Given the significant role of quantitative data in theory building in language assessment, the need for a book covering data analysis methods is strongly felt. The two-volume book, Quantitative Data Analysis for Language Assessment (Fundamental Techniques and Advanced Methods), edited by Vahid Aryadoust and Michelle Raquel and published by Routledge fills this gap. The first volume entitled Quantitative Data Analysis for Language Assessment: Fundamental Techniques was published in 2019, and the second volume by the title of Quantitative Data Analysis for Language Assessment: Advanced methods was published in 2020. The present study is critical in goal, qualitative in methodology, and emergent in analysis. The book is comprehensive in scope and content, includes high-quality, well-written chapters follows a coherent style of writing, and is rich in the number of quantitative data analysis methods. The book has some limitations, including inexplicitness of criteria for selection of quantitative data methods and failure to include more common traditional methods. The book can generate further research in this subfield of applied linguistics, expand the subfield, and contribute to theory building in language assessment. Postgraduate students, L2 assessment practitioners, and researchers are strongly advised to read this invaluable edited book.

The aim of this study was to combine the phenomenology of religious education curriculum with experimental sciences. This research was conducted qualitatively with a phenomenological approach. The statistical population of this study was the designers and specialists of curriculum planning and theology, as well as teachers with master's and doctoral degrees. Participants in this study were selected by purposive sampling method of 15 people to reach the theoretical saturation point. Data were collected using semi-structured interviews. The purpose of selecting these individuals was their experience and inclusion in the text of the integrated curriculum. After data collection and data analysis, a concept map of data analysis was presented. The data were analyzed based on content analysis. The results of data analysis were classified into 3 organizing themes: 1) explaining the content areas of the integration, 2) how to formulate the objectives, and 3) how to conduct the integrated evaluation.