Sabado, Setyembre 27, 2014

Original Poem/Classic Poem/Poem Dedicated for People who have Disabilities



If We Were Them
Rise and walk, crawl or creep, or we can run,
jump and twerk, and dance or play as long as we can.
That’s life we know, we move under the sun,
but think how’s life to a man all left being numb.

Hear the birds sing and all the sounds on earth.
The songs we like and all the voices we can hear.
We grow with music to our death from birth,
but think how’s life to a man who can never hear.

We speak words so we can be understood;
we sing, we talk, we scream and shout, we say, we could.
That’s life we know, it makes us all feel good,
but how is life if we got mouth only for chewing food.

Look around and paint all colors you can find;
the scenes, the shapes, all places, our looks all defined.
 That is our life; we create pictures in our mind,
but how’s life to a man who sees none of all kind.

Walk with a lame and feel how hard it is for him.
Talk to a deaf; hear how he struggles to listen.
Chat with a mute; tell how he tries to say your name.
Stare at a blind, ask him how bright differs from dim.

We say we hate life and thought we’re in vain.
We’re lucky we live a life of joy and not pain.
Think how’s life for them who thought to stop dreamin’
just think how would our lives be if we were them.

Lunes, Mayo 12, 2014

Semantic Differentials

Visayas State University
College of Education
Department of Teacher Education
VisCA, Baybay City, Leyte










“Semantic Differential”
(A Written Report in PrEd 161 – Assessment of Learning 2)










Prepared by:
Ahldeter S. Mantua
Mary Luz Zuela







Submitted to:
Helmar G. Ycong
Instructor






Summer 2014
  I.            OBJECTIVES

         At the end of the lesson, the students are expected to:
·         Define semantic differentials.
·         Determine the theoretical background of semantic differentials.
·         Determine the construction methods and uses of SD
·         Identify the descriptive measures and procedures involved in SD.
·         Identify the advantages and disadvantages of SD.
·         Determine SD’s statistical properties.

II.            INTRODUCTION

   The semantic differential measurement technique is a form of rating scale that is designed to identify the connotative meaning of objects, words, and concepts. The technique was created in the 1950s by psychologist Charles E. Osgood. The semantic differential technique measures an individual's unique, perceived meaning of an object, a word, or an individual. The semantic differential can be thought of as a sequence of attitude scales. The scales are designed such that the left side is generally positive and the right is generally negative. This allows the semantic differential to measure intensity and directionality. The rating scale consists of a list of bipolar responses.

III.            BODY
            Semantic differential (SD) is a type of a rating scale designed to measure the connotative meaning of objects, events, and concepts. The connotations are used to derive the attitude towards the given object, event or concept.Osgood's semantic differential was an application of his more general attempt to measure the semantics or meaning of words, particularly adjectives, and their referent concepts. The respondent is asked to choose where his or her position lies, on a scale between two bipolar adjectives (for example: "Adequate-Inadequate", "Good-Evil" or "Valuable-Worthless"). An example of an SD scale is:
Usually, the position marked 0 is labeled "neutral," the 1 positions are labeled "slightly," the 2 positions "quite," and the 3 positions "extremely." A scale like this one measures directionality of a reaction (e.g., good versus bad) and also intensity (slight through extreme). Typically, a person is presented with some concept of interest, e.g., Red China, and asked to rate it on a number of such scales. Ratings are combined in various ways to describe and analyze the person's feelings.
THEORETICAL  BACKGROUND

Nominalists and Realists

Theoretical underpinnings of Charles E. Osgood's semantic differential have roots in the medieval controversy between the nominalists and realists. Nominalists asserted that only real things are entities and those abstractions from these entities, called universals, are mere words. The realists held that universals have an independent objective existence either in a realm of their own or in the mind of God. Osgood’s theoretical work also bears affinity to linguistics and general semantics and relates to Korzybski'sstructural differential.

Use of Adjectives

The development of this instrument provides an interesting insight into the border area between linguistics and psychology. People have been describing each other since they developed the ability to speak. Most adjectives can also be used as personality descriptors. The occurrence of thousands of adjectives in English is an attestation of the subtleties in descriptions of persons and their behavior available to speakers of English. Roget's Thesaurus is an early attempt to classify most adjectives into categories and was used within this context to reduce the number of adjectives to manageable subsets, suitable for factor analysis.

Evaluation, Potency, and Activity (EPA)

Osgood and his colleagues performed a factor analysis of large collections of semantic differential scales and found three recurring attitudes that people use to evaluate words and phrases: evaluation, potency, and activity. Evaluation loads highest on the adjective pair 'good-bad'. The 'strong-weak' adjective pair defines the potency factor. Adjective pair 'active-passive' defines the activity factor. These three dimensions of affective meaning were found to be cross-cultural universals in a study of dozens of cultures.

The Epa Structure
One of the distinctive features of the SD is its reduction of ratings to three basic dimensions of variation. A number of early studies were conducted to determine the dimensions of bipolar adjective ratings. Of special importance was the thesaurus study in which 76 adjective pairs were chosen from Roget's Thesaurus to represent a great variety of semantic contrasts and the corresponding bipolar scales were used by one hundred college students to rate 20 different concepts. Correlations between the ratings on different scales were calculated and factored. The EPA structure was clearly evident in the results of this and other early analyses; in the thesaurus study the EPA dimensions accounted for more than two-thirds of the common variance. Some additional dimensions were found in the early studies, and several scales that made distinctions too narrowly descriptive or too highly abstract were found to be unrelated to any of the major dimensions. Yet, for the most part, early work with the SD revealed that ratings on most scales are highly predictable in the three EPA dimensions alone.
The EPA structure holds up with a wide variety of subjects, concepts, and scales. Bopp (reported in Osgood, et al., pp. 223-226) had 40 schizophrenics rate 32 words on a 13 scale form; the usual EPA structure was recognizable. Wright (1958) had 40 concepts rated on a 30 scale SD by a survey sample of 2,000 men and women distributed over the spectrum of socioeconomic status. In this study each concept was rated by a different sample of 50 persons so the mean ratings for different concepts were entirely independent. Wright found four factors in his data, the first three of which clearly were EPA. Heise (1965) had 1,000 concepts rated on eight scales by Navy enlistees; factor analyses of the data based on mean ratings for the 1,000 different words yielded the usual EPA structure. DiVesta (1966) had 100 concepts rated on 27 scales by subjects in grades two through seven (20 subjects for each concept). The usual EPA structure emerged, though there was some tendency for Potency and Activity to merge into a single Dynamism dimension up until the fifth grade. DiVesta also reports another study in which grade school children used 21 scales to rate 100 different concepts (this time with 100 subjects rating each concept) and, combining the data for all grades, the usual EPA structure was found.

Characterization of the EPA Dimensions
Considering the generality of the EPA dimensions and their importance in research using the SD, it is worth considering in more detail the distinctions that are involved. In the following paragraphs the EPA dimensions are characterized in two ways. First, some of the typical adjective contrasts that define each dimension are presented. Second, a number of concepts which typically are rated near the extremes of each dimension are given.
Evaluation is associated with the adjective contrasts: nice-awful, good-bad, sweet-sour, and helpful-unhelpful. Some concepts which lie on the positive (good) side of this dimension are: DOCTOR, FAMILY, GOD, CHURCH, HAPPY, PEACE, SUCCESS, TRUTH, BEAUTY, and MUSIC. Some concepts which lie toward the negative (bad) pole are: ABORTION, DEVIL, DISCORDANT, DIVORCE, FRAUD, HATE, DISEASE, SIN, WAR, ENEMY, and FAILURE.
Some scales which define the Potency dimension are big-little, powerful-powerless, strong-weak, and deep-shallow. Concepts which lie toward the positive (powerful) pole are: WAR, ARMY, BRAVE, COP, MOUNTAIN, ENGINE, BUILDING, DUTY, LAW, STEEL, POWER, and SCIENCE. Concepts which lie toward the negative (powerless) pole are: GIRL, BABY, WIFE, FEATHER, KITTEN, KISS, LOVE, and ART.
Activity scales are fast-slow, alive-dead, noisy-quiet, and young-old. Some concepts high in Activity are: DANGER, ANGER, ATTACK, CITY, ENGINE, FIRE, SWORD, TORNADO, WAR, WIN, CHILD, and PARTY. Among concepts which lie toward the negative pole on the Activity dimension are: CALM, SNAIL, DEATH, EGG, REST, STONE, and SLEEP.

SD Space
Sometimes it is convenient to think of the EPA dimensions as forming a three-dimensional space. The SD, or affective, space is illustrated in Figure 1;







Figure 1.The SD Space.

the origin or center of this space represents neutralityon all three dimensions. Treating EPA measurements of a stimulus as coordinates allows the stimulus to be positioned as a point in the space, and this point graphically represents the affective response to the stimulus.

Construction and Use of  SD

The following sections discuss how one makes and uses an SD for research purposes, and what kinds of information are provided for analyses. This discussion serves to introduce vocabulary which will be helpful later on.
The primary question in constructing an SD is what scales should be used. Two basic criteria enter into scale selection; relevance and factorial composition.

Scale Relevance

Subjects find it easier to use scales which relate meaningfully to the concepts being judged and which make distinctions that are familiar. For example, in rating persons, sweet-sour is less relevant, and thus harder to use, than helpful-unhelpful; among laymen, talkative-quiet would be a better scale than manic-depressive. Furthermore (and more important), relevant scales provide more sensitive measurements. More variance is obtained in using relevant scales and the variance of ratings involves less random error.
There are two approaches to identifying scales which are relevant for a given class of concepts and a given sample of persons. On the one hand, subjects can be presented with a set of scales and asked to rank them in terms of their meaningfulness in thinking about x, where x is a class of concepts to be rated like People, Newspapers, Organizations, etc.. One then would use the scales ranking highest in meaningfulness for a given population of raters.
A second, more meticulous approach would be to present pairs or triads of concepts from the stimulus concept domain and ask subjects how these concepts differ. One would make up bipolar scales from the distinctions respondents make, omitting any purely denotative distinctions {e.g., blond versus brunette). For example, if subjects frequently drew the distinction of crudeness, an appropriate scale might be crude-gracious. This approach, developed for the study of individuals by Kelly (1955), has been applied successfully in SD studies (e.g., Triandis).

Factorial Composition

The basic goal in an SD study is to get measurements on the EPA dimensions, and since factor analyses show these dimensions to be independent, one seeks measurements that are independent. This means that appropriate scales will measure the dimensions (i.e., scales that have high factor loadings on the EPA dimensions) and will give relatively pure measures of the dimensions (i.e., each scale has a high loading on just one dimension). The only objective way to select factorially pure scales is on the basis of actual factor analyses. Researchers experienced with the SD are aware that intuition is an unreliable guide in selecting factorially purescales. One can conduct ad hoc factor analyses to learn the factorial composition of new scales, but this is an expensive procedure since studies based on less than 30 concepts and hundreds of subjects are likely to be misleading. The most common procedure is to select scales on the basis of published factor analyses and following are some available reports which indicate the factorial composition of SD scales. The thesaurus study has been a standard source of factor analytic information on SD scales. Because of the large number of scales considered (76), this is an important source, but the factor loadings should be treated only as rough indicators because of the unusual method of factoring and because only 20 concepts were rated in this study. Wright presents the factorial structure for 30 scales based on data from a survey sample of 2,000 adults rating 40 concepts. DiVesta gives the factor loadings of 27 scales used to rate 100 concepts by a large sample of children. Jakobovits gives the highest loading EPA scales for 15 languages (including English) as derived from the pan-cultural factor analyses.
The published factor analytic studies provide a large fund of scales to draw on and usually one can obtain a subset of scales which are relevant to the concept domain of interest. It should be noted, however, that another problem arises in selecting scales from previous studies—the matter of semantic stability. When applied to a special class of concepts, the words in a scale may take on special meanings and thus the scale is literally a different one than previously studied. For example, the words HOT and COLD are used connotatively in rating many concepts (like PEOPLE) but may be used denotatively in rating physical objects. Since the scale takes on different meanings with different concepts, its factorial composition may be different for the special class of objects. The problem of semantic stability is (along with the problem of relevance) the primary impetus for carrying out special factor analyses for each new content area.

Number of Scales

Assuming that one has a set of relevant scales, each of which loads on one and only one of the EPA factors, the next question is how many scales should be included in the final instrument. More than one scale for each dimension is desirable since this improves the reliability of factor scores. On the other hand, reliability characteristics of SD scales are such that it would rarely be useful to include more than ten scales to measure a dimension, and generally speaking, four scales per dimension can give adequate sensitivity for most purposes.
Contrary to the practice in many published studies, the number of Evaluation scales should not be more than the number of Potency and Activity scales. Evaluation scales always are found to be more reliable than Potency or Activity scales and thus fewer, not more, are needed for a given level of precision.

Equivalent Forms

In research it is often necessary or desirable to do repeated measurement. This introduces the question of equivalent forms. There is evidence that subjects may recall the SD rating they have made previously when the time periods between repeated measurements are short. Consequently, such repeated measurements using the same form may not be independent. An example of how this could confound research is given by Coyne and Holzman (1966) who had subjects give SD ratings for their voice at points before and after listening to themselves on a tape recorder. No significant differences were found when the same SD form was used in all ratings, but highly significant changes appeared when subjects used alternate forms of the SD for the different points of time. This experiment suggests that equivalent forms of the SD are necessary in experiments dealing with short range changes in attitudinal reaction.
The primary problem in the development and use of equivalent forms is the large fund of factor analyzed scales that is required; making up two equivalent forms calls for twice as many scales. Given a fund of scales to draw on, one should try to match factor loadings of scales in different forms. Then an experimental design should be used such that some subjects should use Form A at time 1 and Form B at time 2 while other subjects use Form B at time 1 and Form A at time 2.

Format of SD Test Booklets

There are three possible ways of graphically setting up scales and the concepts to be rated:
(1) Concepts can be presented one at a time, with each concept followed by all of the scales on which it is to be rated; typically, the concept is printed at the top of a page and the scales are arrayed below, one after another, and centered on the page.
(2) A concept and one of the scales on which it is to be rated can be presented as a single item with the various concept-scale combinations arrayed randomly one after another. For example, item 1 might be NEGRO followed by the good-bad scale, item 2 RUSSIAN followed by the passive-active scale, item 3 JEW followed by helpful-unhelpful, etc. (It is immaterial whether the stimulus word is placed to the left or right of the scale.)
(3) A single scale can be presented along with all of the concepts which are to be rated on it; for example, the good-bad scale could be presented at the top of the page and concepts listed down along the side, each followed by scale marking positions.
Studies show that measurements differ very little in going from one format to another, although format 3 is least desirable since there is some slight tendency for ratings of one concept to affect ratings on another concept. From the standpoint of data handling, format 1 is preferable since it groups the data for a single concept, facilitating keypunching and statistical analyses.
When format 1 is used, the order of concepts in the test booklet is immaterial since anchoring or order effects are not evident using this format. Sommer (1965) made a determined effort to produce anchor effects and found none: for example, POLITICIAN was rated the same whether preceded by JANITOR, GARBAGE COLLECTOR, FARMER or whether preceded by STATESMAN, SCHOLAR and SCIENTIST.
To disguise the nature of an SD test and to prevent subjects from developing response sets which could reduce sensitivity of measurements, it is customary to mix the scales as much as possible. This means alternating Evaluation, Potency and Activity scales rather than presenting them in blocks and alternating directionality so that the scales' good poles, strong poles, or active poles are not always on the same side.

Adverbial quantifiers
To facilitate the rating of intensity, SD scale positions usually are labeled with adverbs like "extremely," "quite," and "slightly." The study by Wells and Smith inquired into whether the adverbs serve any useful function. SD scales with and without adverbial quantifiers were employed with a survey sample of 400 housewives. It was found that the amount of differentiation in SD ratings was substantially greater when adverbial labels were used: no labels led to many more ratings at the end-points of the scales. Furthermore, interviewers reported that the labeled scales were better understood by the respondents and led to greater cooperation in the rating task. Hence, use of adverbial quantifiers is justified.
The metric characteristics of adverbial quantifiers have been investigated in a number of. The results indicate that adverbs "extremely," "quite," and "slightly" do define rating positions which are about equidistantly spaced. The results from these studies also suggest some other adverbs which might be used in some SD studies. For example, the adverbs of frequency—"seldom," "often," "always"—might be meaningful in SD studies of roles (i.e., is a LAWYER sometimes powerful, usually powerful, always powerful; is a MOTHER sometimes nice, usually nice, always nice). However, the relationship between such frequency ratings and intensity ratings using the customary adverbs is not known.

Administration of an SD

SD’s are easily administered to groups and, when possible, this is certainly the most efficient way to obtain SD data. However, an SD also can be administered successfully on an individual basis by survey interviewers.
Instructions should routinely contain a statement that the purpose of the SD is to find out how people feel about things and so the respondent should rate the way he feels. He should use his first impressions and not try to figure out the "right answer" or the answer that makes most sense. Instructions also should contain an example in which the concept presented would elicit a unanimous response from the subjects, for example, TORNADO. The concept is rated by the test administrator, who explains while making the ratings what the scale positions mean. It has been suggested that subjects should be urged to work quickly; however, Miron found that subjects could be urged to work slowly and thoughtfully and the same results were obtained, mainly because after the first few ratings, subjects worked quickly, regardless of what they were instructed to do.
For many subject populations, one can turn to the literature to check the experiences and procedures of others who have worked with similar groups, for example: college students—Osgood, et al.; children—DiVesta (1966), and Kagan, Hosken and Watson (1961); survey respondents—Wright, and Wells and Smith; factory workers—Triandis; juvenile delinquents—Gordon, et al. (1963); illiterates—Suci (1960).

Test length
Osgood, et al. (p. 8O) suggested that a subject should be allowed about one hour to make 400 SD judgments (for example, to rate 40 concepts on ten scales). Most college students work faster than this and the allowance is generous for even the stragglers in a college student population. On the other hand, this timing estimate is a convenient round figure, and it is perhaps minimal for subjects not accustomed to taking tests. In any case, the patience and endurance of unpaid subjects can rarely be strained beyond 400 judgments, and for non-college subjects (such as survey respondents), the maximum number of ratings undoubtedly is far less—probably more like 50 judgments.

Descriptive Measures and Procedures

A typical SD study dealing with a number of concepts, using several scales for each EPA dimension, and employing a sample of respondents, results in thousands of ratings. Various statistics and procedures are available to compress this data to a comprehensible set of measurements.

Factor Scores

The first step in data reduction is to combine ratings on the separate scales into factor scores. This involves first assigning numerical values to the scale positions; for example, -3, -2, -1,0,1,2,3, going from one end of the scale to the other. (To simplify calculations, numerical values should be adjusted for the directionality of scales; for example, the positions numbered 3 through -3 for the scale nice-awful, and -3 through 3 for the scale bad-good.) The responses that were obtained are then coded and a subject's ratings on a concept averaged over all the scales representing a single factor. The product is a single number representing one subject's reaction to one concept on one of the SD dimensions.

Scale Weights. Assuming that the factor loadings of the scales for a given dimension are all high and comparable in size, that all the scales load mainly on the one dimension, and that all the scales are of approximate equal relevance so that the rating variances
are approximately equal, then it is reasonable to weight the scales equally in calculating the factor scores (i.e., find the simple mean of the ratings). Only if these assumptions are seriously violated, is a more complicated procedure of differential weighting desirable; this could involve weighting each scale by the squared factor loading or the use of multiple regression formulas. Textbooks on factor analysis provide information on the more complicated procedures.

Group Means. A frequent second step in data reduction is finding the group means for the factor scores corresponding to different concepts. This simply involves averaging the factor scores over the subjects in the sample. The group means can be viewed as estimates of true factor scores for the particular concept in the particular group or culture—they are the points around which individuals vary. Group means computed from the SD tend to be extremely stable.

Polarization

The factor scores for a concept constitute a complete description of an affective reaction in terms of the EPA dimensions. For some purposes one might not want such detailed information but simply a general measure of the intensity of the affective response independent of its character. This kind of measure, the emotionality of the concept, is given by the polarization measure—the distance between the neutral point or origin of the SD three-dimensional space and the particular concept under consideration. If the neutral point of the scale was assigned a value of zero in the coding process, the factor scores also have their neutral point at zero and polarization is calculated simply by squaring the factor scores, adding, and taking the square root of the sum. That is:


where e, p, and a, are factor-score measurements of a given concept on the three dimensions.

Profile Analyses

The majority of SD studies involve some hypothesis about differences in affective reaction. For example, one might be interested in reactions to NEGROES versus JEWS; the difference in reaction to NEGROES before and after seeing The Birth of a Nation, or the difference in reactions to NEGROES among Southerners and Northerners. Various approaches for analyzing differences in affective response have been developed.
Dimensions treated separately. One approach examines the differences on each EPA dimension separately. That is, one would compare the means for concept a versus concept b, for time 1 versus time 2, or for group x versus group y on each of the three dimensions separately. This approach provides the most detailed results, the statistical procedures for comparing means are well-studied and relatively non-problematic, and it is definitely the preferred procedure in most SD studies. In any case, it should accompany other types of profile analysis.
D scores.There are instances in which one would like to have a measure of the combined differences on all three EPA dimensions—a summary measure of the total difference in affective reactions. D scores have come to serve this purpose in SD research. These represent the distance between two sets of SD measurements when both are plotted as points in the three-dimensional SD space. The formula for calculating D scores is as follows: let el, pl, al, be the factor score for concept 1 (or time 1 or group 1) ; e2, p2, a2, the measurements for concept 2 (or time 2 or group 2). Then
D =

The meaning of D scores can be illustrated by an example. The average EPA factor scores for the concepts HOME, OFFICE, and WORK were drawn from Heise (1965) and entered into the formula for D. It was found that the distance between HOME and WORK is about 3.8 units while the distance between OFFICE and WORK is .8 units. Thus, the affective reaction to WORK is more similar to that for OFFICE than to that for HOME.
Considerations in using D.The reliability of D scores based on group means (where N = 30) is adequate; the correlations between test and retest or between alternate groups are above .90 (Norman, 1959). The random distribution of D under various conditions of rating errors has been studied by Cozens and Jacobs (1961).
Despite the simplicity and the reliability of the measure, D scores should be employed conservatively. D scores completely hide the character of a difference, and a large D could be due to a big difference on one dimension or small differences on all three dimensions. When only the D scores are presented, a reader has no way of determining which is the case.
Beyond that, however, they can be misinterpreted and lead to artifactual findings. For example, at one time a popular project was to show that the difference (D) between evaluative ratings of Ideal Self and Actual Self is greater for neurotics than for normals. To simplify matters, suppose that all persons see their ideal selves as quite good (an assumption which is realistic). Now suppose that neurotics have low evaluation of their actual selves, rating their actual selves as slightly bad, whereas normals rate their actual selves as slightly good. Since both groups have the same rating of the ideal self, it inevitably follows that the neurotics are further from their ideal selves. It could be a serious error to say that "what's wrong" with neurotics is the discrepancy between their actual and ideal selves, since perhaps what is really wrong with them is merely their low evaluation of actual self, which produces the discrepancy as an artifact or inevitable side effect. In fact, Bass and Fiedler (1959) did find that D scores added very little to the basic factor scores in predicting maladjustment. Pitfalls involved in D scores are discussed in greater detail in a series of articles by Cronbach.

Reliability of SD Measurements

A study of the absolute deviations between ratings of a concept in test and retest (with retest up to three months later) was reported in Osgood, et al. (p. 127). For evaluation scales it was found that the average difference between ratings on the test and retest was somewhat more than one-half scale units. For Potency and Activity scales the average difference between the test and retest ranged from .7 to 1.0 scale units. The authors concluded from their data that a difference of 3 scale units or more between two ratings on the same scale could be considered statistically significant at the .05 level in a two-tailed test.
DiVesta and Dick (1966) studied the test-retest reliabilities of SD ratings made by grade school children. In their study each subject rated a different concept on a series of scales, and reliabilities were determined by correlating the ratings made on a first test with ratings made one month later on a second test. The correlations for different scales ranged from .27 to .56. DiVesta and Dick found that reliabilities are somewhat higher in the higher grades and also that Evaluation scales tend to be somewhat more reliable at all grade levels.
A reliability study by Norman (1959) gives information on how much shift occurs in ratings, relative to what might be expected if the ratings were purely random. Norman had 30 subjects rate 20 concepts on 20 scales in a test and retest spaced four weeks apart. On the average he found that the amount of shift in ratings was about 50 per cent of what would be expected if the ratings were completely random. More specifically, his results showed that 40 per cent of the scale ratings do not shift at all from test to retest, 35 per cent of the ratings shift by one scale unit, and 25 per cent of the ratings shift two or more scale units. Norman found that ratings are more stable for some concepts than for others, and this seems to be related to the number of meanings for a concept. This may also be a function of how extremely the word is rated. Other studies suggest that concepts whose true values are neutral are rated with less reliability (Peabody, 1962; Luria, 1959). Norman also found that some subjects were more stable than others in making their ratings; in particular, there is a tendency for those who use the end-points of scales more often to have lower test-retest stability. Finally, he found that certain scales are associated with greater stability; in particular, Evaluation scales evoke fewer shifts.
The general impression produced by these test-retest reliability studies is that a person's rating of a single concept on a single scale constitutes a measurement, albeit not an extremely delicate one. Such results may be somewhat misleading, however, because test-retest statistics measure stability as well as reliability. Consequently, low correlations may be due to actual changes in subjects' reactions as well as random errors. In any case, single ratings rarely are used in SD research; instead, factor scores, which should be more reliable because they are the averages of ratings on several scales, are more commonly employed.
Factor score reliability. A study is reported in Osgood in which several controversial topics were rated on six evaluation scales, and factor scores, representing each subject's evaluative reaction to a given topic, were obtained by summing the ratings on the six scales. The correlations between test and retest factor scores ranged from 0.87 to 0.97 with a mean of .91. DiVesta and Dick in their study of SD reliability among children made up factor scores by averaging ratings on two scales for a given dimension and correlating the measurements from the first test with those from the second test given one month later. For children in the fourth grade or higher the correlations ranged between .5 and .8 and were highest for Evaluation factor scores; for students in the third grade or lower, test-retest correlations ranged between 0.4 and 0.5. DiVesta and Dick found that test-retest correlations were somewhat higher when the retest followed the first test immediately. In this case the r's ranged between 0.6 and 0.8. Norman examined the effect of making up factor scores from various numbers of scales. His results indicate that factor scores are more reliable than single ratings and that most of the gain in precision is accomplished by averaging just three or four scales; going up to an eight-scale factor score seems to add very little additional stability when looking at data from a test and retest spaced one month apart.
The various studies indicate that there is indeed a significant gain in test-retest correlations when factor scores are used rather than individual scale ratings. Furthermore, it appears that most of the possible improvement can be obtained using relatively few scales in making up the factor scores.
Group means. Many SD studies do not focus on an individual's rating of a concept but on a group mean. That is, interest is in the average score in a certain group rather than the score for anyone person. In such case, there is averaging both across scales (factor scores) and across persons, and reliabilities should be even higher.
DiVesta and Dick calculated factor score means for groups of three to five children. The immediate test-retest correlations ranged from .73 to .94, figures that are significantly higher than the correlations based on individual subjects. Norman calculated scale means for 20 concepts using groups of 30 raters. The test-retest correlations between means was .96, and the correlations between means produced by two different samples of student respondents was .94. Miron averaged the factor scores for 20 concepts across 112 subjects and obtained test-retest correlations of .98 or more.
These studies reveal that group means on the EPA dimensions are highly reliable and stable even when the samples of subjects involved in calculating the means are as small as 30.
Advantage
SD is designed to measure both the direction and the intensity of attitudes simultaneously. It enables the researcher to avoid the task of creating bipolar adjective pairs. Scale may also permit finer discrimination in measuring attitudes.

Disadvantage
Semantic differential suffers from a lack of standardization. The numbers of divisions on the scale are a problem. If too few divisions are used, the scale is crude and lacks meaning; if too many are used, the scale goes beyond the ability of most people to discriminate

Statistical Properties

Five items, or 5 bipolar pairs of adjectives, have been proven to yield reliable findings, which highly correlate with alternative Likert numerical measures of the same attitude.
            One problem with this scale is that its psychometric properties and level of measurement are disputed.The most general approach is to treat it as an ordinal scale, but it can be argued that the neutral response (i.e. the middle alternative on the scale) serves as an arbitrary zero point, and that the intervals between the scale values can be treated as equal, making it an interval scale.

 IV.            SUMMARY and CONCLUSION

The SD is a general procedure for assessing affective responses. The technique has three features that distinguish it as an instrument for social psychological research. First, SDs are easy to set up, administer, and code. This, in conjunction with the demonstrated reliability and validity of the procedure, gives it favorable cost-effectiveness. Second, the EPA structure, which has an unprecedented amount of cross-cultural validation, is interesting theoretically, and measurements on all three dimensions yield a wealth of information about affective responses to a stimulus. The information that the three independent scores give about the character of responses inevitably is lost with alternative measures depending on unidimensionality. Third, since the form of an SD is basically the same whatever the stimulus, research using the SD (and methodological research about the SD) can cumulate.
The SD has been applied frequently as a technique for attitude measurement. Its usefulness in this respect is indicated by the wide variety of meaningful results that have been obtained. Further, SD measurements have been found to correlate highly with measurements on traditional attitude scales. There are, however, a number of questions in the use of SDs for attitude measurement.
When subjects are highly invested in a topic and want to give socially desirable answers, it may be advisable to use an instrument that is less direct than the SD. Social desirability ratings of SD scales correlate very highly with the Evaluation factor loadings of the scales. Thus, if subjects choose to distort their responses toward social desirability, Evaluation scores would be biased upward. If one does use the SD with especially sensitive topics (or respondents) it is worth taking some precaution to guard against social desirability effects (e.g., giving anonymity to respondents). Note, however, that Potency and Activity measurements should be free of this problem since the typical scales for measuring these dimensions are essentially free of social desirability contamination.
Thus far almost all applications of the SD to attitude measurement have relied only on Evaluation measurements. This appears to be an unfortunate tradition. A subjective examination of items in traditional attitude scales suggests that Potency and Activity do get involved in traditional attitude measurements. Furthermore, the multiple correlations of EPA ratings with traditional scales often are much higher than the correlations of Evaluation ratings only with the scales. In the future it would be advisable to obtain ratings on all three dimensions when one is interested in attitudes. Almost certainly the full EPA information will increase the power of analyses.
Perhaps the most important general contribution of the SD is the provision of a single attitude space for all stimuli. This permits analyses, comparisons, and insights that. were virtually impossible with traditional instruments.

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