Reliabilitasreliabilitasreliabilitas

Apakah Reliabilitas itu ? Reliabilitas, atau keandalan, adalah konsistensi dari serangkaian pengukuran atau serangkaian alat ukur. Hal tersebut bisa berupa pengukuran dari alat ukur yang sama (tes dengan tes ulang) akan memberikan hasil yang sama, atau untuk pengukuran yang lebih subjektif, apakah dua orang penilai memberikan skor yang mirip (reliabilitas antar penilai). Reliabilitas tidak sama dengan validitas. Artinya pengukuran yang dapat diandalkan akan mengukur secara konsisten, tapi belum tentu mengukur apa yang seharusnya diukur. Dalam penelitian, reliabilitas adalah sejauh mana pengukuran dari suatu tes tetap konsisten setelah dilakukan berulang-ulang terhadap subjek dan dalam kondisi yang sama. Penelitian dianggap dapat diandalkan bila memberikan hasil yang konsisten untuk pengukuran yang sama. Tidak bisa diandalkan bila pengukuran yang berulang itu memberikan hasil yang berbeda-beda. Pengukuran reliabilitas dapat dilakukan dengan menggunakan berbagai alat statistik. http://id.wikipedia.org/wiki/Reliabilitas

In psychometrics reliability is the accuracy of the scores of a measure. Reliability does not imply validity. That is, a reliable measure is measuring something accurately, but not necessarily what it is supposed to be measuring. For example, while there are many reliable tests, not all of them would validly predict job performance. Reliability may be estimated through a variety of methods that fall into two types: Singleadministration and multiple-administration. Multiple-administration methods require that two assessments are administered. In the test-retest method, reliability is estimated as the Pearson product-moment correlation coefficient between two administrations of the same measure. In the alternate forms method, reliability is estimated by the Pearson product-moment correlation coefficient of two different forms of a measure, usually administered together. Single-administration methods include split-half and internal consistency. The split-half method treats the two halves of a measure as alternate forms. This "halves reliability" estimate is then stepped up to the full test length using the Spearman-Brown prediction formula. The most common internal consistency measure is Cronbach's alpha, which is usually interpreted as the mean of all possible split-half coefficients. Each of these estimation methods is sensitive to different sources of error and so might not be expected to be equal. Also, reliability is a property of the scores of a measure rather than the measure itself and are thus said to be sample dependent. Reliability estimates from one sample might differ from those of a second sample (beyond what might be expected due to sampling variations) if the second sample is drawn from a different population because the true reliability is different in this second population. (This is true of measures of all types-yardsticks might measure houses well yet have poor reliability when used to measure the lengths of insects.) Reliability may be improved by clarity of expression (for written assessments), lengthening the measure, and other informal means. However, formal psychometric analysis is considered the most effective. Such analysis generally involves computation of item statistics such as the

item-total correlation (the correlation between the item score and sum of the item scores of the entire test). These measures are inherently circular but in practice they work well if the test has been constructed carefully so that it's initial draft contains sufficient reliability. http://knowledgerush.com/kr/encyclopedia/Reliability_(psychometric)/

Siapakah Tokoh yang Mengemukakan Teori Reliabilitas ? Cronbach's α (alpha) is a quantity defined in multivariate statistics. It has an important use as measure of the reliability of a psychometric instrument, since it assesses the extent to which a set of test items can be treated as measuring a single latent variable . It was first named as such in the article: Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika 1951;16:297-333, although an earlier version is the Kuder-Richardson Formula 20 (often shortened to KR-20), which is the equivalent for dichotomous items, and Louis Guttman (1945) developed the same quantity under the name lambda-2. Cronbach's α is defined as the mean correlation between each of a set of items, all of which have been measured for every member of a sample, with the mean of all the other items. It is related to the outcome of an analysis of variance of the item data into variance due to the individuals in the sample and variance due to the items. The higher the proportion of variance due to individuals, the higher Cronbach's α. α can take values between minus infinity and 1 (although only positive values make sense). As a rule of thumb, a proposed psychometric instrument should only be used if an α value of 0.8 or higher is obtained on a substantial sample. However the standard of reliability required varies between fields of psychology: cognitive tests (tests of intelligence or achievement) tend to be more reliable than tests of attitudes or personality. There is also variation within fields: it is easier to construct a reliable test of a specific attitude than of a general one, for example. Although this description of the use of α is given in terms of psychology, the statistic can be used in any discipline. http://www.economicexpert.com/a/Cronbach:s:alpha:kb.htm

The Spearman-Brown prediction formula (also known as the Spearman-Brown prophecy formula) is a formula relating psychometric reliability to test length:

where is the predicted reliability; N is the number of "tests" combined (see below); and is the reliability of the current "test". The formula predicts the reliability of a new test composed by

replicating the current test N times (or, equivalently, adding N parallel forms of the current exam to the current exam). Thus N=2 implies doubling the exam length by adding items with the same properties as those in the current exam. Values of N less than one may be used to predict the effect of shortening a test. The formula can also be rearranged to predict the number of replications required to achieve a degree of reliability:

This formula is commonly used by psychometricians to predict the reliability of a test after changing the test length. This relationship is particularly vital to the split-half and related methods of estimating reliability. The formula is also helpful in understanding the nonlinear relationship between test reliability and test length. If the longer/shorter test is not parallel to the current test, then the prediction will not be strictly accurate. For example, if a highly reliable test was lengthened by adding many poor items then the achieved reliability will probably be much lower than that predicted by this formula. Item response theory item information provides a much more precise means of predicting changes in the quality of measurement by adding or removing individual items. http://www.economicexpert.com/a/Spearman:Brown:prediction:formula.htm