Frequently Asked Questions
What is INN?
INN, or the Inter-Nomological Network is an integrated theory development application which aims to reduce redundancy in research in the behavioral sciences. INN uses natural language processing techniques to return search results that are semantically related to the user’s query. For an in-depth explanation, please view the introductory video on the homepage.
In what ways is INN better than other search engines?
For theory development, INN has the capability to outperform other search engines for three main reasons. First, it contains only theory-relevant content such as variable/construct names, definitions, items, as well as construct-level citations. This means that when you search for a variable like “ease of use,” which will return more than 200,000 documents, the vast majority of which do not statistically examine the ease of use construct against other constructs and variables. Second, it is able to return results that are semantically related to a search, even if the search contained none of the same words as the results. This helps to reduce the need to search for multiple redundant terms. Third, INN includes only the top rated journals from each discipline. This reduces the large number of barely-relevant results from other search engines into a smaller pool articles that are more highly related to the original search. Armed with a better understanding of the problem, the user may then move on to other search engines if needed.
How do find ‘similar variables’ and ‘similar items’ work?
These functions are the results of using the Latent Semantic Analysis technique (Deerwester, Dumais et al. 1990; Larsen and Monarchi 2004) to analyze each paragraph in the articles in the selected database. This creates a high-dimensional semantic space containing some sense or “understanding” of the underlying language. Each variable’s name, definition, and items are then treated as a block of text and “projected” into the semantic space. The variable’s location in the high-dimensional space is determined and stored into a meta-semantic space (Larsen, Lee et al. 2010). When you request ‘similar variable’ for a variable of interest, we search the meta-semantic space for the variables closest to yours. The above process is the same for ‘similar items’ except that only one the item texts are projected, stored, and searched for in the meta-semantic space.
How does the ‘synonymy’ function work?
This function is only available on variables that have been part of a manual categorization task inside the project. So far, we have only done this for a subset of the MIS discipline. Please keep in mind that no two categorization tasks (or experts for that matter) will ever create the same categorization structure for a sufficiently complex problem. These results provide a window into our (quite rigorous and time-intensive) process. We did this work comparing the abilities of humans to the functionality of our system as part of the requirements for various papers. We ask that you use these results only for your own literature review rather than as data for your own research.
Find ‘similar variables’ or ‘similar items’ does not work for my variable or item. Why?
Finding similar variables and items is an open research question that the Human Behavior Project was founded on. Specifically, this is the belief that we will be able to develop increasingly effective approaches to finding related variables and items. Our findings so far indicate that the more diverse the database of variables and items, the worse the performance of the ‘similar’ functions. This can to a certain extent be overcome by increasing the dimensionality of the underlying semantic space, which again degrades the performance (speed) of the search engine. For now, at least, it remains a careful balance. The default database for INN is our most diverse database, and will have the worst performance on ‘similar’ searches. For the discipline-specific database, you will likely find that the ‘similar’ functions will work worse for highly heterogeneous disciplines such as Nursing and better for homogeneous disciplines such as Management Information Systems (MIS). In the future, we expect to provide topic-specific databases, depending on grants received. We’re always looking for collaborators interested in applying for joint funding to create these resources.
Why can’t I sort results by discipline? I’m not interested in Nursing variables.
Have you created an account? If you create an account and log in, you have access to discipline-specific databases. This does not currently solve the problem of a user who wants to get rid of one discipline and keep all other results, but it helps users who are primarily interested in only one discipline. This functionality is on our ‘wish-list’. If you believe that variables from a specific set of journals should be combined into a new database, let us know through the Feedback link at the top of the page.
To which disciplines does INN currently cater?
INN currently includes articles from the top journals in education, information systems, marketing, health, psychology, and sociology. In the future, we will also include communication, behavioral economics, management, and science and innovation policy.
What is the science behind INN?
We are continually working to publish the science behind INN. Due to a major multi-year process of building the search-engine and the algorithms behind it as well as a number of other algorithms not yet added to the search engine, we have focused on conference presentations, but this is changing as we have recently started to move several papers into appropriate journals. Some recent publications include: Semantic validity (Larsen, Nevo et al. 2008; Larsen, Lee et al. 2010); Algorithm development (Bong, Larsen et al. 2012); Automatic extraction of theories (Li and Larsen 2011); INN for qualitative analysis (Cook, Larsen et al. 2012); Interfield nomological networks (Larsen and Hovorka 2012).
- Bong, C. H., K. R. Larsen and J. Martin (2012). A Large Scale Knowledge Integration Which Leads to Human Decision Making. IEEE Symposium on Computer and Informatics. Penang, Malaysia, IEEE.
- Cook, P. F., K. R. Larsen, T. J. Sakraida and L. Pedro (2012). "A Novel Approach to Concept Analysis: The Inter-Nomological Network." Nursing Research Forthcoming.
- Deerwester, S., S. Dumais, G. Furnas, T. Landauer and R. Harshman (1990). "Indexing by Latent Semantic Analysis." Journal of the American Society for Information Science 41(391-407).
- Larsen, K. R. and D. S. Hovorka (2012). Developing Interfield Nomological Nets. Hawaii International Conference on System Sciences. Maui, Hawaii, IEEE.
- Larsen, K. R., J. Lee, J. Li and C. H. Bong (2010). A Transdisciplinary Approach to Construct Search and Integration. 16th Americas Conference on Information Systems, Lima, Peru, Association of Information Systems.
- Larsen, K. R. and D. E. Monarchi (2004). "A Mathematical Approach to Categorization and Labeling of Qualitative Data: the Latent Categorization Method." Sociological Methodology 34(1): 349-392.
- Larsen, K. R., D. Nevo and E. Rich (2008). Exploring the Semantic Validity of Questionnaire Scales. Hawaii International Conference on System Sciences. Waikoloa, Hawaii: 1-10.
- Li, J. and K. R. Larsen (2011). Establishing Nomological Networks for Behavioral Science: A Natural Language Processing Based Approach. International Conference on Information Systems. Shanghai, China, Association for Information Systems.