Cluster sampling analysis

You and a group of psychologists are interested in analyzing employee mental health. Your research includes a survey that aims to measure attitudes towards mental health in the tech workplace, and examine the frequency of mental health conditions among tech workers.

The dataset, mh_survey, includes the gender of the respondent, the US state they live in,US_state_live, and whether they have sought treatment for their mental health through their employer, sought_treatment.

You will create a pie chart to analyze the likelihood of a tech worker seeking treatment regarding their mental health in the US, sought_treatment. A random list of 10 state clusters, random_cluster, has bee uploaded for you.

Pandas and numpy as been uploaded as pd and np.

This exercise is part of the course

Analyzing Survey Data in Python

Exercise instructions

Subset dataset to include only states in random_clusters.
Create a pie chart of the sought_treatment column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Subset dataset to inlude only states in cluster_sample
cluster_sample = ____[mh_survey.US_state_live.____(____)]

# Create a pie chart of the sought_treament column
treatment_pie = cluster_sample.____.____(normalize=True)
treatment_pie.____.____()
plt.show()

Edit and Run Code

This exercise is part of the course

Analyzing Survey Data in Python

IntermediateSkill Level

4.7+

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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.

Exercise 1: Introducing Survey Data Analysis Exercise 2: Looking at levels of measurements Exercise 3: Crosstabulation Exercise 4: Descriptive and Inferential Statistics Exercise 5: Descriptive statistics Exercise 6: Inferential statistics Exercise 7: Statistical Modeling Techniques Exercise 8: Scatter plot inspection Exercise 9: Choose a statistical method Exercise 10: Sampling technique match

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.

Exercise 1: Random sampling Exercise 2: Random sample of employees Exercise 3: Reproducible random sampling Exercise 4: Stratified Random Sampling Exercise 5: Spread of yes's and no's Exercise 6: Stratified sampling Exercise 7: Weighted sampling Exercise 8: Blog survey Exercise 9: Weighted sampling on handedness Exercise 10: Cluster Sampling Exercise 11: Group clusters Exercise 12: Choosing clusters Exercise 13: Cluster sampling analysis

Current Exercise

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.

Exercise 1: Descriptive statistics in survey analysis Exercise 2: Frequency distribution Exercise 3: Measures of variability Exercise 4: Measures of central tendency Exercise 5: Inferential statistics in survey analysis Exercise 6: Visualize data: histogram Exercise 7: Find the z-score Exercise 8: Correlations Exercise 9: Analyze variables with .corr()Exercise 10: Are employees happy?Exercise 11: Fair and square

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.

Exercise 1: Regression analysis Exercise 2: Fitting a linear regression model Exercise 3: Visualizing survey data Exercise 4: Safety precautions needed?Exercise 5: Two sample t-test Exercise 6: Are women more extroverted?Exercise 7: Two sample t-test on extraversion Exercise 8: Chi-square test Exercise 9: To chi-square or not to chi-square?Exercise 10: Mental health in tech survey Exercise 11: Mental health vs. remote work Exercise 12: Congratulations