that your data would have occurred by random chance if the null hypothesis were true.

A low p-value (typically less than 0.05) indicates that your findings are significant—that is, unlikely to have happened by chance.

It’s like saying, “The chance that all my friends are exaggerating about the party being fun is really low, so the party probably was fun.”

Why Inferential Statistics Matter?

Inferential statistics let us go beyond just describing our data.

They allow us to make educated guesses about a larger population based on a sample.

This is incredibly useful in almost every field—science, business, public health, and yes, even planning your next party.

By using probability, the Central Limit Theorem, confidence intervals, hypothesis testing, and p-values, we can make informed decisions without needing to ask every single person in the population.

It saves time, resources, and helps us understand the world more scientifically.

Remember, while inferential statistics gives us powerful tools for making predictions, those predictions come with a level of uncertainty.

Being a good data scientist means understanding and communicating that uncertainty clearly.

So next time you hear about a party you missed, use inferential statistics to figure out just how much FOMO (fear of missing out) you should really feel!

Common Statistical Tests: Choosing Your Data’s Best Friend

Alright, now that we’ve covered the basics of descriptive and inferential statistics, it’s time to talk about how we actually apply these concepts to make sense of data.

It’s like deciding on the best way to find out who was the life of the party.

You have several tools (tests) at your disposal, and choosing the right one depends on what you’re trying to find out and the type of data you have.

Let’s explore some of the most common statistical tests and when to use them.

T-Tests: Comparing Averages

Imagine you want to know if the average fun level was higher at this year’s party compared to last year’s.

A t-test helps you compare the means (averages) of two groups to see if they’re statistically different.

There are a couple of flavors:

• Independent t-test : Use this when comparing two different groups, like this year’s party vs. last year’s party.
• Paired t-test : Use this when comparing the same group at two different times or under two different conditions, like if you measured everyone’s fun level before and after the party.

ANOVA : When Three’s Not a Crowd.

But what if you had three or more parties to compare? That’s where ANOVA (Analysis of Variance) comes in handy.

It lets you compare the means across multiple groups at once to see if at least one of them is significantly different.

It’s like comparing the fun levels across several years’ parties to see if one year stood out.

Chi-Square Test: Categorically Speaking

Now, let’s say you’re interested in whether the type of music (pop, rock, electronic) affects party attendance.

Since you’re dealing with categories (types of music) and counts (number of attendees), you’ll use the Chi-Square test.

It’s great for seeing if there’s a relationship between two categorical variables.

Correlation and Regression: Finding Relationships

What if you suspect that the amount of snacks available at the party affects how long guests stay? To explore this, you’d use:

• Correlation analysis to see if there’s a relationship between two continuous variables (like snacks and party duration). It tells you how closely related two things are.
• Regression analysis goes a step further by not only showing if there’s a relationship but also how one variable predicts the other. It’s like saying, “For every extra bag of chips, guests stay an average of 10 minutes longer.”

Non-parametric Tests: When Assumptions Don’t Hold

All the tests mentioned above assume your data follows a normal distribution and meets other criteria.

But what if your data doesn’t play by these rules?

Enter non-parametric tests, like the Mann-Whitney U test (for comparing two groups when you can’t use a t-test) or the Kruskal-Wallis test (like ANOVA but for non-normal distributions).

Picking the Right Test

Choosing the right statistical test is crucial and depends on:

• The type of data you have (categorical vs. continuous).
• Whether you’re comparing groups or looking for relationships.
• The distribution of your data (normal vs. non-normal).

Why These Tests Matter?

Just like you’d pick the right tool for a job, selecting the appropriate statistical test helps you make valid and reliable conclusions about your data.

Whether you’re trying to prove a point, make a decision, or just understand the world a bit better, these tests are your gateway to insights.

By mastering these tests, you become a detective in the world of data, ready to uncover the truth behind the numbers!

Regression Analysis: Predicting the Future

Ever wondered if you could predict how much fun you’re going to have at a party based on the number of friends going, or how the amount of snacks available might affect the overall party vibe?

That’s where regression analysis comes into play, acting like a crystal ball for your data.

What is Regression Analysis?

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest.

Think of it as detective work, where you’re trying to figure out if, how, and to what extent certain factors (like snacks and music volume) predict an outcome (like the fun level at a party).

The Two Main Characters: Independent and Dependent Variables

• Independent Variable(s): These are the predictors or factors that you suspect might influence the outcome. For example, the quantity of snacks.
• Dependent Variable: This is the outcome you’re interested in predicting. In our case, it could be the fun level of the party.

Linear Regression: The Straight Line Relationship

The most basic form of regression analysis is linear regression .

It predicts the outcome based on a linear relationship between the independent and dependent variables.

If you plot this on a graph, you’d ideally see a straight line where, as the amount of snacks increases, so does the fun level (hopefully!).

• Simple Linear Regression involves just one independent variable. It’s like saying, “Let’s see if just the number of snacks can predict the fun level.”
• Multiple Linear Regression takes it up a notch by including more than one independent variable. Now, you’re looking at whether the quantity of snacks, type of music, and number of guests together can predict the fun level.

Logistic Regression: When Outcomes are Either/Or

Not all predictions are about numbers.

Sometimes, you just want to know if something will happen or not—will the party be a hit or a flop?

Logistic regression is used for these binary outcomes.

Instead of predicting a precise fun level, it predicts the probability of the party being a hit based on the same predictors (snacks, music, guests).

Making Sense of the Results

• Coefficients: In regression analysis, each predictor has a coefficient, telling you how much the dependent variable is expected to change when that predictor changes by one unit, all else being equal.
• R-squared : This value tells you how much of the variation in your dependent variable can be explained by the independent variables. A higher R-squared means a better fit between your model and the data.

Why Regression Analysis Rocks?

Regression analysis is like having a superpower. It helps you understand which factors matter most, which can be ignored, and how different factors come together to influence the outcome.

This insight is invaluable whether you’re planning a party, running a business, or conducting scientific research.

Bringing It All Together

Imagine you’ve gathered data on several parties, including the number of guests, type of music, and amount of snacks, along with a fun level rating for each.

By running a regression analysis, you can start to predict future parties’ success, tailoring your planning to maximize fun.

It’s a practical tool for making informed decisions based on past data, helping you throw legendary parties, optimize business strategies, or understand complex relationships in your research.

In essence, regression analysis helps turn your data into actionable insights, guiding you towards smarter decisions and better predictions.

So next time you’re knee-deep in data, remember: regression analysis might just be the key to unlocking its secrets.

Non-parametric Methods: Playing By Different Rules

So far, we’ve talked a lot about statistical methods that rely on certain assumptions about your data, like it being normally distributed (forming that classic bell curve) or having a specific scale of measurement.

But what happens when your data doesn’t fit these molds?

Maybe the scores from your last party’s karaoke contest are all over the place, or you’re trying to compare the popularity of various party games but only have rankings, not scores.

This is where non-parametric methods come to the rescue.

Breaking Free from Assumptions

Non-parametric methods are the rebels of the statistical world.

They don’t assume your data follows a normal distribution or that it meets strict requirements regarding measurement scales.

These methods are perfect for dealing with ordinal data (like rankings), nominal data (like categories), or when your data is skewed or has outliers that would throw off other tests.

When to Use Non-parametric Methods?

• Your data is not normally distributed, and transformations don’t help.
• You have ordinal data (like survey responses that range from “Strongly Disagree” to “Strongly Agree”).
• You’re dealing with ranks or categories rather than precise measurements.
• Your sample size is small, making it hard to meet the assumptions required for parametric tests.

Some Popular Non-parametric Tests

• Mann-Whitney U Test: Think of it as the non-parametric counterpart to the independent samples t-test. Use this when you want to compare the differences between two independent groups on a ranking or ordinal scale.
• Kruskal-Wallis Test: This is your go-to when you have three or more groups to compare, and it’s similar to an ANOVA but for ranked/ordinal data or when your data doesn’t meet ANOVA’s assumptions.
• Spearman’s Rank Correlation: When you want to see if there’s a relationship between two sets of rankings, Spearman’s got your back. It’s like Pearson’s correlation for continuous data but designed for ranks.
• Wilcoxon Signed-Rank Test: Use this for comparing two related samples when you can’t use the paired t-test, typically because the differences between pairs are not normally distributed.

The Beauty of Flexibility

The real charm of non-parametric methods is their flexibility.

They let you work with data that’s not textbook perfect, which is often the case in the real world.

Whether you’re analyzing customer satisfaction surveys, comparing the effectiveness of different marketing strategies, or just trying to figure out if people prefer pizza or tacos at parties, non-parametric tests provide a robust way to get meaningful insights.

Keeping It Real

It’s important to remember that while non-parametric methods are incredibly useful, they also come with their own limitations.

They might be more conservative, meaning you might need a larger effect to detect a significant result compared to parametric tests.

Plus, because they often work with ranks rather than actual values, some information about your data might get lost in translation.

Non-parametric methods are your statistical toolbox’s Swiss Army knife, ready to tackle data that doesn’t fit into the neat categories required by more traditional tests.

They remind us that in the world of data analysis, there’s more than one way to uncover insights and make informed decisions.

So, the next time you’re faced with skewed distributions or rankings instead of scores, remember that non-parametric methods have got you covered, offering a way to navigate the complexities of real-world data.

Data Cleaning and Preparation: The Unsung Heroes of Data Analysis

Before any party can start, there’s always a bit of housecleaning to do—sweeping the floors, arranging the furniture, and maybe even hiding those laundry piles you’ve been ignoring all week.

Similarly, in the world of data analysis, before we can dive into the fun stuff like statistical tests and predictive modeling, we need to roll up our sleeves and get our data nice and tidy.

This process of data cleaning and preparation might not be the most glamorous part of data science, but it’s absolutely critical.

Let’s break down what this involves and why it’s so important.

Why Clean and Prepare Data?

Imagine trying to analyze party RSVPs when half the responses are “yes,” a quarter are “Y,” and the rest are a creative mix of “yup,” “sure,” and “why not?”

Without standardization, it’s hard to get a clear picture of how many guests to expect.

The same goes for any data set. Cleaning ensures that your data is consistent, accurate, and ready for analysis.

Preparation involves transforming this clean data into a format that’s useful for your specific analysis needs.

The Steps to Sparkling Clean Data

• Dealing with Missing Values: Sometimes, data is incomplete. Maybe a survey respondent skipped a question, or a sensor failed to record a reading. You’ll need to decide whether to fill in these gaps (imputation), ignore them, or drop the observations altogether.
• Identifying and Handling Outliers: Outliers are data points that are significantly different from the rest. They might be errors, or they might be valuable insights. The challenge is determining which is which and deciding how to handle them—remove, adjust, or analyze separately.
• Correcting Inconsistencies: This is like making sure all your RSVPs are in the same format. It could involve standardizing text entries, correcting typos, or converting all measurements to the same units.
• Formatting Data: Your analysis might require data in a specific format. This could mean transforming data types (e.g., converting dates into a uniform format) or restructuring data tables to make them easier to work with.
• Reducing Dimensionality: Sometimes, your data set might have more information than you actually need. Reducing dimensionality (through methods like Principal Component Analysis) can help simplify your data without losing valuable information.
• Creating New Variables: You might need to derive new variables from your existing ones to better capture the relationships in your data. For example, turning raw survey responses into a numerical satisfaction score.

The Tools of the Trade

There are many tools available to help with data cleaning and preparation, ranging from spreadsheet software like Excel to programming languages like Python and R.

These tools offer functions and libraries specifically designed to make data cleaning as painless as possible.

Why It Matters

Skipping the data cleaning and preparation stage is like trying to cook without prepping your ingredients first.

Sure, you might end up with something edible, but it’s not going to be as good as it could have been.

Clean and well-prepared data leads to more accurate, reliable, and meaningful analysis results.

It’s the foundation upon which all good data analysis is built.

Data cleaning and preparation might not be the flashiest part of data science, but it’s where all successful data analysis projects begin.

By taking the time to thoroughly clean and prepare your data, you’re setting yourself up for clearer insights, better decisions, and, ultimately, more impactful outcomes.

Software Tools for Statistical Analysis: Your Digital Assistants

Diving into the world of data without the right tools can feel like trying to cook a gourmet meal without a kitchen.

Just as you need pots, pans, and a stove to create a culinary masterpiece, you need the right software tools to analyze data and uncover the insights hidden within.

These digital assistants range from user-friendly applications for beginners to powerful suites for the pros.

Let’s take a closer look at some of the most popular software tools for statistical analysis.

R and RStudio: The Dynamic Duo

• R is like the Swiss Army knife of statistical analysis. It’s a programming language designed specifically for data analysis, graphics, and statistical modeling. Think of R as the kitchen where you’ll be cooking up your data analysis.
• RStudio is an integrated development environment (IDE) for R. It’s like having the best kitchen setup with organized countertops (your coding space) and all your tools and ingredients within reach (packages and datasets).

Why They Rock:

R is incredibly powerful and can handle almost any data analysis task you throw at it, from the basics to the most advanced statistical models.

Plus, there’s a vast community of users, which means a wealth of tutorials, forums, and free packages to add on.

Python with pandas and scipy: The Versatile Virtuoso

• Python is not just for programming; with the right libraries, it becomes an excellent tool for data analysis. It’s like a kitchen that’s not only great for baking but also equipped for gourmet cooking.
• pandas is a library that provides easy-to-use data structures and data analysis tools for Python. Imagine it as your sous-chef, helping you to slice and dice data with ease.
• scipy is another library used for scientific and technical computing. It’s like having a set of precision knives for the more intricate tasks.

Why They Rock: Python is known for its readability and simplicity, making it accessible for beginners. When combined with pandas and scipy, it becomes a powerhouse for data manipulation, analysis, and visualization.

SPSS: The Point-and-Click Professional

SPSS (Statistical Package for the Social Sciences) is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009.

Why It Rocks: SPSS is particularly user-friendly with its point-and-click interface, making it a favorite among non-programmers and researchers in the social sciences. It’s like having a kitchen gadget that does the job with the push of a button—no manual setup required.

SAS: The Corporate Chef

SAS (Statistical Analysis System) is a software suite developed for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics.

Why It Rocks: SAS is a powerhouse in the corporate world, known for its stability, deep analytical capabilities, and support for large data sets. It’s like the industrial kitchen used by professional chefs to serve hundreds of guests.

Excel: The Accessible Apprentice

Excel might not be a specialized statistical software, but it’s widely accessible and capable of handling basic statistical analyses. Think of Excel as the microwave in your kitchen—it might not be fancy, but it gets the job done for quick and simple tasks.

Why It Rocks: Almost everyone has access to Excel and knows the basics, making it a great starting point for those new to data analysis. Plus, with add-ons like the Analysis ToolPak, Excel’s capabilities can be extended further into statistical territory.

Choosing Your Tool

Selecting the right software tool for statistical analysis is like choosing the right kitchen for your cooking style—it depends on your needs, expertise, and the complexity of your recipes (data).

Whether you’re a coding chef ready to tackle R or Python, or someone who prefers the straightforwardness of SPSS or Excel, there’s a tool out there that’s perfect for your data analysis kitchen.

Ethical Considerations

Embarking on a data analysis journey is like setting sail on the vast ocean of information.

Just as a captain needs a compass to navigate the seas safely and responsibly, a data analyst requires a strong sense of ethics to guide their exploration of data.

Ethical considerations in data analysis are the moral compass that ensures we respect privacy, consent, and integrity while uncovering the truths hidden within data. Let’s delve into why ethics are so crucial and what principles you should keep in mind.

Respect for Privacy

Imagine you’ve found a diary filled with personal secrets.

Reading it without permission would be a breach of privacy.

Similarly, when you’re handling data, especially personal or sensitive information, it’s essential to ensure that privacy is protected.

This means not only securing data against unauthorized access but also anonymizing data to prevent individuals from being identified.

Informed Consent

Before you can set sail, you need the ship owner’s permission.

In the world of data, this translates to informed consent. Participants should be fully aware of what their data will be used for and voluntarily agree to participate.

This is particularly important in research or when collecting data directly from individuals. It’s like asking for permission before you start the journey.

Data Integrity

Maintaining data integrity is like keeping the ship’s log accurate and unaltered during your voyage.

It involves ensuring the data is not corrupted or modified inappropriately and that any data analysis is conducted accurately and reliably.

Tampering with data or cherry-picking results to fit a narrative is not just unethical—it’s like falsifying the ship’s log, leading to mistrust and potentially dangerous outcomes.

Avoiding Bias

The sea is vast, and your compass must be calibrated correctly to avoid going off course. Similarly, avoiding bias in data analysis ensures your findings are valid and unbiased.

This means being aware of and actively addressing any personal, cultural, or statistical biases that might skew your analysis.

It’s about striving for objectivity and ensuring your journey is guided by truth, not preconceived notions.

Transparency and Accountability

A trustworthy captain is open about their navigational choices and ready to take responsibility for them.

In data analysis, this translates to transparency about your methods and accountability for your conclusions.

Sharing your methodologies, data sources, and any limitations of your analysis helps build trust and allows others to verify or challenge your findings.

Ethical Use of Findings

Finally, just as a captain must consider the impact of their journey on the wider world, you must consider how your data analysis will be used.

This means thinking about the potential consequences of your findings and striving to ensure they are used to benefit, not harm, society.

It’s about being mindful of the broader implications of your work and using data for good.

Navigating with a Moral Compass

In the realm of data analysis, ethical considerations form the moral compass that guides us through complex moral waters.

They ensure that our work respects individuals’ rights, contributes positively to society, and upholds the highest standards of integrity and professionalism.

Just as a captain navigates the seas with respect for the ocean and its dangers, a data analyst must navigate the world of data with a deep commitment to ethical principles.

This commitment ensures that the insights gained from data analysis serve to enlighten and improve, rather than exploit or harm.

Conclusion and Key Takeaways

And there you have it—a whirlwind tour through the fascinating landscape of statistical methods for data analysis.

From the grounding principles of descriptive and inferential statistics to the nuanced details of regression analysis and beyond, we’ve explored the tools and ethical considerations that guide us in turning raw data into meaningful insights.

The Takeaway

Think of data analysis as embarking on a grand adventure, one where numbers and facts are your map and compass.

Just as every explorer needs to understand the terrain, every aspiring data analyst must grasp these foundational concepts.

Whether it’s summarizing data sets with descriptive statistics, making predictions with inferential statistics, choosing the right statistical test, or navigating the ethical considerations that ensure our analyses benefit society, each aspect is a crucial step on your journey.

The Importance of Preparation

Remember, the key to a successful voyage is preparation.

Cleaning and preparing your data sets the stage for a smooth journey, while choosing the right software tools ensures you have the best equipment at your disposal.

And just as every responsible navigator respects the sea, every data analyst must navigate the ethical dimensions of their work with care and integrity.

Charting Your Course

As you embark on your own data analysis adventures, remember that the path you chart is unique to you.

Your questions will guide your journey, your curiosity will fuel your exploration, and the insights you gain will be your treasure.

The world of data is vast and full of mysteries waiting to be uncovered. With the tools and principles we’ve discussed, you’re well-equipped to start uncovering those mysteries, one data set at a time.

The Journey Ahead

The journey of statistical methods for data analysis is ongoing, and the landscape is ever-evolving.

As new methods emerge and our understanding deepens, there will always be new horizons to explore and new insights to discover.

But the fundamentals we’ve covered will remain your steadfast guide, helping you navigate the challenges and opportunities that lie ahead.

So set your sights on the questions that spark your curiosity, arm yourself with the tools of the trade, and embark on your data analysis journey with confidence.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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The 11 Best Data Analytics Tools for Data Analysts in 2024

As the field of data analytics evolves, the range of available data analysis tools grows with it. If you’re considering a career in the field, you’ll want to know: Which data analysis tools do I need to learn?

In this post, we’ll highlight some of the key data analytics tools you need to know and why. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. What’s even better, a good few of those on this list contain AI data analytics tools , so you’re at the forefront of the field as 2024 comes around.

We’ll start our list with the must-haves, then we’ll move onto some of the more popular tools and platforms used by organizations large and small. Whether you’re preparing for an interview, or are deciding which tool to learn next, by the end of this post you’ll have an idea how to progress.

If you’re only starting out, then CareerFoundry’s free data analytics short course will help you take your first steps.

Here are the data analysis tools we’ll cover:

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Google Cloud AutoML
• Microsoft Power BI

How to choose a data analysis tool

Data analysis tools faq.

So, let’s get into the list then!

1.  Microsoft Excel

Excel at a glance:

• Type of tool: Spreadsheet software.
• Availability : Commercial.
• Mostly used for: Data wrangling and reporting.
• Pros: Widely-used, with lots of useful functions and plug-ins.
• Cons: Cost, calculation errors, poor at handling big data.

Excel: the world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis.

Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools.

It also has a variety of other functions that streamline data manipulation. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data.

It has limitations though. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies. Nevertheless, it’s an important and powerful data analysis tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings. Get started with these ten Excel formulas that all data analysts should know .

Python at a glance:

• Type of tool: Programming language.
• Availability: Open-source, with thousands of free libraries.
• Used for: Everything from data scraping to analysis and reporting.
• Pros: Easy to learn, highly versatile, widely-used.
• Cons: Memory intensive—doesn’t execute as fast as some other languages.

  A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it.

Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation.

Libraries like Beautiful Soup and Scrapy are used to scrape data from the web, while Matplotlib is excellent for data visualization and reporting. Python’s main drawback is its speed—it is memory intensive and slower than many languages. In general though, if you’re building software from scratch, Python’s benefits far outweigh its drawbacks. You can learn more about Python in our full guide .

R at a glance:

• Availability: Open-source.
• Mostly used for: Statistical analysis and data mining.
• Pros: Platform independent, highly compatible, lots of packages.
• Cons: Slower, less secure, and more complex to learn than Python.

R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software.

R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages.

It integrates well with other languages and systems (including big data software) and can call on code from languages like C, C++, and FORTRAN. On the downside, it has poor memory management, and while there is a good community of users to call on for help, R has no dedicated support team. But there is an excellent R-specific integrated development environment (IDE) called RStudio , which is always a bonus!

4.  Jupyter Notebook

Jupyter Notebook at a glance:

• Type of tool: Interactive authoring software.
• Mostly used for: Sharing code, creating tutorials, presenting work.
• Pros: Great for showcasing, language-independent.
• Cons: Not self-contained, nor great for collaboration.

Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text.

Imagine something a bit like a Microsoft word document, only far more interactive, and designed specifically for data analytics! As a data analytics tool, it’s great for showcasing work: Jupyter Notebook runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data analysis tools, like Apache Spark (see below) and offers various outputs from HTML to images, videos, and more.

But as with every tool, it has its limitations. Jupyter Notebook documents have poor version control, and tracking changes is not intuitive. This means it’s not the best place for development and analytics work (you should use a dedicated IDE for these) and it isn’t well suited to collaboration.

Since it isn’t self-contained, this also means you have to provide any extra assets (e.g. libraries or runtime systems) to anybody you’re sharing the document with. But for presentation and tutorial purposes, it remains an invaluable data science and data analytics tool.

5.  Apache Spark

Apache Spark at a glance:

• Type of tool: Data processing framework
• Availability: Open-source
• Mostly used for: Big data processing, machine learning
• Pros: Fast, dynamic, easy to use
• Cons: No file management system, rigid user interface

Apache Spark is a software framework that allows data analysts and data scientists to quickly process vast data sets. It was first developed in 2012, it’s designed to analyze unstructured big data, Spark distributes computationally heavy analytics tasks across many computers.

While other similar frameworks exist (for example, Apache Hadoop ) Spark is exceptionally fast. By using RAM rather than local memory, it is around 100x faster than Hadoop. That’s why it’s often used for the development of data-heavy machine learning models .

It even has a library of machine learning algorithms, MLlib , including classification, regression, and clustering algorithms, to name a few. On the downside, consuming so much memory means Spark is computationally expensive. It also lacks a file management system, so it usually needs integration with other software, i.e. Hadoop.

6. Google Cloud AutoML

Google Cloud AutoML at a glance:

• Type of tool: Machine learning platform
• Availability:  Cloud-based, commercial
• Mostly used for:  Automating machine learning tasks
• Pros: Allows analysts with limited coding experience to build and deploy ML models , skipping lots of steps
• Cons:  Can be pricey for large-scale projects, lacks some flexibility

A serious proposition for data analysts and scientists in 2024 is Google Cloud’s AutoML tool. With the hype around generative AI in 2023 set to roll over into the next year, tools like AutoML but the capability to create machine learning models into your own hands.

Google Cloud AutoML contains a suite of tools across categories from structured data to language translation, image and video classification. As more and more organizations adopt machine learning, there will be a growing demand for data analysts who can use AutoML tools to automate their work easily.

SAS at a glance:

• Type of tool: Statistical software suite
• Availability: Commercial
• Mostly used for: Business intelligence, multivariate, and predictive analysis
• Pros: Easily accessible, business-focused, good user support
• Cons: High cost, poor graphical representation

SAS (which stands for Statistical Analysis System) is a popular commercial suite of business intelligence and data analysis tools. It was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. Created for an enterprise market, the software is generally more robust, versatile, and easier for large organizations to use. This is because they tend to have varying levels of in-house programming expertise.

But as a commercial product, SAS comes with a hefty price tag. Nevertheless, with cost comes benefits; it regularly has new modules added, based on customer demand. Although it has fewer of these than say, Python libraries, they are highly focused. For instance, it offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

8. Microsoft Power BI

Power BI at a glance:

• Type of tool: Business analytics suite.
• Availability: Commercial software (with a free version available).
• Mostly used for: Everything from data visualization to predictive analytics.  
• Pros: Great data connectivity, regular updates, good visualizations.
• Cons: Clunky user interface, rigid formulas, data limits (in the free version).

At less than a decade old, Power BI is a relative newcomer to the market of data analytics tools. It began life as an Excel plug-in but was redeveloped in the early 2010s as a standalone suite of business data analysis tools. Power BI allows users to create interactive visual reports and dashboards , with a minimal learning curve. Its main selling point is its great data connectivity—it operates seamlessly with Excel (as you’d expect, being a Microsoft product) but also text files, SQL server, and cloud sources, like Google and Facebook analytics.

It also offers strong data visualization but has room for improvement in other areas. For example, it has quite a bulky user interface, rigid formulas, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not that user-friendly. It does offer several subscriptions though, including a free one. This is great if you want to get to grips with the tool, although the free version does have drawbacks—the main limitation being the low data limit (around 2GB).

Tableau at a glance:

• Type of tool: Data visualization tool.
• Availability: Commercial.
• Mostly used for: Creating data dashboards and worksheets.
• Pros: Great visualizations, speed, interactivity, mobile support.
• Cons: Poor version control, no data pre-processing.

If you’re looking to create interactive visualizations and dashboards without extensive coding expertise, Tableau is one of the best commercial data analysis tools available. The suite handles large amounts of data better than many other BI tools, and it is very simple to use. It has a visual drag and drop interface (another definite advantage over many other data analysis tools). However, because it has no scripting layer, there’s a limit to what Tableau can do. For instance, it’s not great for pre-processing data or building more complex calculations.

While it does contain functions for manipulating data, these aren’t great. As a rule, you’ll need to carry out scripting functions using Python or R before importing your data into Tableau. But its visualization is pretty top-notch, making it very popular despite its drawbacks. Furthermore, it’s mobile-ready. As a data analyst , mobility might not be your priority, but it’s nice to have if you want to dabble on the move! You can learn more about Tableau in this post .

KNIME at a glance:

• Type of tool: Data integration platform.
• Mostly used for: Data mining and machine learning.
• Pros: Open-source platform that is great for visually-driven programming.
• Cons: Lacks scalability, and technical expertise is needed for some functions.

Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning.

Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without heavy costs. This makes it popular with smaller businesses, who have limited budgets.

Now that we’ve checked out all of the data analysis tools, let’s see how to choose the right one for your business needs.

11. Streamlit

• Type of tool:  Python library for building web applications
• Availability:  Open-source
• Mostly used for:  Creating interactive data visualizations and dashboards
• Pros: Easy to use, can create a wide range of graphs, charts, and maps, can be deployed as web apps
• Cons: Not as powerful as Power BI or Tableau, requires a Python installation

Sure we mentioned Python itself as a tool earlier and introduced a few of its libraries, but Streamlit is definitely one data analytics tool to watch in 2024, and to consider for your own toolkit.

Essentially, Streamlit is an open-source Python library for building interactive and shareable web apps for data science and machine learning projects. It’s a pretty new tool on the block, but is already one which is getting attention from data professionals looking to create visualizations easily!

Alright, so you’ve got your data ready to go, and you’re looking for the perfect tool to analyze it with. How do you find the one that’s right for your organization?

First, consider that there’s no one singular data analytics tool that will address all the data analytics issues you may have. When looking at this list, you may look at one tool for most of your needs, but require the use of a secondary tool for smaller processes.

Second, consider the business needs of your organization and figure out exactly who will need to make use of the data analysis tools. Will they be used primarily by fellow data analysts or scientists, non-technical users who require an interactive and intuitive interface—or both? Many tools on this list will cater to both types of user.

Third, consider the tool’s data modeling capabilities. Does the tool have these capabilities, or will you need to use SQL or another tool to perform data modeling prior to analysis?

Fourth—and finally!—consider the practical aspect of price and licensing. Some of the options are totally free or have some free-to-use features (but will require licensing for the full product). Some data analysis tools will be offered on a subscription or licencing basis. In this case, you may need to consider the number of users required or—if you’re looking on solely a project-to-project basis—the potential length of the subscription.

In this post, we’ve explored some of the most popular data analysis tools currently in use. The key thing to takeaway is that there’s no one tool that does it all. A good data analyst has wide-ranging knowledge of different languages and software.

CareerFoundry’s own data expert, Tom Gadsby, explains which data analytics tools are best for specific processes in the following short video:

If you found a tool on this list that you didn’t know about, why not research more? Play around with the open-source data analysis tools (they’re free, after all!) and read up on the rest.

At the very least, it helps to know which data analytics tools organizations are using. To learn more about the field, start our free 5-day data analytics short course .

For more industry insights, check out the following:

• The 7 most useful data analysis methods and techniques
• How to build a data analytics portfolio
• Get started with SQL: A cheatsheet

What are data analytics tools?

Data analytics tools are software and apps that help data analysts collect, clean, analyze, and visualize data. These tools are used to extract insights from data that can be used to make informed business decisions.

What is the most used tool by data analysts?

Microsoft Excel continues to be the most widely used tool by data analysts for data wrangling and reporting. Big reasons are that it provides a user-friendly interface for data manipulation, calculations, and data viz.

Is SQL a data analysis tool?

Yes. SQL is a specialized programming language for managing and querying data in relational databases. Data analysts use SQL to extract and analyze data from databases, which can then be used to generate insights and reports.

Which tool is best to analyse data?

It depends on what you want to do with the data and the context. Some of the most popular and versatile tools are included in this article, namely Python, SQL, MS Excel, and Tableau.

Top 9 Statistical Tools Used in Research

Well-designed research requires a well-chosen study sample and a suitable statistical test selection . To plan an epidemiological study or a clinical trial, you’ll need a solid understanding of the data . Improper inferences from it could lead to false conclusions and  unethical behavior . And given the ocean of data available nowadays, it’s often a daunting task for researchers to gauge its credibility and do statistical analysis on it.

With that said, thanks to all the statistical tools available in the market that help researchers make such studies much more manageable.  Statistical tools are   extensively used in academic and research sectors  to study human, animal, and material behaviors and reactions.

Statistical tools  aid in the interpretation and use of data. They can be used to evaluate and comprehend any form of data. Some statistical tools can help you see trends, forecast future sales, and create links between causes and effects. When you’re unsure where to go with your study, other tools can assist you in navigating through enormous amounts of data.

In this article, we will  discuss some  of the best statistical tools and their key features . So, let’s start without any further ado.

What is Statistics? And its Importance in Research

Statistics is the study of collecting, arranging, and interpreting data from samples and inferring it to the total population.  Also  known  as the “Science of Data,” it allows us to derive conclusions from a data set. It may also assist people in all industries in answering research or business queries and forecast outcomes, such as what show you should watch next on your favorite video app.

Statistical Tools Used in Research

Researchers often cannot discern a simple truth from a set of data. They can only draw conclusions from data after statistical analysis. On the other hand, creating a statistical analysis is a difficult task. This is when statistical tools come into play. Researchers can use statistical tools to back up their claims, make sense of a vast set of data, graphically show complex data, or help clarify many things in a short period. 

Let’s go through  the top 9 best statistical tools used in research  below:

SPSS (Statistical Package for the Social Sciences)  is a collection of software tools compiled as a single package. This program’s primary function is to analyze scientific data in social science. This information can be utilized for market research, surveys, and data mining, among other things. It is mainly used in the following areas like marketing, healthcare, educational research, etc.

SPSS first stores and organizes the data, then compile the data set to generate appropriate output. SPSS is intended to work with a wide range of variable data formats.

Some of the  highlights of SPSS :

• It gives you greater tools for analyzing and comprehending your data. With SPSS’s excellent interface, you can easily handle complex commercial and research challenges.
•  It assists you in making accurate and high-quality decisions.
• It also comes with a variety of deployment options for managing your software.
• You may also use a point-and-click interface to produce unique visualizations and reports. To start using SPSS, you don’t need prior coding skills.
•  It provides the best views of missing data patterns and summarizes variable distributions.

R  is a statistical computing and graphics programming language that you may use to clean, analyze and graph your data. It is frequently used to estimate and display results by researchers from various fields and lecturers of statistics and research methodologies. It’s free, making it an appealing option, but it relies upon programming code rather than drop-down menus or buttons. 

Some of the  highlights of R :

• It offers efficient storage and data handling facility.
• R has the most robust set of operators. They are used for array calculations, namely matrices.
• It has the best data analysis tools.
• It’s a full-featured high-level programming language with conditional loops, decision statements, and various functions.

SAS  is a statistical analysis tool that allows users to build scripts for more advanced analyses or use the GUI. It’s a high-end solution frequently used in industries including business, healthcare, and human behavior research. Advanced analysis and publication-worthy figures and charts are conceivable, albeit coding can be a challenging transition for people who aren’t used to this approach.

Many big tech companies are using SAS due to its support and integration for vast teams. Setting up the tool might be a bit time-consuming initially, but once it’s up and running, it’ll surely streamline your statistical processes.

Some of the  highlights of SAS  are:

• , with a range of tutorials available.
• Its package includes a wide range of statistics tools.
• It has the best technical support available.
• It gives reports of excellent quality and aesthetic appeal
• It provides the best assistance for detecting spelling and grammar issues. As a result, the analysis is more precise.

MATLAB  is one of the most well-reputed statistical analysis tools and statistical programming languages. It has a toolbox with several features that make programming languages simple. With MATLAB, you may perform the most complex statistical analysis, such as  EEG data analysis . Add-ons for toolboxes can be used to increase the capability of MATLAB.

Moreover, MATLAB provides a multi-paradigm numerical computing environment, which means that the language may be used for both procedural and object-oriented programming. MATLAB is ideal for matrix manipulation, including data function plotting, algorithm implementation, and user interface design, among other things. Last but not least, MATLAB can also  run programs  written in other programming languages. 

Some of the  highlights of MATLAB :

• MATLAB toolboxes are meticulously developed and professionally executed. It is also put through its paces by the tester under various settings. Aside from that, MATLAB provides complete documents.
• MATLAB is a production-oriented programming language. As a result, the MATLAB code is ready for production. All that is required is the integration of data sources and business systems with corporate systems.
• It has the ability to convert MATLAB algorithms to C, C++, and CUDA cores.
• For users, MATLAB is the best simulation platform.
• It provides the optimum conditions for performing data analysis procedures.

Some of the  highlights of Tableau  are:

• It gives the most compelling end-to-end analytics.
• It provides us with a system of high-level security.
• It is compatible with practically all screen resolutions.

Minitab  is a data analysis program that includes basic and advanced statistical features. The GUI and written instructions can be used to execute commands, making it accessible to beginners and those wishing to perform more advanced analysis.

Some of the  highlights of Minitab  are:

• Minitab can be used to perform various sorts of analysis, such as measurement systems analysis, capability analysis, graphical analysis, hypothesis analysis, regression, non-regression, etcetera.
• , such as scatterplots, box plots, dot plots, histograms, time series plots, and so on.
• Minitab also allows you to run a variety of statistical tests, including one-sample Z-tests, one-sample, two-sample t-tests, paired t-tests, and so on.

7. MS EXCEL:

You can apply various formulas and functions to your data in Excel without prior knowledge of statistics. The learning curve is great, and even freshers can achieve great results quickly since everything is just a click away. This makes Excel a great choice not only for amateurs but beginners as well.

Some of the  highlights of MS Excel  are:

• It has the best GUI for data visualization solutions, allowing you to generate various graphs with it.
• MS Excel has practically every tool needed to undertake any type of data analysis.
• It enables you to do basic to complicated computations.
• Excel has a lot of built-in formulas that make it a good choice for performing extensive data jobs.

8. RAPIDMINER:

RapidMiner  is a valuable platform for data preparation, machine learning, and the deployment of predictive models. RapidMiner makes it simple to develop a data model from the beginning to the end. It comes with a complete data science suite. Machine learning, deep learning, text mining, and predictive analytics are all possible with it.

Some of the  highlights of RapidMiner  are:

• It has outstanding security features.
• It allows for seamless integration with a variety of third-party applications.
• RapidMiner’s primary functionality can be extended with the help of plugins.
• It provides an excellent platform for data processing and visualization of results.
• It has the ability to track and analyze data in real-time.

9. APACHE HADOOP:

Apache Hadoop  is an open-source software that is best known for its top-of-the-drawer scaling capabilities. It is capable of resolving the most challenging computational issues and excels at data-intensive activities as well, given its  distributed architecture . The primary reason why it outperforms its contenders in terms of computational power and speed is that it does not directly transfer files to the node. It divides enormous files into smaller bits and transmits them to separate nodes with specific instructions using  HDFS . More about it  here .

So, if you have massive data on your hands and want something that doesn’t slow you down and works in a distributed way, Hadoop is the way to go.

Some of the  highlights of Apache Hadoop  are:

• It is cost-effective.
• Apache Hadoop offers built-in tools that automatically schedule tasks and manage clusters.
• It can effortlessly integrate with third-party applications and apps.
• Apache Hadoop is also simple to use for beginners. It includes a framework for managing distributed computing with user intervention.

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There are a variety of software tools available, each of which offers something slightly different to the user – which one you choose will be determined by several things, including your research question, statistical understanding, and coding experience. These factors may indicate that you are on the cutting edge of data analysis, but the quality of the data acquired depends on the study execution, as with any research.

It’s worth noting that even if you have the most powerful statistical software (and the knowledge to utilize it), the results would be meaningless if they weren’t collected properly. Some online statistics tools are an alternative to the above-mentioned statistical tools. However, each of these tools is the finest in its domain. Hence, you really don’t need a second opinion to use any of these tools. But it’s always recommended to get your hands dirty a little and see what works best for your specific use case before choosing it.

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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Analysis Tools

Data analysis tools help researchers make sense of the data collected. It enables them to report results and make interpretations. How the data is analyzed depends on the goals of the project and the type of data collected. Some studies focus on qualitative data, others on quantitative data, and many on both (mixed-methods studies); examples of these can be found in a NAGT-GER Division hosted collection of presentations on Methods for Conducting GER . The Analytical Tool collection includes examples in these areas, as well as special types of analytical tool used for data specific applications and data visualizations. Quantitative and Qualitative methods both use deductive, inductive, and adductive processes to understand a process or phenomenon, just in different ways using different data.

Quantitative Analysis

Quantitative analysis uses numerical data to identify statistical relationships between variables. Quantitative data are numerical, ordinal, nominal. For example, surveys, questionnaires, and evaluations that include multiple choice items and ratings (e.g., Likert scale) provide quantitative data for analysis.

Qualitative Analysis

Qualitative analysis uses descriptive data to understand processes (e.g., how students learn in a group), develop insights into the form of sensitizing concepts, and present the view of the world from the point of view of the participants (e.g., the teachers, students and others related to the classroom). Qualitative data are descriptive. For example, field notes, interviews, video, audio, open-ended survey questions all provide qualitative data for analysis.

Tool Collection

Browse the collection of the most commonly used qualitative and quantitative analysis tools here. Submit a Tool to the Collection »

Special Types of Analyses

Some types of special analyses in geoscience education research depend on data analysis tools original developed for other purposes in the sciences or social sciences. In this section you can find descriptions of some of those tools, including eye tracking analysis software and data visualization tools (e.g., Generic Mapping Tools, MatLab, ArGIS).

Acknowledgements

Special thanks to Todd Ellis, Jason Jones, Heather Lehto, Steve Reynolds, Julie Rooney-Varga, and Stefany Sit who were part of a working group that helped develop this section of the toolbox.

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Descriptive Analytics – Methods, Tools and Examples

Table of Contents

Descriptive Analytics

Definition:

Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.

Descriptive Analytics in Research

Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:

• Descriptive Statistics: In research, descriptive analytics often takes the form of descriptive statistics . This includes calculating measures of central tendency (like mean, median, and mode), measures of dispersion (like range, variance, and standard deviation), and measures of frequency (like count, percent, and frequency). These calculations help researchers summarize and understand their data.
• Visualizing Data: Descriptive analytics also involves creating visual representations of data to better understand and communicate research findings . This might involve creating bar graphs, line graphs, pie charts, scatter plots, box plots, and other visualizations.
• Exploratory Data Analysis: Before conducting any formal statistical tests, researchers often conduct an exploratory data analysis, which is a form of descriptive analytics. This might involve looking at distributions of variables, checking for outliers, and exploring relationships between variables.
• Initial Findings: Descriptive analytics are often reported in the results section of a research study to provide readers with an overview of the data. For example, a researcher might report average scores, demographic breakdowns, or the percentage of participants who endorsed each response on a survey.
• Establishing Patterns and Relationships: Descriptive analytics helps in identifying patterns, trends, or relationships in the data, which can guide subsequent analysis or future research. For instance, researchers might look at the correlation between variables as a part of descriptive analytics.

Descriptive Analytics Techniques

Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:

Statistical Analysis

This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).

Data Aggregation

It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.

Data Mining

This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).

Data Visualization

This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.

This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.

Cross-tabulation (or Pivot Tables)

It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.

Descriptive Modeling

Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.

Descriptive Analytics Tools

Some common Descriptive Analytics Tools are as follows:

Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.

Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.

QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.

Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.

R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.

Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

When to use Descriptive Analytics

Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:

Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.

Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.

Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.

Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.

Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.

Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.

Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.

Examples of Descriptive Analytics

Examples of Descriptive Analytics are as follows:

Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.

Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.

Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.

Advantages of Descriptive Analytics

Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:

• Understanding the Past: Descriptive analytics provides an understanding of what has happened in the past, offering valuable context for future decision-making.
• Data Summarization: Descriptive analytics is used to simplify and summarize complex datasets, which can make the information more understandable and accessible.
• Identifying Patterns and Trends: With descriptive analytics, organizations can identify patterns, trends, and correlations in their data, which can provide valuable insights.
• Inform Decision-Making: The insights generated through descriptive analytics can inform strategic decisions and help organizations to react more quickly to events or changes in behavior.
• Basis for Further Analysis: Descriptive analytics lays the groundwork for further analytical activities. It’s the first necessary step before moving on to more advanced forms of analytics like predictive analytics (forecasting future events) or prescriptive analytics (advising on possible outcomes).
• Performance Evaluation: It allows organizations to evaluate their performance by comparing current results with past results, enabling them to see where improvements have been made and where further improvements can be targeted.
• Enhanced Reporting and Dashboards: Through the use of visualization techniques, descriptive analytics can improve the quality of reports and dashboards, making the data more understandable and easier to interpret for stakeholders at all levels of the organization.
• Immediate Value: Unlike some other types of analytics, descriptive analytics can provide immediate insights, as it doesn’t require complex models or deep analytical capabilities to provide value.

Disadvantages of Descriptive Analytics

While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:

• Limited to Past Data: Descriptive analytics primarily deals with historical data and provides insights about past events. It does not predict future events or trends and can’t help you understand possible future outcomes on its own.
• Lack of Deep Insights: While descriptive analytics helps in identifying what happened, it does not answer why it happened. For deeper insights, you would need to use diagnostic analytics, which analyzes data to understand the root cause of a particular outcome.
• Can Be Misleading: If not properly executed, descriptive analytics can sometimes lead to incorrect conclusions. For example, correlation does not imply causation, but descriptive analytics might tempt one to make such an inference.
• Data Quality Issues: The accuracy and usefulness of descriptive analytics are heavily reliant on the quality of the underlying data. If the data is incomplete, incorrect, or biased, the results of the descriptive analytics will be too.
• Over-reliance on Descriptive Analytics: Businesses may rely too much on descriptive analytics and not enough on predictive and prescriptive analytics. While understanding past and present data is important, it’s equally vital to forecast future trends and make data-driven decisions based on those predictions.
• Doesn’t Provide Actionable Insights: Descriptive analytics is used to interpret historical data and identify patterns and trends, but it doesn’t provide recommendations or courses of action. For that, prescriptive analytics is needed.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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13 Market Research Tools: Best in Class for 2023

Most market research tools are designed to make it quicker and easier to find relevant data . Whatever the market, product, or purpose, the right research tools can do just that.

But, let’s be honest, some do it far better than others.

Whether you’re an enterprise firm with complex needs and a budget to suit or a smaller business needing free market research tools, read on to discover which online tools for market research are hot right now.

Note: The top market research tools list has been collated using review platforms like G2 , along with direct feedback I collected from over 500 business leaders in June, 2o22.

#1 Best overall market research tool: Similarweb Digital Research Intelligence

Most-loved feature: The Benchmarking tool

We might be a little biased, but this really is the fastest way to see how you measure up against competitors in any sector or location. Analyze market leaders and rising stars to unpack and track their digital success instantly.

Quick Explainer

Similarweb Research Intelligence is a single source of truth for the online world. Giving any business the ability to quickly analyze the online aspects of any industry or market in an instant. It displays critical insights in a way that makes it easy to view trends, competitive performance, audience insights, growth opportunities, and more. It’s the only market research analysis tool that brings together data from desktop, mobile web, and apps to provide a complete view of the digital landscape.

Key abilities

• Competitive benchmarking
• Market research
• Company research tool
• Audience analysis
• Consumer journey tracker
• Mobile app intelligence

Freemium Version: Yes, there is a lite version of the product that provides limited data for a single user, and a single location.

Free Trial: Yes, there’s a 7-day trial available. Try it out here .

Ongoing Subscription: Yes, you can pay monthly or annually for a subscription. Different levels are available, and each package is tailored. Review pricing and plans for Similarweb here.

Like what you’ve seen so far?

See our market-leading digital research tool in action in this quick 2-minute clip.

#2 Best free market research tool: Think with Google

Most-loved feature: Find my Audience

A way to discover new audiences on YouTube based on things like habits, interests, and intended plans to purchase.

Think with Google is a suite of digital research tools that curate resources from a huge pool of data across the web. It presents them as insights that aren’t typically available elsewhere. It’s a unique way to view trends, insights, and stats. Data isn’t offered in real-time but serves more as a library of figures and facts that take the form of articles, videos, interviews, case studies, and more. In addition to being a place people can go for forward-looking perspectives and data, there are several tools designed to help marketers.

Key functions

• High-level insights into most local or national markets
• Behind the scenes look at cross-platform digital campaigns
• Consumer insights
• Deck-ready stats (not in real-time)
• A range of tools to inform marketing objectives and actions

Freemium Version: The entire suite of market intelligence tools is free.

Free Trial: As a free market research tool, no trial is needed.

Ongoing Subscription: You can subscribe to a newsletter, but not the product.

#3 Best digital research tool for content and FAQ development: Answer the Public

Most-loved feature: Search listening alerts

A pro feature that sends you weekly emails that indicate how search behaviors shift over time. It takes the specific phrase or keywords you’re tracking in the platform and updates you weekly.

Quick Explainer 

Discover the questions people are asking online about key terms, products, or services. It’s designed to help content teams and website owners develop new content ideas, and relevant FAQs that are based on the types of queries people ask online.

• Track important keywords and phrases
• Get weekly emails about changes in search behavior
• Enter any keyword to uncover relevant questions or search terms
• Folders to help organize your research

Freemium Version: Yes. You get a limited number (3) of searches per day.

Free Trial: No.

Ongoing Subscription: Yes. You can pay monthly or annually for this service. Pay-monthly fees are a flat rate of $99. Discounts are offered for yearly subscriptions.

#4 Best tool for market research surveys: SurveyMonkey

Most-loved feature: Question bank

A library of hundreds of questions, pre-written by survey methodologists.

As far as market research surveys go, it’s the leading online research tool for surveys worldwide. With plans to suit the individual through to the enterprise, it’s a feature-rich, easy-to-use platform that encompasses creation, collection, and analysis under one roof. Surveys are optimized for any device and integrate with platforms like Zoom, Salesforce, Marketo, and more.

Key functions 

• Create and send unlimited surveys, quizzes, and polls
• Pop-up online surveys
• Mobile app access to create, send and analyze surveys on-the-go
• Team collaboration function (unlocked with a team plan)
• Survey builder
• Customization and branded surveys (available with advantage or premier plans only)

Freemium Version: Yes

Free Trial: Occasionally, free trials are offered for premium plans.

Ongoing Subscription: Yes, you can pay annually or monthly. There are three different plans to choose from, ranging from $25 to $129 per month.

Helpful: Check out our blog and see 18 different ways to use market research surveys .

#5 Best online research tool for marketplaces: Similarweb Shopper Intelligence

Most-loved feature: Cross-shopping analysis

Cross-shopping analysis shows you how loyal a segment of customers is to a brand, along with what other brands they browsed or bought from. Uncover competitors and discover new partnership opportunities; these are game-changing insights if you sell on any marketplace.

Similarweb Shopper Intelligence is a type of online market research tool that helps you uncover and analyze browsing and buying behavior across marketplaces. Using its data, businesses can track category, product, and brand performance with ease. It helps ecommerce organizations to detect potential threats, unearth new product or category opportunities, discover new potential partnerships, and optimize search strategy and performance.

• Monitor consumer demand for any product, brand, or category
• Retail search strategy optimization
• Consumer behavior insights
• Track cross-shopping, loyalty, and purchase frequency
• Analyze brand awareness

Note: This solution uses a unique data methodology via multiple networks and partnerships. At the time of writing, there is no other consumer behavior insights tool for market research that offers this quality of data for marketplaces.

Freemium Version: No.

Free Trial: Yes. There is usually a 7-day trial available here .

Ongoing Subscription: The price is determined by things like the number of categories and/or domains you want to access. Each quote is customized to a client’s specific needs.

Want to know a little more?

Watch this quick clip to see the best ecommerce digital market research tool in action.

#6 Best market research tool for brand tracking: Latana

Most-loved feature: MoE (margin of error) Readings

To deliver transparency on data confidence levels, Latana’s dashboard includes a feature that allows clients to toggle-on, or toggle-off, margin of error (MoE) readings on all data points. These are highlighted using a traffic-light system of confidence (red=low confidence, orange=medium confidence, and green=high confidence). This small feature makes a big impact — it helps clients to correctly interpret the data and to visibly see quality shortcomings.

Latana is a B2C brand tracking tool that provides granular insights about online audiences. It helps organizations understand how key segments of consumers feel about brands and portrays relevant standings vs. industry rivals.

• Focus on niche consumer segments that matter to your business
• Uncover rival’s audience data and identify opportunities to grow
• Understand brand perception, and track how it changes over time
• Discover the most well-known brands in your industry
• Track rival’s brand awareness across gender, age income, location, and education
• Find out the main purchase drivers for your industry
• Infrastructure gives reach to over 6 billion smartphone users globally for representative brand opinions

I caught up with Latana’s CMO, Angeley Mullens. Here’s what she has to say about their offering.

Ongoing Subscription: Pricing for Latana isn’t available online. All packages are tailored to individual brands and their specific needs.

Enjoy 360 Visibility 24/7

Get the data you need to adapt to market changes and industry trends in an instant.

#7  Best research tool for social media listening: Hootsuite

Most-loved feature: Multi-channel insights

It’s a legacy feature, but one which makes it the best online research tool for social listening and monitoring. Having the ability to easily schedule posts, ad campaigns, and handle responses for every social media channel from within a single platform is what makes this a market-leading digital research tool.

Hootsuite continues to claim the number 1 spot on G2’s list of digital research tools for social media monitoring . It’s a tool to help you manage all aspects of business social media, across multiple channels, in a single platform. As well as being able to manage your socials, it also keeps you up-to-date with the latest trends and activities of your rival’s social media channels.

• Publish and schedule social media posts
• Measure cross-platform results
• Message management
• Social media trend analysis
• Social media ad-campaign management

Freemium Version: Yes. You can get a free version that supports 2 social accounts and 1 user.

Free Trial: Yes. A 30-day free trial is available here .

Ongoing Subscription: There are four plans; professional, team, business, and enterprise; ranging from $49-$739.

#8 Best digital research tool for prospecting: Similarweb Sales Intelligence

Most-loved feature: Insights generator tool

The insights generator shows you unique facts for your prospects and accounts; with complete visibility into their digital strategy and performance. It’s ideal for refining sales and marketing efforts while staying focused on growth.

Similarweb Sales Intelligence helps organizations find viable prospects by showing you who to reach out to, when to do it, and how to capture their attention. The lead generator tool helps you find the right prospects, and key insights help create engaging outreach emails. For sales departments, ecommerce and mar-tech sectors, this type of digital research tool can take prospecting and engagement to a completely new level; along with revenue and growth.

Key functions: 

• Lead generation and enrichment
• Digital insights for 100M+ ecommerce websites, publishers, and advertisers
• Fraud detection
• Sales engagement
• SFDC integration

Free Trial: Yes, if you would like a free trial, please request that here.

Ongoing Subscription: Prices for this digital market research tool varies depending on the package and options chosen. Grab a live demo of the product and get a tailored quote here .

Insightful : If you’re looking at market research tools for the ecommerce industry, bookmark our Ecommerce Trends and Predictions for 2023 to read later.

#9 Best market research analysis tool for data visualization: Tableau

Most-loved feature : Connects to almost any data source

As a clear market leader, and a no-brainer for larger organizations with business intelligence analytics and teams. Tableau leads the way in online research tools for data visualization. It connects to a huge range of data sources and pulls information into a highly-appealing dashboard that is designed to make it easier and faster to explore and manage data . It takes data from platforms like Similarweb, then combines it with other data sources before presenting crisp, clear, insights that have the power to shape strategies and drive key transformations.

• Lightning-fast analytics
• Smart dashboards for richer insights
• Live connection to almost any data source, with automatic updates
• Drag-and-drop style UI: easy to use

Freemium Version: No. However, students and teachers get a year’s free access to the platform.

Free Trial: Yes. You can subscribe to a free 30-day trial.

Ongoing Subscription: Most plans are offered annually, with prices ranging from $15 per month upwards. The price depends on whether you use their hosted or on-premise versions, the number of users, and the inclusion of specific plugins.

#10 Best market research tool for UX testing: Loop11

Most-loved feature: Online usability testing

This feature analyzes the usability of a website with users performing live tasks on a site. It helps you understand user behavior, and shows how and why a website is used.

Loop11 is a market research tool that provides usability testing to help organizations build better websites and products. It comes with a pack of useful features that provide both moderated and unmoderated testing, helping businesses to find the right audience to test prototypes and products. It’s designed to help you see how appealing a product is to a particular audience, determine their preferences, then build these insights into a design.

• Ability to test across multiple devices, including tablet, mobile, or desktop
• User-friendly test builder that requires no coding
• Easy-to-add surveys that collect psychographic and demographic data
• Provides useful metrics like time on task, task completion rates, and NPS
• Mapping of customer journeys during a test period

Free Trial: Yes, a 14-day trial is available here .

Ongoing Subscription: All plans come with the option to pay monthly or annually. Prices range from $199-$599 per month.

#11 Best research tool for measuring customer experience: Temper

Most-loved feature: Rating stream

See real-time feedback as customers respond to questions via website or email channels. The stream provides a detailed view of ratings, comments, locations, referrers, email addresses, and more.

Temper allows any company to find out how customers feel about their product at all times. It directly provides first-party data to a business, preventing the need to design and distribute complex surveys. It can be placed as a widget on the site or in emails, and questions are asked to gain real-time feedback from visitors and customers alike.

• Easily deploy questions across website and email channels
• Quickly spot poor experiences to identify problematic areas of a business or product
• The rating graph gives you a real-time view of results for any question asked
• Public rating wall shows how you’re performing, instilling confidence and trust
• Ratings come with open text fields to give additional context to responses
• Referrer data gives you the ability to segment feedback and relative performance
• Tracking variables let you send data with ratings, such as order numbers, user IDs, etc.
• User targeting lets you determine who sees questions and how often they see them

Freemium Version: There is no freemium version. However, their hobby plan gives you a slimmed-down version of the product and costs $12 per month.

Ongoing Subscription: Four plans are available, ranging from hobbyist to enterprise. The lowest pricing tier starts at $12 monthly, and their top-tier solution costs $199 monthly. All plans are pay-monthly, with a 60-day money-back guarantee.

#12 Best online market research tool for focus groups: Remesh

Most-loved feature: Common topics

In just a few clicks, you can view the themes and topics that are most common with your focus group across an entire session. It groups similar responses, specific phrases, and interesting responses in seconds.

Remesh facilitates live, qualitative conversations with focus groups of up to 1000 people at a time. Replicating the focus group format online delivers powerful segmentation and dynamic capabilities that speed up your time to insight and let you hold a real-time conversation at scale.

• Launch a live conversation with up to 1000 people at a time
• Organize and analyze responses in an instant
• Segment your audience based on demographic and response data
• Share visuals and text-based content with the group to get instant feedback
• The algorithm analyzes open-ended responses in real-time

Freemium Version: No

Free Trial: Yes. However, you must first book a demo with a member of their team.

Ongoing Subscription: Remesh provides custom pricing plans that can only be obtained once you’ve taken a demonstration of their platform with a member of their team.

#13 Top collaboration and documentation tool for market research: BIT.AI

Most-loved feature: Content library + smart search

While it sounds quite basic; in essence, this tool for market research professionals makes it quicker and easier to keep track, share, and store key data. Forget trawling through emails, slack, and g-docs to find files; the smart search feature helps you locate files in an instant.

A dynamic platform that helps researchers collaborate, track, share, and manage research data in a single place. This is one of the best online market research tools for those who need a place to bring together resources like websites, PDFs, articles, images, infographics, blogs, reports, videos, etc. it’s low-cost and connects to some of the most widely used tools. Being able to share multidimensional data with others, or simply keeping track of secondary market research in a single place makes it a firm favorite.

• Over 100 integrations with applications like Tableau, Miro, G-docs, Onedrive, and more
• Real-time editing and live collaboration
• Content Library
• Smart search
• Supports a huge range of content and file types

Freemium Version: Yes. Available for teams of up to 5 collaborators.

Free Trial: Yes, a free trial is available here .

Ongoing Subscription: A range of packages are available, costing between $8-$20 monthly.

Best market research tools for startups

There is another often-forgotten set of tools used for market research that are ideal for startups. If you’ve got zero budget and a little time on your hands, you can do most types of desk research for free. Sources include:

• Company reports, case studies, and whitepapers
• Research and trade associations
• Media coverage
• Internal sales or usage reports
• Academic or scientific journals
• Government and non-government agencies
• Public library records
• Competitor websites
• Educational institutions

Helpful: Check out this article about how to do market research for a startup .

Wrapping up….

With cost and time key considerations for anyone looking at tools for market research, it’s vital to choose wisely. While free market research tools are all good and well, they won’t always serve you when you’re on a deadline or require key insights on a specific competitor, market, or product.

Similarweb helps companies win in the digital world. Whatever the market, goal, or business size, its solutions are designed to help organizations understand their market and compete and beat rivals.

Take it for a test run today. Trial any Similarweb solution free for the first 7-days using this link .

Need to know more about the ROI of Similarweb? 

What are the best market research tools for secondary research?

The internet is probably the best tool for market research there is. It’s a goldmine of secondary market research data. But beware of data validity and check your information is coming from a trusted source.

What are the best market research tools for surveys?

Survey monkey is considered the best online market research tool for surveys, but key players like Typeform and Zoho follow closely behind. Budget and features usually determine the right tool for your needs.

What are the best free market research tools?

The best free tools for market research include: Answer the Public, Think with Google, Similarweb lite, SurveyMonkey’s basic plan, and Hootsuite’s free plan.

What are the best market research tools for qualitative research?

Qualitative research includes things like focus groups, open-ended surveys, case studies, and observation research. As such, the best tool for online research like this would be something like BIT.ai’s documentation and collaboration tool. Another useful tool for qualitative market research would be an online survey provider, like SurveyMonkey, Typeform, or Google Forms.

What are the best market research tools for quantitative research?

As this type of research is focused more on numbers, the best quantitative market research tools include things like Similarweb Digital Research Intelligence and Tableau. Each performs a different function but works together to collect, analyze, and present data in the most useful way possible.

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Discover what your customers and employees are really thinking.

Survey software gets answers to your most important customer, employee, marketing and product questions. It can handle everything from simple customer feedback questionnaires to detailed research projects for the world’s biggest brands.

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Today's reality—sound familiar?

2.6x more success could have been realized in marketing campaigns with better research & insights., 23% of organizations don’t have a clear market research strategy in place., 13% ​​of marketing spend is wasted for reasons that could have been addressed through better market research., with online survey software you can:.

• Eliminate manual data collection
• Get real-time, actionable insights
• Reach more people, faster and easier
• Get better, more honest responses
• Create professional surveys without any experience

Ready to take your market research to the next level?

Answers and insights from your audience, wherever they are.

Wherever you need to gather data, survey software can help. From a simple survey link you can paste anywhere, to advanced integrations with your CRM, to email, social, website, QR code, SMS and offline surveys, we’ll help you reach your target respondents, no matter where they are.

Drag-and-drop simplicity for even the most advanced surveys

Choose from 23 question types (including video/audio responses) and use advanced logic, branching, quotas, API integrations into Zendesk and email triggers to build and launch your project. It’s all done in an intuitive drag-and-drop software interface that makes even the most sophisticated surveys easy to create, launch and analyze.

Next-level survey reports and dashboards

Make better decisions with advanced reports and dashboards you can share in seconds. Choose from over 30 different graph types, share reports online, or export survey data to popular formats like CSV, TSV, Excel, SPSS and more.

Built-in intelligence with every type of survey

Leverage advanced analysis, including video feedback summarization powered by generative AI, crosstabs, and statistical analysis tools. Automatically review survey design to ensure methodology best practices, response quality, and compliance with internal policies and PII.

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Survey software FAQs

A survey is a method of gathering information using relevant questions from a sample of people with the aim of understanding populations as a whole. Surveys provide a critical source of data and insights for everyone engaged in the information economy, from businesses to media, to government and academics.

Survey software is a tool used to design, send and analyze surveys online. It’s the primary method of collecting feedback at scale whether that’s a simple questionnaire or a detailed study such as customer or employee feedback as part of a more structured experience management program. Cloud-based survey technology has revolutionized the ability to get data, quickly, from a large number of respondents by automating the process of sending out surveys across a variety of channels from websites and mobile to apps, email and even chatbots.

Surveys provide quick, quantitative data on a wide audience’s opinions, preferences, and experiences. They are cost-effective, easy to administer, and can reach a large population. They also allow for anonymity, increasing the chance of honest responses, and their standardized format makes it easy to aggregate and analyze data for clear insights into trends and patterns.

To create a survey , define the objectives, choose target participants, design clear and concise questions, select a survey tool or platform, and ensure the layout is logical. Test the survey, distribute it, and collect responses. Remember to keep it as brief as possible while gathering the necessary information.

To write survey questions , be clear and specific to avoid confusion. Use simple, unbiased language, and opt for closed-ended questions for easier analysis. Ensure questions are relevant to your objectives, and avoid leading or loaded questions that could influence answers. Pretest your questions to catch any issues and revise as needed for clarity and objectivity.

Now used by more than 18,000+ brands, and supporting more than 1.3 billion surveys a year, Qualtrics empowers organizations to gather invaluable customer insights and take immediate, game-changing action – with zero coding required. The Qualtrics survey tool makes it easy to get answers to your most important marketing, branding, customer, and product questions, with easy-to-use tools that can handle everything from simple customer feedback questionnaires to detailed research projects.

Qualtrics Strategic Research pricing is based on interactions including number of survey responses and minutes of video feedback. Our special online pricing offer starts at $420 per month and can be purchased here . Alternatively, you can get started with a free account with basic functionality, or get 30 days access to advanced features with a free trial .

Yes, we offer a free account option with basic survey functionality.

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• Inappropriate use of proton pump inhibitors in clinical practice globally: a systematic review and meta-analysis
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• http://orcid.org/0000-0002-5111-7861 Amit K Dutta 1 ,
• http://orcid.org/0000-0003-2472-3409 Vishal Sharma 2 ,
• Abhinav Jain 3 ,
• Anshuman Elhence 4 ,
• Manas K Panigrahi 5 ,
• Srikant Mohta 6 ,
• Richard Kirubakaran 7 ,
• Mathew Philip 8 ,
• http://orcid.org/0000-0003-1700-7543 Mahesh Goenka 9 ,
• Shobna Bhatia 10 ,
• http://orcid.org/0000-0002-9435-3557 Usha Dutta 2 ,
• D Nageshwar Reddy 11 ,
• Rakesh Kochhar 12 ,
• http://orcid.org/0000-0002-1305-189X Govind K Makharia 4
• 1 Gastroenterology , Christian Medical College and Hospital Vellore , Vellore , India
• 2 Gastroenterology , Post Graduate Institute of Medical Education and Research , Chandigarh , India
• 3 Gastroenterology , Gastro 1 Hospital , Ahmedabad , India
• 4 Gastroenterology and Human Nutrition , All India Institute of Medical Sciences , New Delhi , India
• 5 Gastroenterology , All India Institute of Medical Sciences - Bhubaneswar , Bhubaneswar , India
• 6 Department of Gastroenterology , Narayana Superspeciality Hospital , Kolkata , India
• 7 Center of Biostatistics and Evidence Based Medicine , Vellore , India
• 8 Lisie Hospital , Cochin , India
• 9 Apollo Gleneagles Hospital , Kolkata , India
• 10 Gastroenterology , National Institute of Medical Science , Jaipur , India
• 11 Asian Institute of Gastroenterology , Hyderabad , India
• 12 Gastroenterology , Paras Hospitals, Panchkula , Chandigarh , India
• Correspondence to Dr Amit K Dutta, Gastroenterology, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India; akdutta1995{at}gmail.com

https://doi.org/10.1136/gutjnl-2024-332154

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• PROTON PUMP INHIBITION
• META-ANALYSIS

We read with interest the population-based cohort studies by Abrahami et al on proton pump inhibitors (PPI) and the risk of gastric and colon cancers. 1 2 PPI are used at all levels of healthcare and across different subspecialties for various indications. 3 4 A recent systematic review on the global trends and practices of PPI recognised 28 million PPI users from 23 countries, suggesting that 23.4% of the adults were using PPI. 5 Inappropriate use of PPI appears to be frequent, although there is a lack of compiled information on the prevalence of inappropriate overuse of PPI. Hence, we conducted a systematic review and meta-analysis on the inappropriate overuse of PPI globally.

Supplemental material

Overall, 79 studies, including 20 050 patients, reported on the inappropriate overuse of PPI and were included in this meta-analysis. The pooled proportion of inappropriate overuse of PPI was 0.60 (95% CI 0.55 to 0.65, I 2 97%, figure 1 ). The proportion of inappropriate overuse by dose was 0.17 (0.08 to 0.33) and by duration of use was 0.17 (0.07 to 0.35). Subgroup analysis was done to assess for heterogeneity ( figure 2A ). No significant differences in the pooled proportion of inappropriate overuse were noted based on the study design, setting (inpatient or outpatient), data source, human development index of the country, indication for use, sample size estimation, year of publication and study quality. However, regional differences were noted (p<0.01): Australia—40%, North America—56%, Europe—61%, Asia—62% and Africa—91% ( figure 2B ). The quality of studies was good in 27.8%, fair in 62.03% and low in 10.12%. 6

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Forest plot showing inappropriate overuse of proton pump inhibitors.

(A) Subgroup analysis of inappropriate overuse of proton pump inhibitors (PPI). (B) Prevalence of inappropriate overuse of PPI across different countries of the world. NA, data not available.

This is the first systematic review and meta-analysis on global prescribing inappropriateness of PPI. The results of this meta-analysis are concerning and suggest that about 60% of PPI prescriptions in clinical practice do not have a valid indication. The overuse of PPI appears to be a global problem and across all age groups including geriatric subjects (63%). Overprescription increases the patient’s cost, pill burden and risk of adverse effects. 7–9 The heterogeneity in the outcome data persisted after subgroup analysis. Hence, this may be inherent to the practice of PPI use rather than related to factors such as study design, setting or study quality.

Several factors (both physician and patient-related) may contribute to the high magnitude of PPI overuse. These include a long list of indications for use, availability of the drug ‘over the counter’, an exaggerated sense of safety, and lack of awareness about the correct indications, dose and duration of therapy. A recently published guideline makes detailed recommendations on the accepted indications for the use of PPI, including the dose and duration, and further such documents may help to promote its rational use. 3 Overall, there is a need for urgent adoption of PPI stewardship practices, as is done for antibiotics. Apart from avoiding prescription when there is no indication, effective deprescription strategies are also required. 10 We hope the result of the present systematic review and meta-analysis will create awareness about the current situation and translate into a change in clinical practice globally.

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• Abrahami D ,
• McDonald EG ,
• Schnitzer ME , et al
• Jearth V , et al
• Malfertheiner P ,
• Megraud F ,
• Rokkas T , et al
• Shanika LGT ,
• Reynolds A ,
• Pattison S , et al
• O’Connell D , et al
• Choudhury A ,
• Gillis KA ,
• Lees JS , et al
• Paynter S , et al
• Targownik LE ,
• Fisher DA ,

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @drvishal82

Contributors AKD: concept, study design, data acquisition and interpretation, drafting the manuscript and approval of the manuscript. VS: study design, data acquisition, analysis and interpretation, drafting the manuscript and approval of the manuscript. AJ, AE, MKP, SM: data acquisition and interpretation, critical revision of the manuscript, and approval of the manuscript. RK: study design, data analysis and interpretation, critical revision of the manuscript and approval of the manuscript. MP, MG, SB, UD, DNR, RK: data interpretation, critical revision of the manuscript and approval of the manuscript. GKM: concept, study design, data interpretation, drafting the manuscript, critical revision and approval of the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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