Descriptive statistics plays a crucial role in summarizing and interpreting quantitative data, offering the necessary tools to organize and visualize data effectively. This chapter explores the techniques used to summarize and describe the main features of sample data. It guides you in selecting the appropriate descriptive statistics, such as measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). Detailed explanations of visual representations, including histograms, boxplots, and scatter plots, are provided to improve your data interpretation. Additionally, the chapter covers grouped data, frequency distributions, and advanced concepts such as percentiles, quartiles, and confidence intervals. By the end of this chapter, you will be prepared to apply descriptive statistics with confidence, ensuring that your research findings are both precise and insightful.

This chapter provides a comprehensive guide to preparing effective research reports, including theses, dissertations, and journal articles. It covers the purpose, structure, and key components necessary for clearly communicating findings. The chapter begins with fundamental principles of writing style and language, which are essential for creating effective research reports. These principles apply to every section of a research report, including the introduction, methodology, results, and conclusion. Key topics include document formatting, major sections, and subsections, all aimed at enhancing readability and impact. The chapter also discusses different types of abstracts, emphasizing the characteristics of a well-crafted informative abstract. Additionally, it provides guidance on constructing effective section such as literature reviews, methods, results, and statistical reporting, along with effective discussions and conclusions. Finally, the chapter highlights the importance of proper citation and referencing in research reports. By the end, you will be able to prepare, structure, and present research documents that effectively communicate findings and adhere to academic standards.

This chapter explores the essential concept of research variables, highlighting their significance in applied linguistics research. We will begin by defining what a research variable is and explore its pivotal role in shaping your studies. You will learn to distinguish between research constructs and variables, and gain confidence to understand and use these concepts effectively. We will also examine different types of variables, including independent, dependent, and control variables and also discuss the crucial process of operationalizing constructs and variables, highlighting measurement tools and techniques that can enhance the reliability of your findings. Throughout the chapter, practical examples from applied linguistics research will demonstrate how these concepts are applied in real-world contexts. Finally, we will introduce various measurement scales, ensuring you gain a clear understanding of how to quantify and analyze your research data accurately.

This chapter discusses the crucial role of sampling in quantitative research. Selecting an appropriate sample is essential, as it directly impacts the validity and reliability of the study’s findings. The chapter explores various sampling procedures and the key factors that must be considered when determining both the sampling method and the appropriate sample size. It begins by clarifying the concepts of sampling frames and populations, discussing their significance in selecting a representative sample from a larger group. The chapter then examines various sampling strategies, including probability and non-probability methods and discussing the advantages and disadvantages of each. Understanding these approaches will enable you to make informed decisions tailored to your specific research questions. Finally, the chapter guides you through calculating the required sample sizes for your studies and examining the factors that influence sample size determination. By the end of this chapter, you will not only see the importance of effective sampling but also have the knowledge to apply these concepts confidently in your own quantitative research studies.

This chapter explores the important concept of reliability and its significance in quantitative research. You will gain a comprehensive understanding of different types of reliability, including test–retest reliability, interrater reliability, and internal consistency. The chapter will guide you through various methods for assessing reliability, such as the split-half method, coefficient alpha, and intraclass correlation, supported by practical examples. By the end of this chapter, you will have learned not only how to conduct and calculate reliability estimates for various measures and instruments, including Cohen’s kappa and correlation coefficients, but also how to interpret these estimates effectively. The chapter will also discuss the advantages and disadvantages of each type of reliability, helping you recognize their implications for your research. Additionally, you will discover strategies to enhance reliability and increase the rigor of your data collection procedures.

This chapter provides a comprehensive overview of the most common types of quantitative data collection tools used in applied linguistics research, with practical examples. It will explore various methods for collecting quantitative data, including a range of tests, tasks, and measures tailored to assess specific language skills, affective factors, and cognitive variables. Understanding the design, implementation, and application of these tools is crucial for conducting effective research, which will be thoroughly addressed throughout the chapter. Additionally, the chapter evaluates the strengths and limitations of different quantitative data collection methods, helping you determine their suitability for your specific research goals. By the end of this chapter, you will be well prepared to choose and apply the appropriate data collection tools for your research, enhancing the rigor and impact of your findings in the field.

In the previous chapter we learned how satellite data to estimate various water targets such as precipitation and surface water, can be combined in a model-reservoir system to track a reservoir’s dynamic state and understand river regulation. In this chapter we will cover how satellite data can be used to manage crops and irrigation. We will learn how satellite data can be used to estimate an area under a specific crop using classification techniques, which then helps us understand the water need for that area. Next we will learn methods to estimate crop water demand and actual crop water consumption.

In this chapter, we will cover the remote sensing of precipitation to understand how precipitation is tracked. Precipitation is considered one of the most important components of the water cycle that drives the availability of water and its management. For example, precipitation leads to runoff and streamflow, irrigates a field of crops and provides the water for crop growth, fills up lakes, reservoirs and ponds that are a key source for water management. The understanding of precipitation remote sensing will pave the way for learning more complex water management applications that are being increasingly carried out around the world today using satellite water data. We will first cover the history of precipitation remote sensing that began with using active sensing and ground radar. Next, we will cover satellite-based sensing where the challenges and complexities are different. The pros and cons of using various electromagnetic wavelengths will be covered. Finally, we will cover the topic of multi-sensor precipitation estimation based on the synergistic use of multiple satellite sensors spanning different wavelengths of the electromagnetic spectrum.

In the previous chapter we covered how satellite remote sensing can be used to classify areas under a crop, estimate their crop water demand and actual crop water consumption. This information can be used for irrigation management using satellite data. In this chapter we will cover how satellite data can be used to estimate temperature of surface water. We will cover the basic principle behind the estimation technique, understand the limitation of the technique and then build some data literacy to derive the surface temperature of water in regulated rivers ourselves.

In this Chapter, we will explore how reservoirs can be monitored from space for water management. Today it is now possible to track the dynamic state of reservoirs at temporal and spatial scales of satellite remote sensing. This dynamic state comprises inflow, outflow, surface area, storage change and evaporative losses. Most of these variables can be modeled using satellite data or directly estimated using satellite data. This chapter will introduce readers to the Reservoir Assessment Tool (RAT) that we have developed as an open-source complete package for users to use the full power of satellite remote sensing to track reservoirs anywhere.

This chapter will explore the topic of citizen science in the context of water management using satellite remote sensing. This is a broad field and the goal here is to expose readers to a social yet important issue of using citizens to carry out science for building more robust management solutions. As mentioned earlier in Chapter 11, this chapter is in no way comprehensive. The objective here is to encourage readers to start thinking about the idea of citizen science and the positive role it can play in building more equitable satellite-based water management solutions