American Journal of Educational Research, 2015, Vol. 3, No. 5, 619-623 Available online at http://pubs.sciepub.com/education/3/5/14 © Science and Education Publishing DOI:10.12691/education-3-5-14 Improving Accuracy of Educational Research Conclusions by Using Lisrel Awaluddin Tjalla * Department of Guidance and Counseling, State University of Jakarta, Kampus UNJ, Jl. Rawamangun Muka, Rawamangun, Jakarta *Corresponding author: awaluddin.tjalla@yahoo.com Received February 14, 2015; Revised April 26, 2015; Accepted April 28, 2015 Abstract The purpose of this paper are (1) to propose applying a technique of data analysis of educational variables using LISREL, (2) to construct variables including in the research model. The other benefit of this technique compared to the conventional analysis model are: (1) faulty estimate on variables relation caused by measurement error can be corrected and (2) statistical test on whether or not a theoritical model describing relation structure between variables can be carried out. The other benefit is that fit between theoritical model and data can be tested. The use of LISREL technique to analyze variable data for education is a must. Sharpness and accuracy in predicting the variables which are considered to have influence on variables can be obtained. On the contrary, measurement error which takes place from relation between research variables can be explained. Accordingly, this analysis technique is considered “comprehensive” to improve accuracy of conclusion generalization in the field of educational research which currently undergoes more complex problem. Keywords: data analysis, educational variables, LISREL technique Cite This Article: Awaluddin Tjalla, “Improving Accuracy of Educational Research Conclusions by Using Lisrel.” American Journal of Educational Research, vol. 3, no. 5 (2015): 619-623. doi: 10.12691/education-3-5-14. 1. Introduction Globalization which is characterized by advancement in technology has resulted in problems on human life interaction, including those in educational field. In the perspective of research methodology, augmenting problems in educational field either quantitatively or qualitatively require settlement with correct technique of data analysis. This is aimed at obtaining objective result of data analysis and can be generalized accurately. Data analysis in educational field is not as easy as that in the other fields, such as : industry, engineering, agriculture, economics, etc., in which their required statistical analysis can easily be fulfilled. Interpretation made does not bear high risk compared to the more complex data of educational variables. Therefore, correct technique of statistical analysis is required. Pophamand Sirotnik (1973) mentioned two main objectives of statistical use in a bid to analyze variables data in the educational field, they are [19]: 1. Statistical techniques to describe data (descriptive satistics). This statistics is used to infer numerical data, such as : test scores, age and educational year. 2. Statistics used by researchers to describe better inference against a phenomenom observed at sample and then generalized conclusion of population is taken. This analysis technique points to relation between variables. In such case, educational researchers try to describe relation between such variables as students’ IQ, achievement and attitude to learning program at school. With regard to the second objective of using statistics (inferensial statistics),the commonly used technique of statistical analysis in the educational field is regression analysis by applying production function approach (Draper and Smith, 1981) [3]. However, such model constitutes underlying weaknesses in terms of : (1) concept, (2) result measurement, and (3) some biased sources which are the characteristics in the educational field. Accordingly, by applying regression with Ordinary Least Squares (OLS) approach, such disturbances need serious attention and required assumption can be met. This is because assumption break may cause inccorrect and misleading conclusion generalization due to possible misinterpretation (Suriasumantri, 2000) [22]. 2. Analysis Path analysis is commonly applied as well in analyzing educational variables data irrespective of its weaknesses. The following are the weaknesses (Mueller, 1996) [18]: (1) When assumption of series of variables events (one variable cannot precede the other and vice versa) is not met, then path analysis cannot be applied; (2) It is always assumed that error outside the system has no correlation between one and another; (3) Difficulty in determining unknown parameters of the available data; (4) Direction of cause based relation cannot be determined by analysis result but dependent upon the concept developed by researchers. In case of any faulty concept, then conclusion and interpretation will be faulty as well; and (5) Principally, path analysis is the application of equation of