Quantitative Data Analysis 101 Tutorial: Descriptive vs Inferential Statistics (With Examples)

Does quantitative analysis fill you with dread? Here's how to make sense of your numbers and choose the right method. 🎓 GET 1-ON-1 DATA ANALYSIS HELP: https://go.gradcoach.com/QqQow 🧰 FREE RESEARCH TOOLKIT: https://go.gradcoach.com/tXo8G THE KEY QUESTIONS WE ANSWER IN THIS VIDEO What is quantitative data analysis? Analyzing data that is numbers-based, or data that can easily be converted into numbers without losing any meaning, such as category-based variables like gender, ethnicity or native language. It contrasts with qualitative analysis, which deals with words, phrases and expressions. What is quantitative analysis used for in research? Three purposes: measuring differences between groups, assessing relationships between variables, and testing hypotheses in a scientifically rigorous way. What are the two branches of statistical analysis? Descriptive statistics and inferential statistics. Descriptives describe your specific data set, your sample; inferential methods make predictions about the wider population based on that sample. Your research may use only descriptives, or a mix of both. What are the most common descriptive statistics? The mean, which is the mathematical average; the median, the midpoint when the numbers are arranged in order; the mode, the most commonly repeated number; standard deviation; and skewness. What does standard deviation tell you about your data? How dispersed a range of numbers is, meaning how close they sit to the mean. Where most numbers are close to the average the standard deviation is relatively low; where they are scattered all over the place it is relatively high. What is skewness in statistics? Skewness indicates how symmetrical a range of numbers is: whether they cluster into a smooth bell curve in the middle of the graph, called a normal or parametric distribution, or lean to the left or right, called a non-normal or non-parametric distribution. Why do descriptive statistics matter if I want to use inferential methods? Descriptives tell you the shape of your data, and each inferential method carries assumptions about distribution, so they are the first step to knowing which inferential methods you can and cannot use. Rushing past them leaves you with very flawed results. What is the difference between a t-test and ANOVA? A t-test compares the means of two groups to assess whether they differ to a statistically significant extent, for example blood pressure between people who took a new medication and people who did not. ANOVA does the same but across multiple groups rather than only two. What is correlation analysis? Correlation analysis assesses the relationship between two variables: if one increases, does the other increase, decrease or stay the same? If average temperature goes up, do ice cream sales rise too? It measures that relationship scientifically rather than assuming it. What is the difference between correlation and regression analysis? Both assess the relationship between variables, but regression goes further and looks at cause and effect, not just whether two variables move together. How do I choose the right quantitative analysis method? Two factors decide it. First, the nature of your data: its level of measurement, whether nominal, ordinal, interval or ratio, and whether its distribution is normal or not, since some methods only work with parametric data. Second, your research questions and hypotheses. OVERVIEW 00:00 Introduction 02:30 What is quantitative data analysis? 03:23 The three purposes of quantitative analysis 04:55 The two branches: descriptive vs inferential statistics 05:34 Sample vs population, and why the difference matters 08:05 What descriptive statistics do 08:45 Mean, median and mode explained 09:27 Standard deviation and skewness explained 10:21 Worked example: descriptive statistics on a real data set 12:44 Why descriptive statistics matter 14:09 What inferential statistics do 16:44 T-tests and ANOVA explained 17:55 Correlation and regression analysis explained 20:22 How to choose the right quantitative analysis method 21:18 Data types: nominal, ordinal, interval and ratio 25:07 Recap FREE RESOURCES Quantitative Data Analysis Methods: A Beginner's Guide - https://go.gradcoach.com/Wfo8n Descriptive Statistics: A Beginner's Guide - https://go.gradcoach.com/BBQMd Inferential Statistics 101: A Beginner's Guide to Statistical Tests - https://go.gradcoach.com/QjhaG How to Choose the Right Statistical Test - https://go.gradcoach.com/ovy8F OUR SOCIALS 🟢 Facebook -   / gradcoachofficial   🟢 Twitter -   / gradcoachdotcom   🟢 LinkedIn -   / grad-coach-intl   🟢 YouTube -    / gradcoach