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Psychometrics An Introduction Second Editionth Edition by R. Michael Furr, Verne R. B PDF, EPUB archived file. Download link: File name: document_id_237614.zip File type: Self extracted archive File size: 59.6 MB Uploaded: December 13, 2017 Enjoy 🙂 Average Rating of 4.6 from 25 reviews Tags: Psychometrics An Introduction Second Editionth Edition by R. Michael Furr, Verne R.

Psychometrics An introduction Second edition R. Michael Furr, Verne R. Bacharach Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Material Psychometrics An Introduction Second edition R.M. Bacharach Chapter 1: Psychometrics and the Importance of Psychological Measurement Observable behavior and unobservable psychological attributes Psychological tests are used as instruments to measure observable events in the physical world.

Typically, this is some kind of behavior and is conducted for two purposes. First, because of the interest in specific behavior in its own right (like the way facial expression affects the perception of emotions). Second, and much more commonly, behavioral scientists observe human behavior as a way of assessing unobservable psychological attributes such as intelligence, depression, knowledge etc. In testing working memory, three things should be noticed: 1. An inference from an observable behavior to an unobservable psychological attribute is made.

Psychometrics An Introduction Second Editionth Edition by R. Michael Furr, Verne R. B PDF, EPUB archived file. Download link: File name: document_id_237614.zip. File type: Self extracted archive. File size: 59.6 MB. Uploaded: December 13, 2017. Average Rating of 4.6 from 25 reviews. Furr RM, Bacharach VR: Psychometrics: an introduction, Los Angeles, 2014, Sage. Dean E: Survey research in virtual worlds, N.D. Sicignano M: Big data analysis and quality improvement in social services, December 21, 2012.

We assume that the particular behavior that we observed was in fact a measure of working memory. 2. For our interpretation of digit recall scores to be considered valid, the recall task had to be theoretically linked to working memory.

3. When measuring working memory, we assume that working memory is more than a figment of our imagination. Psychological tests: definition and types What is a psychological Test? According to Cronbach (1960), a psychological test “is a systematic procedure for comparing the behavior of two or more people”. The definition includes three important components: 1. Tests involve behavioral samples of some kind 2. The behavioral samples must be collected in some systematic way 3. The purpose of the test is to compare the behaviors of two or more people.

Also the comparison of performance by the same individuals at different points in time can be included in this definition. Types of tests Tests can vary in content, in the regard to the type of response required (open-end test, closed-ended test), in the methods used to administer them (individual or group tests).

Another common distinction concerns the intended purpose of test scores. Psychological tests are often categorized as either criterion referenced or norm referenced. Criterion referenced tests are often seen in setting in which a decision must be made about a person’s skill level. A fixed, predetermined cutoff tests score is established.

Two groups are made: 1. Those whose performance exceeds the performance criterion 2. Those whose performance does not. Norm referenced tests are used to compare a person’s test score with scores from a reference sample, or a normative sample.

A person is compared with other people. Another common distinction is between speeded tests and power tests. Speeded tests are time-limited tests. The number of questions answered are counted. Power tests are not time limited, examinees are expected to answer all the questions (in contrast to speeded tests). Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study MaterialWhen numerals have the property of order, they indicate the rank order of people relative to each other along some dimension. The property of Quantity When numbers have the property of quantity, they provide information about the magnitude of differences between people.

The number 0 0 is a strange number. It has two potential meanings. 1. Zero reflects a state in which an attribute of an object or event has no existence. 2. The meaning of zero is to view it as an arbitrary quantity of an attribute: relative zero. Four scales of measurement The four scale of measurement are nominal scales, ordinal scales, interval scales and ratioscales. Principle Level of measurement (type of scale) example identity nominal type of disorder + order ordinal SES + quantity interval SAT-score + absolute zero ratio weight Chapter 3: individual differences and correlations The nature of variance There are at least two kinds of differences that behavioral scientists attempt to measure: - Interindividual differences: differences that exist between people - Intra-individual differences: differences that emerge in one person over time or under different circumstances. Variability and distributions of scores Central tendency The most basic facet of a distribution of scores is central tendency: What is the “typical” score in the distribution or what is the score that is most representative?

Variability Variance and standard deviation reflect variability as the degree to which scores in a distribution deviate from the mean. Variance=s2= Standard deviation= s = √s2 = Quantifying the Association between distributions Interpreting the association between two variables Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study MaterialThere are two types of information that we would like to know about the association between two variables. First, we would like to know the direction of the association. Second, we would like to know the magnitude of the association. Covariance The covariance represents the degree of association between the variability in the two distributions of scores. Covariance=cxy = Although the covariance does provide information about the magnitude, the covariance does not provide clear information about the magnitude of the association between two variables.

Correlation The correlation coefficient is intended to provide an easily interpretable index of linear association. It ranges from -1 to +1. The great benefit of correlation is that it reflects the magnitude of the association more clearly than covariance. Correlation = rxy= Binary items Some tests are based on dichotomous responses to test items.

Like other tests, these items are scored by summing or averaging responses. In binary scores, the average score will range between 0 and 1, representing the proportion of items answered with 1 (for example 1=yes, 0=no).

Interpreting test scores Z scores (standard scores) An understanding of z scores is important for at least two reasons. First, z scores provide insight into the meaning of test scores as being high, medium and low. A second reason is that z scores can be used to conceptualize and compute important statistical values. Converted standard scores (standardized scores) Converted scores are simply z scores that have been converted into values that people might find easier to understand. T= Z(Snew) + Xnew In this formula, a new standard deviation and a new mean is chosen. Percentile ranks Another common way of presenting and interpreting test scores is through percentile ranks, which indicate the percentage of scores that are below or at a specific score. For example, if we know that a test taker has scored at the 85th percentile, then we know that the person has a relatively high score: he has scored higher than 85% of the other people who have taken the test.

Chapter 4 Test Dimensionality and Factor Analysis What does a test measure? Does it measure for example six separate facets, whit each facet being reflected by a single adjective? Or does it measure a single construct? This illustrates the issue of test dimensionality. In this chapter three main types of tests will be discussed: 1) unidimensional tests, 2), multidimentional tests with correlated dimensions, and 2) multidimensional tests with uncorrelated dimensions. Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study MaterialBy scanning an interitem correlation matrix, one begins to understand a test’s dimensionality (which items are correlated?).

By examining the pattern of correlations, a very basic form of factor analysis has been performed, known as eyeballing. On test with lots of items, eyeballing does not work. EFA is a procedure that simplifies this process. Rather than visually inspecting a matrix, EFA can be used to process large set of correlations. Conducting an interpreting an EFA FA can be conducted by using participants’ raw data.

- Choosing an extraction method In the first step an extraction methods needs to be chosen. This refers to the specific statistical technique to be implemented, and options include principal axis factoring (PAF), maximum likelihood analysis and principal component analysis (PCA). PAF and PCA are most commonly used, PAF is recommended over PCA. - Identifying the number of factors and extracting them In the second step of EFA the number of factors within the set of items are identified and statistical software extracts that number of factors. To address the number of factors issue, test developers refer to statistics called eigenvalues. There are three ways of using eigenvalues: 1. To examine the relative size of eigenvalues themselves.

Examining the eigenvalues, we scan down the descending values in the output column and hope to find a point at which all subsequent differences between values become relatively small. The location of this point has implications for the answer to the “number of dimensions” question. 2. Eigenvalue greater than 1: eigenvalues are used to evaluate the number of dimensions. 3. Using eigenvalues to examine the scree plot. Probably the best way. A scree-plot is a graphical presentation of eigenvalues. We look for a relatively large difference or drop in the plotted value (leveling-off point).

An obvious flattening point suggests that the number of factors is one less than the factor number of the flattening point. - Rotating the factors If evidence suggests that a scale is multidimensional, the we usually “rotate” the factors. The purpose of this step is to clarify the psychological meaning of the factors. There are two general types of rotations: 1. Orthogonal rotation which generates factors that are uncorrelated. 2. Oblique rotation which generates factors that can be either correlated or uncorrelated with each other. - Examining Item-Factor associations EFA presents these associations in terms of “factor loadings”, each item has a loading on each factor. By examining the loadings and identifying the items that are most strongly linked to each factor, we can begin to understand the factors’ psychological meaning.

Hp Deskjet 650c Driver Windows 7. Factor loadings range between -1 and +1. When interpreting factor loadings, two pieces of information are important: 1. The size of the loading indicates the degree of association between an item and a factor.

2. The direction of a loading (positive or negative). - Examining the associations among factors Finally, we should examine the correlations among the factors.

Oblique rotations allow factors to be either correlated or uncorrelated with each other, whereas orthogonal rotations force the factors to Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Materialbe uncorrelated. The results of oblique rotations thus include a correlation for each pair of factors, revealing the higher-order associations. Chapter 5 Reliability Reliability = Observed scores, true scores and measurement errors Reliability depends on two things: 1. The extent to which differences in test scores can be attributed to real inter- or intra- individual differences 2. The extent to which differences in test scores are a function of measurement error Classical test theory (CTT) makes a very important assumption about measurement error. It assumes that error occurs as if it is random, the measurement error is just as likely to inflate as to decrease any particular score. Because error affects scores as if it is random, the inflation and deflation caused by error is independent of the individuals’ true level. There are two important consequences of this assumption about error. 1. Error tends to cancel itself out across respondents: error inflates the scores of some respondents while decreasing others in such a way that the average effect is zero.

2. Error scores are uncorrelated with true scores. Four ways to think of reliability Zie table 5.2 1. Reliability as the ratio of true score variance to observed score variance Probably the most common expression of reliability: Rxx= This value tells us what percentage of the differences that we see among respondents’ observed scores can be attributed to differences among their true trait level. 2. Lack of error variance Error variance represents the degree to which error affects different people in different ways. Rxx= A small degree of error variance indicates that respondents’ scores are being affected only slightly by measurement error. It would indicate that the error affecting one person’s score is not very different from the error affecting another person’s score.

3. The (squared) correlation between observed scores and true scores Correlation coefficient tells us the degree to which differences in one variable are consistent with differences in another variable. Thus, reliability can be seen in terms of the (squared) correlation between observed scores and true scores: Rxx= 4. Lack of (squared) correlation between observed scores and error scores Reliability can also be seen as the degree to which observed scores are uncorrelated with error scores. To the degree that differences in observed test scores reflect differencesi n the effects of error, the test is unreliable. Thus: Rxx= 1 - Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Material 2. The length of the test-retest interval.

Longer intervals are likely to result in more psychological change 3. The period at which the interval occurs. Change is more likely to occur at some periods in an individual’s life than at other points If true scores change during the test-retest interval, then the test-retest correlation reflects two independent factors: 1. The degree to which measurement error affects the test 2. The amount of change in true scores Internal consistency reliability A third approach to estimate reliability is through internal consistency. Respondents only have to complete one test at one point. The fundamental idea behind the internal consistency approach is that the different “parts” of a test can be treated as different forms of a test. There are two fundamental factors that affect the reliability of test scores: 1. The consistency among the parts of a test.

2. Test’s length Split-half estimates of reliability Split-half reliability is based on splitting a test into two separate parts. This procedure can be seen as a three-step procedure: 1. Create two subtest scores 2. Compute the correlation between the two subtests. If the test is reliable, then we should find that respondents’ scores on the “odd” half are consistent with the “even” half. 3. Enter the split-half correlation into a specialized formula to compute the reliability estimate Rxx= The split half correlation cannot be used itself as the estimate of reliability.

Because it is based on a correlation derived from within the test itself, the split-half reliability is called an internal consistency estimate of reliability. “raw” coefficient alpha Item-level approaches take the logic of internal consistency a step further by conceiving of each item as a subtest. It can be seen as a two-step process: 1. Item-level statistics are calculated 2. Item-level information is entered into a specialized equation to estimate the reliability of the complete test. The raw coefficient: K= number of items. “standardized” coefficient alpha The standardized alpha is appropriate if a test score is created by aggregating standardized responses to test items (like z-scores). For the calculation the spearman-brown formula is used: Rxx= The standardized alpha and the raw alpha often produce the same estimates.

Raw alpha for binary items: KR20 The raw alpha equation can be used for binary items. However, there is a more specialized formula: the kuder-richardson 20. Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Material Rxx= Factors affecting the reliability of test scores - Consistency among the parts of a test.

A test with greater internal consistency will have a greater estimated reliability. Average interim correlation can be increased by adjusting the items when necessary (replace questions, make them more clearly). - Length of the test. A long test is more reliable than a short test.

By doubling the length of the test, the original true score variance is quadrupled. However, also the error variance is doubled.

Sample homogeneity and reliability generalization Another factor that has subtle but important effects on the size of reliability coefficients it he heterogeneity of the people taking the test. The greater the variability among people in a group with respect to the psychological attribute being measured, the larger the reliability coefficient. A second and related implication is that sample heterogeneity highlights the utility of reliability generalization studies.

Reliability of difference scores Estimating the reliability of difference scores The reliability of difference scores (Rd) can be estimated on the basis of three sets of information: 1. The estimated reliability of each of the two tests used to compute the difference scores 2. The variability of the tests’ observed scores 3. The correlation between the observed test scores Factors affecting the reliability of difference scores There are two primary factors that determine whether a set of difference scores will have good reliability. 1. The correlation between the tests’ observed scores 2. The reliability of the two tests used to compute the difference scores. Tests that have high reliabilities will produce difference scores that have relatively high reliability. Chapter 7: The importance of reliability Applied behavioral practice: evaluation of an individual’s test score Psychological test scores are often used by psychologists and others to make decisions that have important effects on people’s lives. A test’s reliability has crucial implications for the quality of decisions that are made on the basis of their test scores.

Two important sources of information can help us evaluate an individual’s test score: point estimates and confidence intervals. Point estimates of true scores Two kinds of point estimates can be derived from an individual’s observed test score. One point estimate is based solely on an individual’s observed test score at one point. The second point, sometimes called an adjusted true score estimate, takes measurement error into account. Regression to the mean refers to the likelihood that on a second testing an individual’s score is likely to be closer to the mean than was his or her first score. True score confidence intervals Confidence intervals reflect the accuracy or precision of the point estimate as reflective of an individual’s true score.

95%CI= Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Materialway, one can determine which items contribute well to reliability and which detract from the test’s reliability. Item discrimination is a common concept for evaluating the degree to which an item might affect a test’s internal consistency, i.e. The degree to which an item differentiates people who score high on the total test from those who score low on the total test. This can be done via item-total correlation. Item difficulty (mean) and item variance An item’s mean and variance are potentially important factors affecting its contribution to the psychometric quality of a test. In some cases, the item’s mean tells us about the item’s variability. An item’s mean is sometimes interpreted as the item’s difficulty.

For example, the mean of item 5 is.80, which tells us that 80% of the respondents answered the items correctly. Chapter 8 Validity (conceptual basis) What is validity?

A rather basic definition of validity is “the degree to which a test measures what is it supposed to measure”. A better definition would be: “the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of a test”. This more sophisticated definition has a number of important implications: 1. A measure itself is neither valid nor invalid, rather the issue of validity concerns the interpretation and uses of a measure’s score. 2. Validity is a matter of degree; it is not an “all-or-none” issue. 3. The validity of a test’s interpretation is based on evidence and theory. The importance of validity Measurements are meaningful and useful only if they have acceptable validity for their intended purpose. Without test validity, decisions about societal issues could be misformed, wasteful or even harmful.

Furthermore, without test validity, test-based decisions about individuals could be misinformed or harmful. Validity evidence: test content Validity of test score interpretation hinges on five types of evidence: associations with other variables, consequences of use, test content, response processes and internal structure. Threats to content validity One threat to content validity occurs when a test includes construct-irrelevant content. A test should include no content that is irrelevant to the construct for which the test is to be interpreted.

A second threat is construct underrepresentation. Although a test should not include content that is beyond its core construct, it should include the full range of content that is relevant to the construct, as much as possible. In practice, test developers and test users face a trade-off between the ideal of content validity and the reality of the testing situation. Content validity versus face validity Face validity is closely related to content validity. Face validity is the degree to which a measure appears to be related to a specific construct, in the judgment of nonexperts, such as test takers and representatives of the legal system.

That is, a test has face validity if its content simply looks relevant to the person taking the test. The difference between content validity and face validity is an important one. Malang Dhoom 3 Video Song Full Download on this page.

Content validity is the degree to which the content of a measure truly reflects the full domain of the construct, no more Stuvia.com - The Marketplace to Buy and Sell your Study Material Stuvia.com - The Marketplace to Buy and Sell your Study Material and no less. Face validity is the degree to which nonexperts perceive a test to be relevant for whatever they believe it is being used to measure. Validity evidence: internal structure of the test A test’s internal structure is the way the parts of a test are related to each other. An important validity issue is the match between the actual internal stricter of a test and the structure that the test should be possess.

Factor analysis addresses several fundamental issues related to a test’s internal structure. 1. It helps clarify the number of factors within a set of items 2. It reveals the associations among the factors/dimensions within a multidimensional test.

3. It identifies which items are linked to which factors. Validity evidence: response processes A third type of validity evidence is the match between the psychological processes that respondents actually use when completing a measure and the processes that they should use.

Validity evidence: associations with other variables We need to consider the way in which a construct might be connected to other relevant psychological variables. If respondents’ test scores are to be interpreted as reflecting of the respondents’ standing on a specific psychological construct, then our theoretical understanding of that construct should lead us to expect that test scores will have particular patterns of associations with other variables. It involves the match between a measure’s actual association with other measures and the associations that the test should have. When evaluating the pattern of validity associations, several types of evidence must be considered: - Convergent evidence: the degree to which test scores are correlated with test of related constructs - Discriminant evidence: the degree to which test scores are uncorrelated with test of unrelated c.