Measurement Invariance
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Measurement Invariance
Measurement invariance or measurement equivalence is a statistical property of measurement that indicates that the same construct is being measured across some specified groups. For example, measurement invariance can be used to study whether a given measure is interpreted in a conceptually similar manner by respondents representing different genders or cultural backgrounds. Violations of measurement invariance may preclude meaningful interpretation of measurement data. Tests of measurement invariance are increasingly used in fields such as psychology to supplement evaluation of measurement quality rooted in classical test theory. Measurement invariance is often tested in the framework of multiple-group confirmatory factor analysis (CFA). In the context of structural equation models, including CFA, measurement invariance is often termed ''factorial invariance''. Definition In the common factor model, measurement invariance may be defined as the following equality: :f(\textit \ ...
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Construct (philosophy Of Science)
In philosophy, a construct is an object which is ''ideal'', that is, an object of the mind or of thought, meaning that its existence may be said to depend upon a subject's mind. This contrasts with any possibly ''mind-independent'' objects, the existence of which purportedly does not depend on the existence of a conscious observing subject. Thus, the distinction between these two terms may be compared to that between phenomenon and noumenon in other philosophical contexts and to many of the typical definitions of the terms realism and idealism also. In the correspondence theory of truth, ideas, such as constructs, are to be judged and checked according to how well they correspond with their referents, often conceived as part of a ''mind-independent'' reality. Overview As mind-dependent objects, concepts that are typically viewed as constructs include the abstract objects designated by such symbols as 3 or 4, or words such as liberty or cold as they are seen as a result of in ...
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Cross-cultural Studies
Cross-cultural studies, sometimes called holocultural studies or comparative studies, is a specialization in anthropology and sister sciences such as sociology, psychology, economics, political science that uses field data from many societies through comparative research to examine the scope of human behavior and test hypotheses about human behavior and culture. Cross-cultural studies is the third form of cross-cultural comparisons. The first is comparison of case studies, the second is controlled comparison among variants of a common derivation, and the third is comparison within a sample of cases. Unlike comparative studies, which examines similar characteristics of a few societies, cross-cultural studies uses a sufficiently large sample so that statistical analysis can be made to show relationships or lack of relationships between the traits in question. These studies are surveys of ethnographic data. Cross-cultural studies are applied widely in the social sciences, particular ...
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Differential Item Functioning
Differential item functioning (DIF) is a statistical characteristic of an item that shows the extent to which the item might be measuring different abilities for members of separate subgroups. Average item scores for subgroups having the same overall score on the test are compared to determine whether the item is measuring in essentially the same way for all subgroups. The presence of DIF requires review and judgment, and it does not necessarily indicate the presence of bias. DIF analysis provides an indication of unexpected behavior of items on a test. An item does not display DIF if people from different groups have a different probability to give a certain response; it displays DIF if and only if people from different groups ''with the same underlying true ability'' have a different probability of giving a certain response. Common procedures for assessing DIF are Mantel-Haenszel, item response theory (IRT) based methods, and logistic regression. Description DIF refers to differe ...
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Predictive Power
The concept of predictive power, the power of a scientific theory to generate testable predictions, differs from ''explanatory power'' and ''descriptive power'' (where phenomena that are already known are retrospectively explained or described by a given theory) in that it allows a prospective test of theoretical understanding. Examples A classic example of the predictive power of a theory is the discovery of Neptune as a result of predictions made by mathematicians John Couch Adams and Urbain Le Verrier, based on Newton's theory of gravity. Another example of the predictive power of theories or models is Dmitri Mendeleev's use of his periodic table to predict previously undiscovered chemical elements and their properties. Though largely correct, he misjudged the relative atomic masses of tellurium and iodine. Moreover, Charles Darwin used his knowledge of evolution by natural selection to predict that since a plant (''Angraecum sesquipedale'') with a long spur in its flowers ...
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Explanatory Power
Explanatory power is the ability of a hypothesis or theory to explain the subject matter effectively to which it pertains. Its opposite is ''explanatory impotence''. In the past, various criteria or measures for explanatory power have been proposed. In particular, one hypothesis, theory, or explanation can be said to have more explanatory power than another about the same subject matter * If more facts or observations are accounted for; * If it changes more "surprising facts" into "a matter of course" (following Peirce); * If more details of causal relations are provided, leading to a high accuracy and precision of the description; * If it offers greater predictive power (if it offers more details about what should be expected to be seen and not seen); * If it depends less on authorities and more on observations; * If it makes fewer assumptions; * If it is more falsifiable (more testable by observation or experiment, according to Popper). * If it can be used to compress encod ...
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Nomological
In philosophy, nomology refers to a "science of laws" based on the theory that it is possible to elaborate descriptions dedicated not to particular aspects of reality but inspired by a scientific vision of universal validity expressed by scientific laws. Etymology "Nomology" derives from the Greek , ''law'', and , ''reason''. The term nomology may come from Aristotle. The '-ology' suffix implies 'order', 'word' and 'reason', and is about being subjectively reasonable or 'logical' as in sociology and psychology. The 'nom-' part implies 'rule' and 'law', and is about being objectively lawful or 'nomic' as in economics. Nomology of mind The nomology of mind is the branch of science and philosophy concerned with the laws or principles governing the thought processes and operation of the mind, especially as defined by custom or culture. In the mid-19th century, it was described as one of two grand divisions of philosophy, the other being metaphysics, for example: Nomological ne ...
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Correlation
In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are ''linearly'' related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However ...
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Mean
There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value (magnitude and sign) of a given data set. For a data set, the ''arithmetic mean'', also known as "arithmetic average", is a measure of central tendency of a finite set of numbers: specifically, the sum of the values divided by the number of values. The arithmetic mean of a set of numbers ''x''1, ''x''2, ..., x''n'' is typically denoted using an overhead bar, \bar. If the data set were based on a series of observations obtained by sampling from a statistical population, the arithmetic mean is the ''sample mean'' (\bar) to distinguish it from the mean, or expected value, of the underlying distribution, the ''population mean'' (denoted \mu or \mu_x).Underhill, L.G.; Bradfield d. (1998) ''Introstat'', Juta and Company Ltd.p. 181/ref> Outside probability and statistics, a wide range of other notions of mean are o ...
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Analysis Of Variance
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the ''t''-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means. History While the analysis of variance reached fruition in the 20th century, antecedents extend centuries into the past according to Stigler. These include hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model. Laplace was performing hypothesis testing ...
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Construct Validity
Construct validity concerns how well a set of indicators represent or reflect a concept that is not directly measurable. ''Construct validation'' is the accumulation of evidence to support the interpretation of what a measure reflects.Polit DF Beck CT (2012). Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th ed. Philadelphia, USA: Wolters Klower Health, Lippincott Williams & Wilkins Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence such as content validity and criterion validity. Construct validity is the appropriateness of inferences made on the basis of observations or measurements (often test scores), specifically whether a test can reasonably be considered to reflect the intended construct. Constructs are abstractions that are deliberately created by researchers in order to conceptualize the latent variable, which is correlated with scores on a given measure ...
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Classical Test Theory
Classical test theory (CTT) is a body of related psychometric theory that predicts outcomes of psychological testing such as the difficulty of items or the ability of test-takers. It is a theory of testing based on the idea that a person's observed or obtained score on a test is the sum of a true score (error-free score) and an error score. Generally speaking, the aim of classical test theory is to understand and improve the reliability of psychological tests. ''Classical test theory'' may be regarded as roughly synonymous with ''true score theory''. The term "classical" refers not only to the chronology of these models but also contrasts with the more recent psychometric theories, generally referred to collectively as item response theory, which sometimes bear the appellation "modern" as in "modern latent trait theory". Classical test theory as we know it today was codified by Novick (1966) and described in classic texts such as Lord & Novick (1968) and Allen & Yen (1979/2002). ...
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Christian Welzel
Christian Welzel (born 1964) is a German political scientist at the Leuphana University Lueneburg and director of research at the World Values Survey Association. He is known for the model of cultural dimensions which measures emancipative values and secular values. University education and career Welzel obtained his Magister Artium (M.A.) degree in political science and economic history at the Saarland University in 1991 and continued with a doctorate at the University of Potsdam. For his monograph ''Democratic Elite Change: The Renewal of East German Elites from the Perspective of Democratic Sociology'' he was awarded a PhD with distinction in 1996. He worked as a senior research fellow in the department of “Institutions and Social Change” of the Social Science Research Center Berlin and qualified as professor at the Free University of Berlin. Following the publication of ''Vantage Point ‘Human Development’: On the Roots of Democracy and the Causes of its Diffusion' ...
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