Learn about factor analysis, a statistical method used to uncover underlying dimensions in a dataset. Discover its types, benefits, and real-world examples. https://hubs.ly/Q02FC69x0
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Correlation analysis in market research is a statistical method that identifies the strength of a relationship between two or more variables. Learn more. https://hubs.ly/Q02C5sG30
What is Correlation Analysis? [How to Measure Pros & Cons]
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GMP (Batch-7) ll Indian Institute of Management Kozhikode (IIM-K) ll Deputy Manager - Operation Control Group at Titan Company Ltd (A TATA Enterprises) ll ICTRD Certified Business Management Expert 2023-24
Introduction to Univariate, Bivariate and Multivariate Analysis.
Univariate, Bivariate and Multivariate Analysis
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Developed a cheat sheet for choosing the right statistical significance test and corresponding effect size measures in Human-Computer Interaction research. Please consider reporting effect size alongside any statistically significant findings. #Statistics #ResearchTools #HCI
A Brief Note on Selecting and Reporting the Right Statistical Test
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There are many tools that we use to analyse qualitative data. One of these includes a Qualitative Comparative Analysis (QCA). Read one our blogs to understand when and why a QCA could be used and the essential steps to execute one.
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Big Data Engineer | Expert in Hive & Elasticsearch | Managing & Analyzing Datasets up to 5TB | Skilled in SQL, Data Warehousing & Analytics | Driving Data-Driven Decisions
📊 Unlocking the Power of Sample Size in Data Analysis: A Guide for Data Enthusiasts 📊 Hello #DataCommunity, Understanding sample size is a cornerstone of robust data analysis, and it's a topic that often comes up in interviews for data roles. Here's why it's crucial and some best practices for selecting the right sample size. 🔍 Why Sample Size Matters 1️⃣ Statistical Power: A larger sample size boosts statistical power, enhancing the likelihood of detecting real effects. 2️⃣ Margin of Error: A larger sample narrows the margin of error, making your findings more reliable. 3️⃣ Confidence Level: A larger sample elevates the confidence level, allowing for greater certainty that the sample mirrors the population. 🛠 Best Practices for Sample Selection 1️⃣ Align with Objectives: Your sample size should resonate with your research goals. 2️⃣ Random Sampling: Aim for a random sample to minimize bias. 3️⃣ Stratified Sampling: For diverse populations, consider stratified sampling to ensure all subgroups are represented. 4️⃣ Representativeness: Always validate that your sample is a good proxy for the population. 5️⃣ Statistical Formulas: Leverage statistical tools to calculate the ideal sample size. 6️⃣ Pilot Testing: Conduct a pilot test to identify any limitations in your data collection methods. 📝 Common Interview Questions & Answers 1️⃣ How do you determine the right sample size for a project? "I start by understanding the research objectives and the population variance. Then, I use statistical formulas to calculate the sample size based on desired confidence levels and margin of error." 2️⃣ Can you explain the concept of statistical power? "Statistical power is the probability that a test will detect a real effect if one exists. Higher statistical power means a lower chance of committing a Type II error." 3️⃣ What are the risks of using a sample that is too small? "A small sample size can lead to unreliable results, a larger margin of error, and reduced statistical power, making it difficult to draw meaningful conclusions." 📚 Further Reading https://lnkd.in/gj2jphDb https://lnkd.in/gDYrWvHe Understanding sample size is more than just a numbers game; it's about elevating the reliability and impact of your research. So, always give it the attention it deserves. #DataAnalysis #SampleSize #Statistics #DataScience #BestPractices
Central Limit Theorem (CLT): Definition and Key Characteristics
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🔍 **Exploring Key Statistical Tests: Establishing Relationships Between Variables** 🔍 Understanding the relationships between variables is crucial for insightful research. Some of the main statistical tests used to establish these relationships: 1. **Correlation Analysis**: Measures the strength and direction of the relationship between two variables. Commonly used tests include Pearson's correlation and Spearman's rank correlation. 2. **Regression Analysis**: Assesses the impact of one or more independent variables on a dependent variable. Techniques include linear regression, multiple regression, and logistic regression. 3. **Chi-Square Test**: Examines the association between categorical variables, determining if distributions of variables are independent of each other. 4. **ANOVA (Analysis of Variance)**: Compares means across multiple groups to see if there are statistically significant differences. Variants include one-way ANOVA and two-way ANOVA. 5. **t-Tests**: Compare the means of two groups to determine if they are statistically different from each other. Types include independent t-tests and paired t-tests. At Beulah Researchers, we specialize in guiding you through the process of selecting and applying the right statistical tests for your research. Our expert team can help you prepare your data, run the tests, and interpret the results with precision and clarity. Let us help you uncover meaningful insights and establish robust relationships in your data! 💡📊 #StatisticalTests #ResearchSupport #BeulahResearchers 🌟
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Multivariate OR Multivariable? Understand the key differences between multivariate and multivariable analysis! 🌟 These distinct statistical techniques are essential for analyzing complex data in medical research. Learn more in our latest blog post: https://lnkd.in/dzS6KdaF #StatisticalAnalysis #StatisMed #MedicalResearch #Statistics #Biostatistics
Multivariate vs. Multivariable Analysis: Understanding the Key Differences - StatisMed
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unwritten assumptions in statistical analysis I have often seen research designs with so many statistical flaws more recently. I question the credibility of research inference. it's crucial to recognize the potential pitfalls in statistical analysis. One should always be cautious about unwritten assumptions when conducting research. Using a one-way ANOVA to compare means among multiple groups isn't always appropriate, especially with small sample sizes. Proper diligence in research design, including exploring alternative statistical approaches and assessing data distribution, is essential for robust and credible findings. Consulting with experts and making informed decisions can greatly improve the quality of research outcomes. I wrote on this subject matter in a blog post. why not take a look? https://lnkd.in/diBGQGuu #inferential #statistics #flaws #designs #research #error #dataanalysis #nosaanalytics
The one-size-fits-all unwitting assumptions (Part 1)
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#snsintitutions #snsdesignthinkers #designthinking Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question: Qualitative vs. quantitative: Will your data take the form of words or numbers? Primary vs. secondary: Will you collect original data yourself, or will you use data that has already been collected by someone else? Descriptive vs. experimental: Will you take measurements of something as it is, or will you perform an experiment? Second, decide how you will analyze the data. For quantitative data, you can use statistical analysis methods to test relationships between variables. For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.
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🔬 Small expected frequencies in chi-square analysis: A statistical minefield! Did you know some say all expected values should be >10, while others argue for >5? Then there's the '80% rule' (80% of cells >5, rest >1), or the 'average expected count >6' guideline. And don't forget the 'no cell <1 unless N>40' crowd! 🤯 Confused? You're not alone! Want clear, up-to-date guidelines to navigate this maze with confidence? Discover practical, easy-to-follow approaches for handling those tricky small frequencies. Get the clarity you need in 'From Data to Insights: A Beginner's Guide to Cross-Tabulation Analysis'. Now 20% off! https://lnkd.in/dq9Sb6eA #Statistics #ChiSquare #DataAnalysis
From Data to Insights: A Beginner's Guide to Cross-Tabulation Analysis
routledge.com
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