The focus of this article is to evaluate the maximum likelihood estimation (MLE) performance in estimating the person parameters in the Rasch rating scale model (RRSM). For that purpose, 1000 iterations of the Markov Chain Monte Carlo (MCMC) simulation technique were performed based on a different number of sample sizes and several number of items. The performance of MLE in estimating the person parameters according to the different number of sample sizes was compared through accuracy and bias measures. Root mean square error (RMSE) and mean absolute error (MAE) were used to examine the accuracy of the estimates, while bias in estimation was assessed through the mean difference of estimates and true values of the person parameters. The simulated survey data sets in this study were generated according to the RRSM under the assumption of normality was satisfied. Results from the simulation analysis showed that in comparison to the larger sample sizes, smaller sample sizes tend to produce higher RMSE and MAE. In addition, the maximum likelihood estimates of the person parameters in smaller sample sizes also recorded a higher value of the mean difference of the person estimates and its true values compared to larger sample sizes. Findings from this study imply that the use of the MLE approach in small sample sizes results in less accurate and highly biased person estimates across the number of items.

In recent years, there has been increasing in the number of research focusing on subjective well-being issues in many countries all over the world. It also received considerable attention from the Malaysian government nowadays in order to improve the overall quality of life of the people in the country. Subjective well-being concerning with people happiness and overall life satisfaction towards their own day-to-day life experience. This systematic review is conducted to explore and highlight the determinants of subjective well-being to be research on in detail in the future study. A total of 60 articles obtained from academic search engines and online databases which are Google Scholar, ScienceDirect and Scopus have been chosen to be reviewed within a period from September 2017 until January 2018. Only articles obtained through journals indexed in Scopus and ISI will be included in this study, with only 33 articles eligible for final review. From the review, it was found that besides personality factors, health, religious commitment and spirituality, the socio-economic attributes such as income, financial and employment status are the most focusing determinants of subjective well-being among the researchers. For future research, it is recommended that to further study by conducting an interview with the target respondents, reviewing relevant articles for the purpose of identifying the suitable measurement instrument to be used for the survey and then conducting empirical research for determining the most contribution factors of subjective well-being among the society as the results that will obtain through empirical research will provide more accurate findings and will become a useful guidance for the government in implementing the effective and relevant policies which benefits to the people in the country.

Subjective well-being is focusing on happiness and life satisfaction of the people. This review is carried out to examine the contributing factors of people's happiness and life satisfaction and also to determine the measurement instrument used in conducting a survey. A total of 60 articles obtained from academic search engines and online databases which are Google Scholar, ScienceDirect and Scopus have been chosen to be reviewed within a period from September 2017 until January 2018. Only 33 articles indexed in Scopus and ISI are eligible for final review. Keywords: Subjective well-being; happiness; life satisfaction eISSN 2514-7528 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.

This paper focuses on the methods used for estimating the parameters in Rasch Measurement Model (RMM). These include the MLE and Bayesian Estimation (BE) techniques. The accuracy and precision of the parameter estimates based on these two MLE and BE were discussed and compared. A questionnaire is a well-known measurement instrument used by most of the researchers. It is a powerful tool for collecting data in survey research. It should be noted that the quality of a measurement instrument used plays a key role in ensuring the quality of data obtained in the survey. Therefore, it has become essential for the researchers to carefully design their questionnaire so that the quality of the data obtained can be preserved. Then, it is also vital for the researchers to assess the quality of the data obtained before it can be successfully used for further analysis. Review of the literature shows that RMM is a psychometric approach widely used as an assessment tool of many measurement instruments developed in various fields of study. At present, the Maximum Likelihood Estimation (MLE) techniques were used to estimate the parameters in the RMM. In order to obtain more precise and accurate parameter estimates, a certain number of sample size and normality assumption are usually required. However, in a small sample, MLE could produce bias, imprecise and less accurate estimates with bigger standard error. A proper selection of the parameter estimation techniques to deal with small sample and non-normality of data is required to obtain more precise and accurate parameter estimates. From the review, it reveals that BE has successfully dealt with the issues of small sample and non-normality of the data. It produced a more accurate parameter estimate with smallest Mean Squared Error (MSE), particularly in a small sample compared to MLE.

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