Providing Help with Statistical Concepts and Terms: Enhanced Glossary and Ontology

1. Providing Help with Statistical Concepts and Terms: Enhanced Glossary and Ontology Stephanie W. Haas Ron Brown Cristina Pattuelli

2. Development of Enhanced Glossary ontology term content context specificity format presentations user control

3. Terms • Include terms that users frequently encounter on agency sites, not comprehensive dictionary • Basic level of statistical literacy, not highly technical resource • Strategies for term identification • examination of frequently-visited pages • anecdotal evidence from agency and non-agency consultants • metadata user study • webcrawl of agency sites

4. Content • Provide basic level of explanation • May include: • definition • example • brief tutorial • demonstration • interactive simulation • combination • May incorporate related terms and concepts • Give pointers to more complete and/or more technical explanations

5. Context specificity • Explanations provided at varying levels of specificity • General, context-free, “universal” • Agency or concept-specific, incorporating entities from agriculture, labor, science R&D, energy, etc. • Table- or statistic-specific, based on a single row, column, or statistic, e.g., CPI, national death rate, gasoline prices in NY state, etc.

6. Provide explanations of term or concept that are as relevant to user’s current context as possible. • When user invokes help on a term, the most specific explanations available are offered. • If there is no explanation for that specific statistic or table, more general (e.g., agency-specific) ones are offered. Default is “universal” level. • Path from specific to general is based on the ontology.

7. Format • User can choose desired format of explanation, based on interest, learning style, reading level, hardware/software limitations • text • text plus audio (narration) • graphic • animation • interactive

8. User Control • Make glossary help attractive and accessible • Help users understand the statistics they find without interrupting their information-seeking task • Let users know when help is available • Let users choose the format and specificity they desire • Control mechanisms, e.g., means of invocation and termination, pop-up windows, mouse-overs, etc.

9. Creating the Ontology • Select ontology editor to meet our needs • Include terms and concepts to support glossary. • May need “connecting nodes” that aren’t in glossary • Relationships • standard – isa, instance, etc. • domain-specific – predicts, smoothes, etc. • Visualization tools for end users (future work)

10. Ontology support for glossary Relationships support design and display of term explanations • Specificity of explanations • inheritance of more general explanations • Explanation templates • sample: samples for specific surveys • index: CPI, Antiknock Index • Related terms – incorporation into tutorial • population, sample

11. Current Coverage • adjustment • universal • age adjustment - FL death rates • seasonal adjustment - NY unemployment rate • index • universal, CPI, Antiknock index • population, parameter, sample, statistic • universal, weekly gasoline prices, NY state weekly gasoline prices, height & weight of U.S. adult residents

12. Mock-ups

13. population & sample (1) Population Dislikes dogs Likes dogs p = 10/50 = .2 = 20% Suppose this picture represents the population of people in the entire country. In this population, a certain percentage (p) of people like dogs. In this example, 10 people like dogs. P is the parameter that measures this view of the population. It is the value that you would get if you could survey the entire population. 20% of the people in this population like dogs.

14. population & sample (2) Population Dislikes dogs Likes dogs Sample p = 10/50 = .2 = 20% P* = 3/10 = .3 = 30% In real life it is difficult to survey the entire population so we take a sample. We can then count the number of people in the sample who like dogs, and calculate a statistic (P*) that is an estimate of the value of p. In this case, P* overestimates the value of the parameter p.

15. EIA weekly gasoline prices Every Monday, retail prices for all three grades of gasoline are collected by telephone from a sample of approximately 900 retail gasoline outlets. Reported in: Weekly U.S. Retail Gasoline Prices, Regular Grade Dollars per gallon, including all taxes http://www.eia.doe.gov/oil_gas/petroleum/data_publications/wrgp/mogas_home_page.html

16. text example of population and sample for this table • graphical example of population and sample for this table

17. graphical example of population & sample, gasoline prices sample: 900 retail gasoline outlets population: all retail gasoline outlets regular gasoline, mean price/gallon, 9/30/02 = $1.413

18. text example of sample for NY • graphical example of sample for NY

19. $$ graphical example of sample, NY gasoline prices 9/30/02 sample of New York retail gasoline outlets mean cost = $1.529 per gallon

20. graphical example of population and sample for body measurements

21. graphical example of population and sample for body measurements 5,000 individuals are surveyed annually each participant represents approximately 50,000 other U.S. residents

22. Population is_described_by Parameter mean Is part of Is a predictor of standard_deviation Statistic Sample is_described_by sample_mean sample_standard_deviation

23. Population is_described_by Parameter mean Is part of Is a predictor of standard_deviation Is a predictor of Statistic Sample is_described_by Is a predictor of sample_mean sample_standard_deviation

24. U.S. residents Population NY State retail gasoline outlets U.S. retail gasoline outlets U.S. R&D companies Is part of Is part of Is part of Is part of Is part of Sample n U.S. R&D companies 900 U.S. retail gasoline outlets n NY State retail gasoline outlets instance of 5,000 U.S. residents/yr

25. 6 24.7 59 103 42 10.1 combiner index = 12.3 Index An index combines numbers measuring different things into a single number. The single number represents all the different measures in a compact, easy-to-use form. Values for an index can be compared to each other, for example, over time.

26. Jan. combiner Apr. combiner Jul. combiner Oct. combiner 14.3 13.1 12.3 13.9 The index has increased this year.

27. Consumer Price Index (CPI) • The Consumer Price Index (CPI) represents changes in prices of all goods and services produced for consumption by urban households. It combines prices into a single number that can be compared over time. • Items are classified into 8 major groups: • Food and Beverages • Housing • Apparel • Transportation • Medical Care • Recreation • Education and Communication • Other

28. transportation food & beverage apparel education & communication housing recreation medical care other CPI combiner Consumer Price Index

29. 1997 CPI Combiner 1998 CPI Combiner 1999 CPI Combiner 2000 CPI Combiner 2001 CPI Combiner The Consumer Price Index has increased since 1995.

30. Antiknock Index, also known as Octane Rating A number used to indicate gasoline’s antiknock performance in motor vehicle engines. The two recognized laboratory engine test methods for determining the antiknock rating, i.e., octane rating, of gasolines are the Research method and the Motor method. In the United States, to provide a single number as guidance to the consumer, the antiknock index (R+M)/2, which is the average of the Research and Motor octane numbers, was developed. http://www.eia.doe.gov/glossary/glossary_a.htm

31. (R + M)/2 Research method Motor method Antiknock Combiner Antiknock Index, also known as Octane Rating Regular: 85 - 88 Midrange: 88 - 90 Premium: 90 or above

32. Next Steps • expand coverage of core terms • webcrawl indicates measures of central tendency are next: average, mean, median, mode • expand coverage of ontology • expand presentation examples • animations, simulations • explore user controls • user study of effectiveness