Before completing the tasks, work through each of the sub-pages for tutorial 1. 

Select 10 Twitter Accounts of your choice and create a dataset with the following variables:

• Twitter Handle - Record the name of the Twitter account

• Gender/Organisation - Record the gender of the Twitter account holder (record Males as 0 and Females as 1 - add labels so that you can easily see what is what in your data). 

Note: Some of you may select organisations rather than people, or a combination of the two. If this is the case create one variable that measures whether the twitter account is a person or an organisation and a second variable that collects the sex, if available - if it is an organisation just add in NaN for missing values.

• Followers - Record the number of followers

• Following - Record how many people the account follows

• Year Joint - Record the year the person joined.

If you haven't already done so, add a description for each variable and add labels to your Gender levels (Male for 0s and Female for 1s)

Use the Filter and Replace function in Excel to clean your data

• Use the Excel Filter function to delete any rows where Progress is below 75.

• Re-label the Variable names in the following order: Progress; Age; HouseHold_Income; HH_Dependents; Gender; Know_Place

Clean the following variables using the replace function

• Whole Dataset: Replace -99 with a blank space

• Household_income: Replace ¬¨¬ with no space

• HH_Dependents: Change None to 0 and 5 or more with 5

• Gender: Replace Male with 0, Female with 1; and Other: Blank

• Know_Place: Replace text with numbers

  • Strongly agree with -2

  • Somewhat agree with -1

  • Neither agree nor disagree with 0

  • Somewhat disagree with 1

  • Strongly disagree with 2

Open up the saved file in Jamovi and attached Labels and descriptions.

Add the following descriptions to variables: 

• Household_income: Income of Household in £

• HH_Dependents: Number of Children in Household

• Gender: Male/Female

• Know_Place: I know my place in the world

Now add the following labels:

• Gender: for 0 add Male; for 1 add Female

• Know_Place: 

  • For -2 add Strongly agree

  • For  -1 add Somewhat agree

  • For 0 add Neither agree nor disagree

  • For 1 add Somewhat disagree

  • For 2 add  Strongly disagree 

Your file is now ready to analyse.  You would use a very similar process to clean a bigger dataset. 

If you have really big datasets, it is worth learning some Python or R to make data cleaning easier. 

Using the Twitter dataset you have just created do the following:

Create an ordinal variable using the Followers variable.  Call it Followers_cat. 

Create the following categories:

• Less than 1,000 followers

• Between 1,001 - 10,000 followers

• Between 10,001-100,000 followers

• More than 100,001 Followers

Change the numbers to suit your dataset (e.g. instead of 1,000 change it to 1 million).

You should now have a new variable that has four ordinal categories.

For this task use the Dataset you just cleaned in  task 2:

Create a nominal variable using the Age variable. Call it Over_50

Create the following categories:

• Over 50 - this variable should include everyone over the age of 50

• Under 50  - this variable should include everyone 49 and younger

You should now have a new nominal variable.