Briefly, KDC analysis is a categorical time-series analysis which views the observed data as an ordered sequence of complex propositions. The researcher postulates several directed graphs corresponding to different possible predictions regarding how the sequence of propositions is organized and then the relative contribution of each directed graph is estimated.
Golden (1998), Jaynes and Golden (2003), and the recent thesis by Ismaili (2012) are good places to start learning about KDC analysis.
Ismaili, P. B. (2012) Effects of Expertise and Hypertext Presentation Formats on Dynamic Mental Models using Both Classical and Novel Statistical Sequential Analysis. Doctoral dissertation. School of Behavioral and Brain Sciences. University of Texas at Dallas, Richardson, TX.
Ismaili, P. B. and Golden, R. M. (2008). Traversal patterns for content designed web environment. WSEAS Transactions on Information Science and Applications, 11, 5, 1521-1530.
Jaynes, C. and Golden, R. M. (2003). Statistical detection of local coherence relations in narrative recall and summarization data. In R. Alterman and D. Kirsh (eds.). Proceedings of the 25th Annual Conference of the Cognitive Science Society, Boston, MA: Cognitive Science Society, 3-8.
Golden, R. M. (1998). Knowledge digraph contribution analysis of protocol data. Discourse Processes, 25, 179-210. Semi-final draft of the original!
Golden, R. M. (1997). Causal network analysis validation using synthetic recall protocols. Behavior Research Methods Instruments & Computers, 29, 15-24.
Golden, R. M. (1994). Analysis of Categorical Time-Series Text Recall Data using a Connectionist Model. Journal of Biological Systems, 2, 283-305. 283-305.
Golden, R. M. (2006). Technical Report: Knowledge Digraph Contribution Analysis. School of Behavioral and Brain Sciences, GR4.1, University of Texas at Dallas, Richardson, TX 75083-0688.
Golden, R. M. (2006). KDC Software Powerpoint Tutorial. School of Behavioral and Brain Sciences, GR4.1, University of Texas at Dallas, Richardson, TX 75083-0688.
Currently, we are only supporting KDC on PC computers. Eventually it will be made available for MACS and LINUX operating systems.
After you have downloaded the MCRINSTALLER, click on the MCRINSTALLER and it will walk you through the installation procedure for installing the MATLAB Compiler Run-Time Library on your computer.
You have legal permission to install this MATHWORKS software to run KDC but you do not have permission to use the MATHWORKS software for any other purpose.
Uninstalling the MATLAB Compiler Run-Time Library software. Do not delete the MCRINSTALLER software from your machine. If you ever want to uninstall the MATLAB Compiler Run-Time Library software then you will need to use the MCRINSTALLER for a "clean" uninstall. To uninstall, simply go through the motions as if you wanted to install the software, the MCRINSTALLER is smart enough to figure out that the software is already installed and will ask you if you want to uninstall the software. This is the best way to uninstall the MATLAB Compiler Run-Time Library. After you have completed this uninstallation procedure, then feel free to delete anything you like!
Note that the KDC Software is for ACADEMIC use only to support scientific research. Also note that the KDC software is not a commercial software package and we are constantly updating and revising the software. Please check the website KDC Software Updates for KDC software updates and fixes and known problems.
Note that if you skip Step 1 then the downloaded KDC software will not work properly (or at all) on your machine unless you just happen to already have MATLAB R2011b installed on your computer. In that case, you can skip Step 1; otherwise YOU MUST HAVE INSTALLED the MATHWORKS COMPILER RUN-TIME LIBARY on your computer before installing KDC!!!
After you download this folder, unzip the folder. When you start up the KDC software you will need to identify this folder. Then you should load in one of the example models. There is a text file in the folder called KDChelpREADME.txt which should be helpful. Example data files are neurostudents.data and psychstudents.data. Example model files are BiDirectionalALL.model and BiDirectionalSemantic.model.
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