A Quantitative Analysis for Non-Numeric Data (Published)
This study illustrates the use of an Association Rule General Analytic System (ARGAS) for analyzing non-numeric data. Previous research by Parente, Finley and Megalis (2021) showed how the ARGAS approach could be used to test hypotheses in conventional experimental designs. This study illustrates how ARGAS can be used in exploratory research settings such as single-case research, assessing organization in multi-trial learning experiments, analysis of social media, and case-oriented studies of individuals. This approach to analysis is appropriate in research settings where the units of measure are words, shapes, or other forms of non-numeric data.
Citation: Parente F., Finley J.C., Magalis C. (2023) A Quantitative Analysis for Non-Numeric Data, International Journal of Quantitative and Qualitative Research Methods, Vol.11, No.1, pp.1-11
Keywords: ARGAS, GLM, Social media, and case-oriented qualitative research., association rule, hypothesis generation, single case research
An Association Rule General Analytic System (ARGAS) for hypothesis testing in qualitative and quantitative research (Published)
This paper describes an Association Rule General Analytic System (ARGAS) as an alternative to the General Linear Model (GLM) for hypothesis testing. We illustrate how the ARGAS can be used to analyze both qualitative and quantitative research data. The advantages of the ARGAS approach derives from the fact that it is designed to analyze words or numbers that are converted into words. Unlike the GLM, it does not have any distributional assumptions. Association rule calculations are well-developed and there are a variety of computer software applications available that expedite the computations. The purpose of this study is to illustrate how the ARGAS can be applied and how to interpret the results.
Keywords: ARGAS, GLM, Pattern Recognition, association rule analysis, hypothesis testing, qualitative, quantitative
An Association Rule General Analytic System (ARGAS) for hypothesis testing in qualitative and quantitative research (Published)
This paper describes an Association Rule General Analytic System (ARGAS) as an alternative to the General Linear Model (GLM) for hypothesis testing. We illustrate how the ARGAS can be used to analyze both qualitative and quantitative research data. The advantages of the ARGAS approach derives from the fact that it is designed to analyze words or numbers that are converted into words. Unlike the GLM, it does not have any distributional assumptions. Association rule calculations are well-developed and there are a variety of computer software applications available that expedite the computations. The purpose of this study is to illustrate how the ARGAS can be applied and how to interpret the results.
Keywords: ARGAS, GLM, Pattern Recognition, association rule analysis, hypothesis testing, qualitative, quantitative