EDUCATION MANAGEMENT RESEARCH DATA ANALYSIS: COMPARISON OF RESULTS BETWEEN LISREL, TETRAD, GSCA, AMOS, SMARTPLS, WARPPLS, AND SPSS FOR SMALL SAMPLES

Abstract - The purpose of this study is to compare the results of quantitative research data processing in the field of education management using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software for small samples or respondents. This research method is quantitative and research data analysis uses the four types of software to obtain a comparison of the results of the analysis. The analysis in this study focuses on the analysis of hypothesis testing and regression analysis. Regression analysis is used to measure how much influence the independent variable has on the dependent variable. The field of this research is education management and the research data uses quantitative data derived from questionnaire data for a small sample of 32 respondents with three research variables, namely the independent variable of transformational leadership and job satisfaction, while the dependent variable is teacher performance. Based on the results of the analysis using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software, the results showed that for a small sample there was no significant difference in the significance value of p-value and t-value. There is also no significant difference in the determination value, and the correlation value in the resulting structural equation also has no significant difference in results, while for CB-SEM represented by Lisrel, Tetrad cannot process data with a small sample size.


INTRODUCTION
Many researches in the field of education management use statistical software tools such as Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Many researchers in the field of education management are still hesitant in choosing which software to use. There are many researches in the field of education that use SmartPLS, such as those conducted by Budi Santoso, P., Asbari, M., Siswanto, E., & Fahmi, K. (2021). examines the role of job satisfaction and organizational citizenship behavior on performance. Putra, F., Asbari, M., Purwanto, A., Novitasari, D., & Santoso, P. B. (2021) investigated linking social support and performance in college. Johan, M. (2021) examines the effect of knowledge sharing and interpersonal trust on innovation. Purwanto, A., Santoso, PB, Siswanto, E., Hartuti, H., Setiana, YN, Sudargini, Y., & Fahmi, K. (2021) the effect of hard skills, soft skills, Nidhomul Haq, Vol 6, Issue 2, 2021 Agus Purwanto et al 384 organizational learning and innovation capability on performance lecturer. Novitasari, D., Asbari, M., Purwanto, A., Fahmalatif, F., Sudargini, Y., Hidayati, L. H., & Wiratama, J. (2021). examined the effect of social support factors on the performance of elementary school teachers. Anas Ahmadi, E., Herwidyaningtyas, F.B., & Fatimah, S. (2020) examined the influence of organizational culture, work motivation, and job satisfaction on lecturer management performance. Jayus, J.A. (2021). Examining the effect of distributive justice, procedural justice and interactional justice on teacher involvement and teacher performance. Ahmed, U., Umrani, W. A., Qureshi, M. A., & Samad, A. (2018). examined the relationship between teacher support, academic efficacy, academic resilience, and student engagement in Bahrain. There are many researches in the field of education that use SPSS, such as those conducted by ebjan, U., & Tominc, P. (2015) on the impact of teacher support and conformity with learning needs on the use of SPSS by students. Murtiningsih, M., Kristiawan, M., & Lian, B. (2019)

METHOD
This research method is quantitative, research data analysis uses Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS software to obtain a comparison of the results of the analysis. The analysis in this study

Result and Discussion
A. Testing the Significance of t-Value The first stage of data analysis is testing the significance of the relationship between the independent variable transformational leadership (X), job satisfaction (Y1) with the dependent variable teacher performance (Y2) by looking for t-Value using Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS, the decision criteria if the t-Value value is greater than 1.96 or > 1.96 then the relationship is significant, if less than 1.96 or < 1.96 then the relationship is not significant. For WarpPLS does not produce a t-statistic value, the significance test can be seen on the p-value, so that the tstatistic value will be obtained. The test results with 4 software for a direct relationship can be seen in Table 1  Relationship between transformational leadership (X) and job satisfaction (Y1) Based on the results of the software analysis, the results of the t-Value using Amos data cannot be processed. The t-Value using SmartPLS is 89,509, which is greater than 1.96, so it can be concluded that the relationship between X and Y1 is significant. The result of t-Value using SPSS is 21.424 which is greater than 1.96 so that it can be concluded that the relationship is significant so that it can be concluded that SmartPLS and SPSS give the same results.
Relationship between transformational leadership (X) and performance (Y2). Based on the results of the software analysis, the results of the t-Value using Amos using Amos data cannot be processed. The t-Value using SmartPLS is 1.960, which is greater than 1.96, so it can be concluded that the relationship between X and Y2 is significant. The results of the t-Value using SPSS of 2.125 are greater than 1.96 so that it can be concluded that the relationship between X and Y2 is significant, so it can be concluded that SmartPLS and SPSS give the same results.
The relationship between transformational leadership (X) and performance (Y2) through job satisfaction (Y1) Based on the results of the software analysis, the results of the t-Value using Amos data cannot be processed. The result of t-Value using SmartPLS is 0.822 which is smaller than 1.96 so that it can be concluded that the relationship between X and Y2 through Y1 is not significant. The result of t-Value using SPSS of 1.051 is smaller than 1.96, so it can be concluded that the relationship between X and Y2 through Y1 is not significant, so it can be concluded that SmartPLS and SPSS give the same results.

B. Testing the Significance of p-Value
The second stage is data analysis, namely testing the significance of the relationship between the independent variable transformational leadership (X), job satisfaction (Y1) with the dependent variable teacher performance (Y2) by looking for pvalue using SPSS, Amos, SmartPLS, WarpPLS and SPSS software. The decision is that if the p-value is less than 0.050 or <0.050 then the relationship is significant, if it is more than 0.050 or >0.050 then the relationship is not significant. The test results with 4 software for direct connection are as follows:  Relationship between transformational leadership (X) and job satisfaction (Y1) Based on the results of the software analysis, the p-value results using Amos data cannot be processed. The p-value using SmartPLS is 0.000 less than 0.050 so it can be concluded that the relationship between X and Y1 is significant. The p-value using WarpPLS is 0.000 less than 0.050 so it can be concluded that the relationship is significant. The p-value using SPSS is 0.000 less than 0.050, so it can be concluded that the relationship between X1 and Y1 is significant, so it can be concluded that WarpPLS, SmartPLS and SPSS give the same results.
The relationship between transformational leadership (X1) and performance (Y2) Based on the results of the software analysis, the p-value results using Amos data cannot be processed. The p-value using SmartPLS is 0.046 which is smaller than 0.050, so it can be concluded that the relationship between X and Y2 is significant. The result of p-value using WarpPLS is 0.410 less than 0.050 so that it can be concluded that the relationship between X and Y2 is significant. The result of the p-value using SPSS is 0.042 less than 0.050 so it can be concluded that the relationship between X and Y2 is significant t so that it can be concluded that WarpPLS. SmartPLS and SPSS give the same result. The relationship between transformational leadership (X1) and performance (Y2) through job satisfaction (Y1) Based on the results of software analysis, the p-value using SmartPLS was 0.411 more than 0.050, so it was concluded that the relationship between X and Y2 through Y1 was not significant. The result of p-value using WarpPLS is 0.450 which is greater than 0.050 so that it can be concluded that the relationship between X and Y2 through Y is not significant. The results of the p-value using SPSS of 0.302 is greater than 0.050 so that it can be concluded that the relationship between X and Y2 through Y is not significant.

Coefficient of Determination Test
Testing the coefficient of determination to calculate the influence of the independent variable on the dependent variable. In this study, the R Square termination coefficient was calculated for the independent variables of transformational leadership (X), Job Satisfaction (Y1) and Performance (Y2   Table 4, the R Square value for Performance (Y2) using Amos cannot be run. The value of R Square for Performance (Y2) using SmartPLS is 0.852 or 85.2%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) is 85.2% while the remaining 14.8% is influenced by other variables not discussed in this study. The R Square value for Performance (Y2) using SmartPLS is 0.85 or 85%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) by 85% while the remaining 15% is influenced by other variables. which were not discussed in this study. The value of R Square for Performance (Y2) using SmartPLS is 0.844 or 84.4%, meaning that the performance variable (Y2) is influenced by transformational leadership variables (X) and job satisfaction (Y1) is 84.4% while the remaining 15.6% is influenced by other variables not discussed in this study.

Correlation Coefficient Test
The correlation coefficient shows the strength of the linear relationship and the direction of the relationship between variables. If the correlation coefficient is positive, then the two variables have a unidirectional relationship (Purwanto et al, 2020). This means that if the value of the variable X is high, then the value of the variable Y will be high as well. Conversely, if the correlation coefficient is negative, then the two variables have an inverse relationship. This means that if the value of the variable X is high, then the value of the variable Y will be low and vice versa. According to Hair et al (2017) to make it easier to interpret the strength of the relationship between two variables, the following criteria are provided: The results of structural equations using Lisrel, Tetrad and Amos software cannot be run. The results of the structural equation using SmartPLS software obtained the equation is Y2 = 0.642X1+0.287X2+e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.642, meaning that there is a strong correlation and indicates that if the value of transformational leadership ( X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.642 units. This means that the partial effect of transformational leadership on performance is 64.2%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.287, meaning that there is a sufficient correlation and shows that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.287 units . This means that the effect of job satisfaction (Y1) on performance partially is 28.7%.
The results of the structural equation using WarpPLS software obtained the equation is Y2 = 0.658X1+0.271X2+e, meaning that the correlation coefficient value of the influence of transformational leadership The results of the structural equation using SPSS software obtained the equation is Y2=0.219+ 0.642X1 + 0.324Y1 + e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.642, meaning that there is a strong correlation and indicates that if the value of leadership transformational (X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.642 units plus a constant of 0.219 units. This means that the partial effect of transformational leadership on performance is 64.2%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.324, meaning that there is a sufficient correlation and indicates that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.324 units plus the constant 0.219 units . This means that the effect of job satisfaction (Y1) on performance partially is 32.4%.
The results of the structural equation using the GSCA software obtained the equation is Y2=a+ 0.703X+ 0.229Y1 + e, meaning that the correlation coefficient value of the influence of transformational leadership variable (X) on performance (Y2) is 0.704, meaning that there is a strong correlation and shows that if the value of transformational leadership ( X) increases by 1 unit, while the value of job satisfaction (X2) remains, the performance value (Y2) will increase by 0.703 units n. This means that the partial effect of transformational leadership on performance is 70.3%. The correlation coefficient value of the influence of job satisfaction variable (Y1) on performance (Y2) is 0.229 meaning that there is a sufficient correlation and indicates that if the value of job satisfaction (Y1) increases by 1 unit, while the value of transformational leadership (X) remains, the performance value (Y2 ) will increase by 0.229 units plus the constant 0.229 units . This means that the effect of job satisfaction (Y1) on performance partially is 22.9%.

CONCLUSION
Based on the results of the analysis using GSCA, SPSS, SmartPLS and WarpPLS software, the results showed that for a small sample there was no significant difference in the significance value of p-value and t-value. There is also no significant difference in the determination value produced, and the correlation value in the resulting structural equation also has no significant difference in results, while for CB-SEM represented by