Events and Training

Statistics Boot Camps

 

 

 

 

 

 

 

Return to your newsroom ready to craft statistics into stories that count! This workshop is aimed at strengthening the skills of reporters who are familiar with basic computer-assisted reporting and want to add statistical analysis to their toolkits. Taught by Jennifer LaFleur, senior editor for data journalism at The Center for Investigative Reporting, and David Donald, data editor for The Center for Public Integrity.

NICAR is a joint program of IRE and the Missouri School of Journalism.

Thanks to the Investigative Reporting Workshop at American University in Washington, D.C. for hosting this Stats Boot Camp.  

 

 

Register for a statistics boot camp:

May 16-18, 2014 - Washington, DC |

 

 

Sliding fee scale:

Listed prices are for current IRE members. Nonmembers add $70; nonmember students add $25.

Newspapers:
• Sunday circulation under 50,000: $350
• Sunday circulation under 100,000: $550
• Sunday circulation 100,000 and over: $850

Television:
• 50-200 market: $350
• 25-50 market: $550
• Top 25 market: $850

Radio: $550

Magazine and Newsletters: $350

Online Media:
• 15 or more full-time employees (FTE): $550
• 15 or less full-time employees (FTE): $350

Universities: $350

Freelance: $300

Students: $300

 

Sample schedule


Day 1

9 a.m.
Recent stories using advanced techniques

10 a.m.
From database managers to more powerful software: Simple tasks in SPSS that will save time everyday

11 a.m.
The basics of data analysis: Units of analysis and the data ladder

Noon
Lunch

1 p.m.
Recoding: From values to categories and back

2 p.m.
Working with many categorical variables: Frequencies, crosstabs, layered crosstabs and significance testing on rates

4 p.m.
Overview of sampling


Day 2

9 a.m.
From categorical to continuous variables: Ranges, descriptives and pictures

10 a.m.
Mixing categorical and continuous — ANOVAs

11 a.m.
Working with many continuous variables: Correlations, scatter plots

Noon
Lunch

1 p.m.
The basics of linear regressions

2 p.m.
Diagnosing your regressions: You're not done yet

4 p.m.
Great graphics and what they have in common


Day 3

9 a.m.
Going further with predictive models — The basics of dummy variables and logistic regressions

10:30 a.m.
More data analysis tools for Census and other data: Gini, diversity and segregation measures

Noon
Lunch

1 p.m.
Combining many measures into indexes, from school quality to quality of life

2 p.m.
(Optional) Question-and-answer session

3-4 p.m.
(Optional) Open lab