Chapter 2 covers the basics of research design.It is written so that students without any research design experience or coursework can learn common research designs to enable them to conduct statistical analyses in the text.Hypotheses development with variable construction (dependent and independent variables) are covered and applied to experimental and non-experimental designs.Survey methods including question construction and implementation of surveys is presented.

We have analyzed many variables in Cantonese but not in other languages: classifier specialization, tone mergers, vowel splits and mergers, motion event expression, and (L > R), as well as (VOT) and (PRODROP). As little sociolinguistic work on any variety of this globally large language exists, these studies serve as useful models to expand variationist studies to languages that vary in many ways from the North American, Indo-European languages of focus to date. We show that classifiers are developing a specific semantic contrast (for number-marking) in Heritage Cantonese, amplifying a homeland trend; that three tone mergers that were reported to be completed are only partial, in both homeland and heritage varieties; that some vowel mergers and splits may be attributed to influence from English, but that changes in the constraints governing motion event expression cannot be attributed to simplification or English-contact effects. We report on covariation among the variables, showing that it is not the case that the same speakers lead change in each. Thus, it is not easy to claim that language proficiency or patterns of use are responsible for the variation. Rather, internal change and identity-marking motivations for change must be considered.

The variables examined in Chapters 5 and 6 show little evidence of being used for identity work. That is, they do not show (consistent) effects of ethnic orientation measures or speaker sex. This chapter explicitly contrasts variables that reflect indexicality (correlation to social factors) in homeland varieties to non-indexical variables. We begin by considering three indexical variables in Italian: (VOT) in unstressed-syllable contexts, (APOCOPE), and (R), illustrating the extent to which indexicality is maintained in the heritage variety. We find increasing use of the more standard variant only in (VOT). Furthermore, we find that younger speakers (both in homeland and heritage) favour the non-standard variant. We then compare the variable (R), the contrast between trill (or tap) and approximant variants, in Italian and Tagalog, where it has indexical value in the homeland varieties, to Russian and Ukrainian, where it does not. Finally, we consider two additional indexical variables: Cantonese denasalization and Korean VOT. We conclude by contrasting the behavior of homeland-indexicals in heritage varieties. The presence of indexical value in homeland varieties does not consistently influence outcomes in the heritage varieties.

The prime ministers all play chess on a multi-dimensional board, prey to challenges that vary in type and intensity over time, some of which are new and growing, and others constant. The most skilful negotiate their way through these constraints, turning them to their advantage, and refuse to be defined by adversities. The least able are swallowed up by them. We first consider institutional restraints, the checks and balances they face, some dating back to 1721, before considering variable constraints, which have made and destroyed premierships, and have rendered even the best-qualified incumbent a cornered animal.

Until recently, algebra was regarded as the domain of the secondary school years in most countries. In addition, it was often regarded in quite narrow ways by non-mathematics teachers, parents and students as being concerned with the manipulation of symbols according to tightly prescribed rules. Recent attention to algebra in the primary school has not regarded it as appropriate that such a narrow view of algebra be taken, leading to the use of terms such as ‘pre-algebra’ or ‘early algebra’ to describe the mathematics involved.

In this chapter, it is recognised that students’ understanding of algebra in the secondary school rests on foundations that are laid in the primary school, as reflected in the Australian Curriculum: Mathematics v. 9.0. These foundations are concerned with key algebraic ideas about patterns and generalisations, rather than with symbolic representations of these, such as x and y. This chapter explores developmental models associated with patterns and algebraic concepts, with a focus on developing algebraic thinking.

This chapter examines suitable statistics questions for investigation by children of different ages, using a cycle of problem, plan, data, analysis and conclusion (PPDAC) (Wild & Pfannkuch 1999). The importance of variation in data and different types of variables and the difference between a population and a sample are investigated. Readers will explore different ways of displaying data to ‘tell a story’. The importance of drawing inferences from data and the uncertainty associated with these inferences are discussed. Readers will engage in activities that use technology to support the development of statistical understanding.

This chapter provides an overview of the processes that are commonly used for analyzing data. Our intention is to explain what these processes achieve and why they are done. Analyzing data goes through four stages. For each stage, we explain the most important concept and then explain the practical steps that are involved. This begins with the data themselves as variables. Next, we move on to describing the data, their variance and covariance, with linear models. Next, we cover interpreting effects and focus on effect sizes. We end with a discussion of inferences about the population and how the presence of uncertainty has to be taken into account in reaching conclusions.

This chapter sets out the elements of multiple regression analysis. If properly designed this enables us to estimate the effect of each separate factor upon wellbeing. To find the explanatory power of the different factors, we run the equation using standardised variables, that is, the original variables minus their mean and divided by their standard deviation. The resulting coefficients – or partial correlation coefficients – reflect the explanatory power of the independent variation of each variable.

The surest way to determine a causal effect is by experiment. The best form of experiment is by random assignment. We then measure the wellbeing of the treatment and the control group before and after the experiment. The difference-in-difference measures the average treatment effect on the treated. Where random assignment is impossible, naturalistic data can be used and the outcome for the treatment group compared with a similar untreated group chosen by Propensity Score Matching.

This chapter provides an introductory coverage of the major issues involved in designing and executing sociolinguistic research with a focus on spoken Arabic in natural settings. It explains the concept of the observer’s paradox and suggests methods to reduce its effects in sociolinguistic interviews. It covers ethnographic, qualitative, and quantitative methods. The use of dependent and independent variables is explained in detail, with a focus on age as a social variable. The chapter ends with ethical considerations as an integral part of research and research conduct.

Many parameters are associated with IHC testing assays. With so many variables, it is quite easy to accumulate errors within the system. To make things more manageable, these considerations are categorized into three main groups. Pre-analytic aspects occur before the assay, analytic factors are concerned with the staining protocol and post-analytic elements relate to interpreting of results. It has also come to reason that any one variable can impact the reliability and consistency of the overall IHC assay. In this regard, standardization requirements have been enlisted to assist laboratories achieve optimal results. In addition, monitoring proficiency testing regimens and various organizations are in place to ensure high levels of standards are attained. All these endeavours are known as quality assurance and quality control measures. They are arranged under the overall umbrella of a facility’s quality management system.

This chapter introduces key elements of the basic syntax and semantics. Topics of discussion include the treatments of compositional semantic value, assignment functions and variables for assignments, pronouns and traces, and quantification in object language and metalanguage. A preliminary compositional semantic derivation is provided.

Research on purpose starts with learning how to look at purposeful behavior as a process of control. This means learning to see behavior as being organized around the control of perceptual variables. The next step is to try to see what these variables might be. Ideas about what these variables might be come from the names we commonly use for various behaviors, as well as from existing research studies. Once you have an idea of what variable or variables an organism is controlling, you can refine that hypothesis by looking at the control process from the point of view of the behaving system itself. The refined hypothesis is then tested using the method of specimens, which involves testing one organism at a time under all experimental conditions. The results of this research are evaluated in terms of scientific rather than statistical significance.

Objective: Delve into programming logic and flow, VBA syntax, and debugging tools. Become familiar with code structure, communication with spreadsheets, dynamic data storage, conditional statements and loops, calling worksheet functions, and creating user-defined ones.

This chapter introduces object-oriented programming and explains how to make use of it in Python. It covers the basic syntax of defining and using objects. It also introduces the object inheritance system and closes with an extended example of object-oriented syllable structure.

This chapter introduces the different data types of Python: integers, characters, strings, lists, dictionaries, tuples, etc. The chapter also treats the concept of mutability.