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This chapter provides a theoretical overview of reliability, as well as pragmatic considerations in establishing different types of reliability. To illustrate key points, it draws from two scales: the Family Responsibility Scale and the Parental Self-Care Scale. Various forms of reliability are addressed, including interrater, test-retest, and internal consistency. Guidelines for interpreting reliability coefficients for clinical and research purposes are provided, including computation of stratified alpha for multidimensional measures. Computation of the standard error of measurement (SEM) is illustrated. The chapter concludes by asserting that a solid reliability coefficient is indispensable as a primary principle in assessing the quality of scores from a scale or test.

This chapter outlines the methods used in the empirical part of the book and addresses issues such as validity and reliability, both in general and also with specific reference to the empirical analysis of the book. Furthermore, the link is made between the theoretical definitions of the central concepts and the variables investigated in Chapter 6.

This chapter places Cassian's monastic programme within the milieu of Nitrian asceticism and redresses the arguments that have recently been advanced against Cassian's reliability as an historian. It brings to bear in the study of Cassian's works the significant results from contemporary scholarship that have effectively demonstrated that the Fathers of the Egyptian desert were possessed of a theological culture of considerable vitality, and that stereotypes about the pursuit of intellectual activity (not least allegorical interpretation of Scripture in the manner of Philo, Origen, and others) cleaving along ethnic lines are simply unsound.

Provides a discussion of issues relating to research design and experimental methods in political science. Issues are elaborated relating to control and random case selection, internal and external validity, identification of mediating variables, and replication. Examples of experimental contributions in political science are outlined in the fields of public opinion research, decision‐making and information processing, collective action, public choice, and public policy. Experimentation represents a burgeoning cutting‐edge approach in the future of political science research.

This chapter begins by reviewing the shortcomings of the official U.S. measure of poverty, arguing that it is unreliable and invalid. Then, the chapter reviews major theoretical and methodological advances in poverty measurement and advocates five criteria in the measurement of poverty: (1) to measure comparative historical variation effectively, (2) to be relative rather than absolute, (3) to conceptualize poverty as social exclusion and capability deprivation, (4) to incorporate taxes and transfers, and (5) to integrate the depth of poverty. Overall, the aim is to facilitate the integration of theoretical and methodological advances into the empirical measurement of poverty. Also, criticisms are made of absolute measures of poverty, the measurement of poverty before taxes and transfers, and measures of redistribution. This chapter makes a theoretical argument regarding how poverty should be measured for the study of affluent democracies.

This chapter examines the phenomenon of automimicry, where individuals within a population may share the same warning signal but differ in their investment in defence. It seeks to explain the evolution of this variability and how the predator’s continued appropriate response to the warning signal can be maintained in the face of this potential decrease in signal reliability. It also considers the use of mimicry by predators (aggressive mimicry), floral mimicry that attracts pollinators, and intraspecific sexual mimicry.

We respect certain epistemic norms — including (roughly) that one should tentatively believe things to have the colors they seem to have, should conform to modus ponens and the principle of non-contradiction, and should reason in accord with induction. But what could make these (or certain alternative norms, perhaps) the right ones? What could explain their correctness? This chapter scrutinizes the most commonly offered answers to this question. Special attention is devoted to the ‘semantogenetic’ strategy whereby the correctness of such norms is grounded in the meaning-engendering features of words. (For example, it's sometimes said that the axioms of arithmetic should be accepted because they implicitly define the primitive arithmetical terms). But amongst the competing strategies briefly examined here are proposals that invoke rational intuition, those that are militantly internalistic, those that prioritize considerations of reliability, and those that aim to ground the facts of epistemic rationality ‘constructively’ in normative commitments implicit in our linguistic activity. It is argued that none of these approaches is remotely adequate. And it is suggested, in conclusion, both that the correctness of the above-mentioned norms cannot be explained, and that this result should be neither surprising nor worrying.

This chapter examines participant selection, sampling design, sample size and sampling error; as well as the importance of statistical power, effect size, confidence levels, and confidence intervals. Types of sampling, including probability and nonprobability sampling methods, are discussed in relation to both quantitative and qualitative research designs. The measurement properties of instruments including requirements of validity and reliability as well as issues in measurement with human measures (credibility, inquiry audits, and triangulation) are presented.

This chapter briefly introduces the robust-weak-fusion model, which offers an exceptionally clear and elegant framework within which to understand empirical studies on cue combination. Research on cue combination is an area in the cognitive neurosciences where quantitative models and predictions are the norm rather than the exception—and this is certainly a development that this book welcomes wholeheartedly. What they view critically, however, is the strong emphasis on so-called optimal cue combination. Optimal in the context of human cue combination typically refers to the minimum-variance unbiased estimator for multiple sources of information, corresponding to maximum-likelihood estimation when the probability distribution of the estimates based on each cue are Gaussian, independent, and the prior of the observer is uniform (noninformative). The central aim of this chapter is to spell out worries regarding both the term optimality as well as against the use of the minimum-variance unbiased estimator as the statistical tool to go from the reliability of a cue to its weight in robust weak fusion.

Predators that rely on surprise may be persuaded to desist from attacking if prey use reliable signals that the predator has been detected. Prey may also be able to reliably signal to a predator that they are difficult to catch or subdue, and that cause the predator to desist from attacking or switch their attack to another prey individual. The theory underlying such signals is considered and compared to the available empirical data to determine the evolution of such signals and their ecological prevalence.

The proximal stimulation at ear and eye is neutral, the excitations from different sources are intermixed, and the excitations can be organized in several ways to yield different percepts. The Gestalt psychologists proposed laws of organization due to cortical field forces that acted to knit the visual excitations into continuous enclosed objects. The resulting objects were described as the “simplest” possible, termed prägnanz, although prägnanz was never clearly defined. Similar laws of organization have been proposed for auditory excitation. The most important is onset synchrony, namely auditory excitations that start at the same time are assumed to come from one source. When there is simultaneous auditory and visual excitation, the normal unity assumption is that both excitations come from the same source. If there is a conflict in the two excitations, the compellingness and reliability of the excitations will determine how each is weighted in importance.

This chapter considers how a neural system could properly combine cues even as their reliabilities change dynamically and continuously in time. It describes a framework for characterizing how uncertain information may be represented in population codes, and how the representation may be optimized to facilitate optimal decoding. It then motivates and presents a coding scheme within this framework that applies to time-varying stimulus variables. Next, it describes a hierarchical network setup that utilizes this coding approach, and demonstrates an application to a dynamic cue-combination task—a novel adaptation of a sensorimotor task. Specifically, it considers how a neural population might recursively integrate dynamic inputs from multiple sensory modalities with learned prior information, to determine appropriate motor commands that control behavior; the results of simulated experiments with this network are presented. Finally, the implications of this scheme and suggest some future directions are discussed.

In every scientific context, some procedures are presupposed to be legitimate measurement (or observation) procedures. But to regard some procedure as a legitimate measurement procedure is to presuppose not only that that method is de facto reliable, but also that it would still have been reliable under a broad range of counterfactual suppositions. In this way, and only in this way, scientific methodology as such constrains the way in which we evaluate logically contingent counterfactuals. So, the principle NP (nomological preservation) is presupposed in every possible scientific context if and only if the propositions that are presupposed to be laws of nature in any given scientific context are exactly those regularities that collectively underwrite the reliability of the measurement procedures that are considered legitimate in that context. It follows that the elements of the law‐governed world‐picture are all correct if, and only if, a certain account of lawhood is true.

Variability is inherent in neurons. To account for variability we have to make use of stochastic models. We will take a look at this biologically more rigorous approach by studying the fundamental signal of our brain’s neurons: the action potential and the voltage-gated ion channels mediating it. We will discuss how to model and simulate the action potential stochastically. We review the methods and show that classic stochastic approximation methods fail at capturing important properties of the highly nonlinear action potential mechanism, making the use of accurate models and simulation methods essential for understanding the neural code. We will review what stochastic modelling has taught us about the function, structure, and limits of action potential signalling in neurons, the most surprising insight being that stochastic effects of individual signalling molecules become relevant for whole-cell behaviour. We suggest that most of the experimentally observed neuronal variability can be explained from the bottom-up as generated by molecular sources of thermodynamic noise.

Absolutely no judgment or evaluation should be included when reporting a reliability analysis. Estimates should speak for themselves and it is the task of the readers to judge their values. The discussion should focus on limitations of analysis. In this chapter, example discussions for the Benchmark Assessment of English Language Arts (ELA), the Palmetto Achievement Challenge Test (PACT) of mathematics, and the MSCAN are provided. Several books for future reading are recommended. These include books on classical test theory. Some of the recommended book include Theory of Mental Tests by Gulliksen, Statistical Theories of Mental Test Scores by Lord and Novick, and Reliability for the Social Sciences: Theory and Applications by Traub. The chapter also advises that reading be done on original references so that one can read the original words of the author. Other references are given after this chapter.