Discriminating power of descriptive items

 

Facility Value

 

TYPE AND USES OF ATTITUDE SCALES

TYPE AND USES OF ATTITUDE SCALES from Sajan Ks

TYPE AND USES OF ATTITUDE SCALES

Attitude scales are usually used for the measurement of attitude towards any other individuals, objects, ideas or things. These explain what the individual‟s acquired ways of thinking are for the present construct and that is attitude. Thus attitude scales (also known as opinionnaries) which usually consist of a large number of statements towards objects of attitude, are one such indirect measure. A measurement instrument that contains some combined statements related with particular attitude or its sub-dimensions and provides a combine score is called an attitude scale. Anastasi (1976) defined that attitude scales are designed to provide a quantitative measure of the individual's relative position along a uni-dimensional attitude continuum and it yields a total score indicating the direction and intensity of theindividual‟s attitude towards an object or other stimulus category. Thus, one method of assessing the attitudes of an individual concerning a particular concept or object is, by using an attitude scale. Since an attitude scale is a hypothetical or latent variable relatively an immediately observable variable, attitude measurement consists of the assessment of an individual's responses to a set of situations. The set of situations is usually a set of statements (items) about the attitude object, to which the individual responds with a set of specified response categories "agree" and disagree".

Types attitude scales

·         Arbitrary Scales

·         Differential Scales

·         Summatted Scales

·         Cumulative Scales

·         Factor Scales

Arbitrary Scale

Arbitrary Scales are developed on adhoc basis and designed largely through the researcher„s own subjective selection of items. The researcher first collects a few statements or items which he believes are unambiguous and appropriate to a given topic. Some of these are selected for inclusion in the measuring instrument and then people are asked to check in a list the statements with which they agree. The chief merit of such scales is that they can be developed very easily, quickly and with relatively less expense. They can also be designed to be highly specific and adequate. Because of these benefits, such scales are widely used in practice. At the same time, there are some limitations of these scales. The most important one is that the researcher does not have objective evidence that such scales measure the concepts for which they have been developed. Others have simply to rely on researcher‟s insight and competence.

Differential Scales

Differential scales are associated with the name of L.L. Thurstone. These have been developed using consensus scale approach. Under such approach the selection of items is made by a panel of judges who evaluate the items keeping in view of whether they are relevant to the topic area and unambiguous in implication (Kothari, 2008).There are conditions, when the method of paired comparison is not well suited to the situation, the reason being that number of statements to be scaled is large probably because subjects do not have the patience to make a large number of comparative judgments. In such a situation, the solution is to scale the statements through the method of equal appearing interval where each subject is required to make only one comparative choice for each statement. Along with the statements, each subject is given 11 cards on which A to K are written. These cards are arrangedbefore the subjects in a manner that A is kept at the extreme left. „A‟ indicates the most unfavourable interval and „K‟ is kept extreme right and it represents the most favourable interval. The middle category is designated by the letters G to K which represent various degrees of favourableness and the cards lettered from E to A represent various degrees of unfavourableness. A number of statements, usually 20 or more, are gathered that express various points of views towards the situation (Best, 2006). | A | B | C | D | E | F | G | H | I | J | K | Unfavourable Neutral FavourableThurstone and Chave defined only the two extremes and the middle category (of the 11 intervals) on the ground that the undefined between successive cards would represent equal appearing intervals for all the subjects. The subjects are requested to sort the given statements in terms of 11 intervals represented by 11 cards. Ordinarily, there is no limit for sorting. But Thurstone and Chave reported that subjects took 45 minutes in sorting 130 statements into 11 intervals. Thurstone and Chave made the following assumptions in this method:

(i) The intervals into which the statements are sorted or rated are equal.

(ii) The attitude of the subjects does not influence the sorting of the statements into the various

In the other words, subjects having favourable attitudes and those having unfavorable attitude would do the sorting in a similar manner. Thus the scale values of the statement are independent of the attitude of the judges (Chanderakandan, et al. (2001).

Summated Scales or Likert Scales

A  Summated scale or Likert scale is a psychometric scale commonly involved in research that employs questionnaires. It is the most widely used approach to scaling responses in survey research, such that the term (or more accurately the Likert-type scale) is often used interchangeably with rating scale, although there are other types of rating scalesThe scale is named after its inventor, psychologist Rensis Likert.A scale can be created as the simple sum or average of questionnaire responses over the set of individual items (questions). In so doing, Likert scaling assumes distances between each choice (answer option) are equal. Many researchers employ a set of such items that are highly correlated (that show high internal consistency) but also that together will capture the full domain under study (which requires less-than perfect correlations).

Cumulative Scales

The method of cumulative scaling is developed by using Guttman‟s scale. Guttman‟s method of scale analysis or scalogram analysis differs considerably from the two methods of attitude scales construction discussed previously. The Guttman Scale is based upon the methods of cumulative scaling and has been defined by Guttman (1950) himself as -“We shall call a set of items of common content a scale if a person with a higher rank than another person is just high as orhigher on every item than the other person”It states that a scale will mean a set of items of common content subject to the condition that a person with higher rank or score will rank higher than another person on the same set of statements. It is in such condition that Guttman‟s Scale operates. For example, a person whoresponds with “yes” to item (a) will also be responding in “yes” term to items (b), (c) and (d). All the four items are measuring the same dimension, that is, height and Guttman (1945) called it uni-dimensional scale”. Similarly, if a set of attitude statements measure the same attitude, they are said to constitute a uni-dimensional scale or a Guttman Scale. According to Guttman, one advantage of the uni-dimensional scale is that from the total score of the person one can reproduce the pattern of his responses to the statements. Suppose, for example, that in the above sample, “yes” is given a weight of 1 and “no” is given a weight of 4, we can say that he has responded “yes” to items a,b,c &d. Likewise, if a person has secured a total weight of 3, he has responded “yes” to item b, c and d “No” to item a. Such prediction regarding the perfect reproducibility is true in a perfect Guttman scale only. In case of attitude, statements showing perfect reproducibility are rarely achieved because some degrees of irrelevancy is always present.A case of perfect reproducibility has been demonstrated where in responses of 10 subjects towards five items have been displayed. Each item has two responses categories –Agree and Disagree. The response category “Agree” is scored with one the other response category “Disagree” is scored with 0. Subsequently, an attempt is made to evaluate the scalability of the items. If the coefficient of reproducibility is below 0.90, no enumerative scale is said to exist between the values 0.85 to 0.90, a quasi-scale is said to exist. Thus for Guttman, the co-efficient of reproducibility must be at least 0.90 for constituting the cumulative scale.The major criticism of the Guttman scale is that it ignores the problem of selecting representative items from the initial pool. As a matter of fact, no scientific procedures have been instituted for selection of items.

Factor Scales

Factor scales are developed through factor analysis or on the basis of inter-correlation of items which indicates that a common factor accounts for the relationships between items. Kothari (2008) cited Emory, (1976) that factor scales are particularly “useful in uncovering latent attitude dimensions and approach scaling through the concept of multiple-dimension attribute space. More specifically the two problems viz., how to uncover underlying (latent) dimensions which have not been identified, are dealt with through factor scales. An important factor scale based onthe factor analysis is Semantic Differential (S.D.) and the other one is Multidimensional Scaling.

(i) Semantic differential scale:- Semantic differential scale or the S.D. scale developed by Charles E. Osgood, G.J. Suci and P.H. Tanenbaum (1957), is an attempt to measure the psychological meanings of an object to an individual. This scale is based on the presumption that an object can have different dimensions: - property space or what can be called the semantic space- in the context of Semantic differential scale. The semantic differential technique is meant for obtaining a person‟s psychological reactions to certain objects, persons or ideas under study. The term semantic differential means a study of the differences in the psychological meanings of an object etc. It consists of a number of bipolar adjectives each having seven equally spaced scale points. The respondent indicates an attitude or opinion by checking on any one of seven spaces between the two extremes.

(ii)Multidimensional scaling: Two approaches, the metric and the non-metric both, are usually discussed and used in the context of MDS, while attempting to construct a space containing mpoint such that m(m-1)/2 inter-point distance reflect the input data. The metric approach to MDS treats the input data an interval scale data and solves by applying statistical methods for the additive constant which minimizes the dimensionality of the solution space. This approach utilizes all the dimensionality of the solution. The non-metric approach first gathers the non￾metric similarities by asking respondents to rank order all possible pairs that can be obtained from a set of objects. Such non-metric data is then transformed into some arbitrary metric space. and then the solution is obtained by reducing the dimensionality.

Uses of Arbitrary  scales :

Arbitrary scales are developed on ad hoc basis and are designed largely through the researcher’s own subjective selection of items. The researcher first collects few statements or items which he believes are unambiguous and appropriate to a given topic. Some of these are selected for inclusion in the measuring instrument and then people are asked to check in a list the statements with which they agree.

The chief merit of such scales is that they can be developed very easily, quickly and with relatively less expense. They can also be designed to be highly specific and adequate. Because of these benefits, such scales are widely used in practice.

Uses of Differential scales

The semantic differential is today one of the most widely used scales used in the measurement of attitudes. One of the reasons is the versatility of the items. The bipolar adjective pairs can be used for a wide variety of subjects, and as such the scale is called by some "the ever ready battery" of the attitude researcher.A specific form of the SD, Projective Semantics method uses only most common and neutral nouns that correspond to the 7 groups (factors) of adjective-scales most consistently found in cross-cultural studies (Evaluation, Potency, Activity as found by Osgood, and Reality, Organization, Complexity, Limitation as found in other studies). In this method, seven groups of bipolar adjective scales corresponded to seven types of nouns so the method was thought to have the object-scale symmetry (OSS) between the scales and nouns for evaluation using these scales. For example, the nouns corresponding to the listed 7 factors would be: Beauty, Power, Motion, Life, Work, Chaos, Law. Beauty was expected to be assessed unequivocally as “very good” on adjectives of Evaluation-related scales, Life as “very real” on Reality-related scales, etc. However, deviations in this symmetric and very basic matrix might show underlying biases of two types: scales-related bias and objects-related bias. This OSS design had meant to increase the sensitivity of the SD method to any semantic biases in responses of people within the same culture and educational background.

Uses of summatted scales

·         To measure the social attitude Likert scale is used.

·         It uses only the definitely favourable and unfavorable statement.

·         It consists series of statements to which the respondents is to react.

·         Each response is given as numerical score and the total score of a respondents is found out by summing up his different scores for different purposes.

·         The Likert scale uses several degrees of agreement or disagreement.

Uses of cumulative scales

Highly hierarchical and structured in nature: Due to the hierarchical and structured nature of this scale, it can be extremely productive in short surveys and questionnaires. For example, to analyze social distance, employee hierarchy, stages of evolution etc.

Implemented to gain insights for multiple queries: Guttman scale includes multiple statements for the respondents to answer which occupies a short space in an online survey.

More intuitive than other uni-dimensional scales: The way in which the answers are represented in this scale makes Guttman scale extremely intuitive for users.

Produces data in a ranked manner: The statements mentioned in this scale have their degree of importance and values associated accordingly. Thus, the results of this scale are in terms of ranks. 

CORRELATIONAL RESEARCH

WHAT IS CORRELATIONAL RESEARCH, WHEN Do

YOU USE IT, AND HOW DID IT DEVELOP?

Correlational designs provide an opportunity for you to predict scores and explain the

relationship among variables. In correlational research designs, investigators use the cor￾relation statistical test to describe and measure the degree of association (or relationship)

between two or more variables or sets of scores. In this design, the researchers do not

attempt to control or manipulate the variables as in an experiment; instead, they relate,

using the correlation statistic, two or more scores for each person (e.g., a stunt motiva￾tion and a student achievement score for each individual).

A correlation is a statistical test to determine the tendency or pattern for two (or more)

variables or two sets of data to vary consistently. In the case of only two variables, this

means that two variables share common variance, or they co-vary together. To say that

two variables co-vary has a somewhat complicated mathematical basis. Co-vary means

that we can predict a score on one variable with knowledge about the individual’s score

on another variable. A simple example might illustrate this point. Assume that scores on

a math quiz for fourth-grade students range from 30 to 90. We are interested in whether

scores on an in-class exercise in math (one variable) can predict the student’s math quiz

scores (another variable). If the scores on the exercise do not explain the scores on the

ath quiz, then we cannot predict anyone’s score except to say that it might range from

30 to 90. If the exercise could explain the variance in all of the math quiz scores, then

we could predict the math scores perfectly. This situation is seldom achieved; instead,

we might fi nd that 40% of the variance in math quiz scores is explained by scores on the

exercise. This narrows our prediction on math quiz scores from 30 to 90 to something

less, such as 40 to 60. The idea is that as variance increases, we are better able to predict

scores from the independent to the dependent variable (Gall, Borg, & Gall, 1996).

 The statistic that expresses a correlation statistic as a linear relationship is the product–moment correlation coefficient.

 is also called the bivariate correlation, zero-order

corre lation, or simply r, and it is indicated by an “r” for its notation. The statistic is calculated for two variables ( r

xy ) by multiplying the z scores on X and Y for each case and then divid￾ing by the number of case minus one (e.g., see the detailed steps in Vockell & Ashner, o

as the prediction that ability, quality of schooling, student motivation, and academic

coursework infl uence student achievement (Anderson & Keith, 1997). You also use this

design when you know and can apply statistical knowledge based on calculating the cor￾relation statistical test.

How Did Correlational Research Develop?

The history of correlational research draws on the themes of the origin and development

of the correlation statistical test and the procedures for using and interpreting the test. Stat￾isticians fi rst developed the procedures for calculating the correlation statistics in the late

19th century (Cowles, 1989). Although British biometricians articulated the basic ideas ofWHAT ARE THE KEY CHARACTERISTICS OF CORRELATIONAL DESIGNS?

As suggested by the explanatory and prediction designs, correlation research includes

specifi c characteristics:

◆ Displays of scores (scatterplots and matrices)

◆ Associations between scores (direction, form, and strength)

◆ Multiple variable analysis (partial correlations and multiple regression)

 Displays of Scores

If you have two scores, in correlation research you can plot these scores on a graph (or

scatterplot) or present them in a table (or correlation matrix).

Scatterplots

Researchers plot scores for two variables on a graph to provide a visual picture of the

form of the scores. This allows researchers to identify the type of association among vari￾ables and locate extreme scores. Most importantly, this plot can provide useful informa￾tion about the form of the association—whether the scores are linear (follow a straightline) or curvilinear (follow a U-shaped form). It also indicates the direction of the associ￾ation (e.g., one score goes up and the other goes up as well) and the degree of the asso￾ciation (whether the relationship is perfect, with a correlation of 1.0, or less than perfect).

A plot helps to assess this association between two scores for participants. A scat￾terplot (or scatter diagram) is a pictorial image displayed on a graph of two sets of

scores for participants. These scores are typically identifi ed as X and Y, with X values

represented on the horizontal axis, and Y values represented on the vertical axis. A single

point indicates where the X and Y scores intersect for one individual.

Using scales on the horizontal (abscissa) axis and on the vertical (ordinate) axis,

the investigator plots points on a graph for each participant. Examine the scatterplot of

scores in Figure 11.1, which shows both a small data set for 10 students and a visual plot

of their scores. Assume that the correlation researcher seeks to study whether the use of

the Internet by high school students relates to their level of depression. (We can assume

that students who use the Internet excessively are also depressed individuals because

they are trying to escape and not cope with present situations.) From past research, we

would predict this situation to be the case. We measure scores on the use of the Internet

by asking the students how many hours per week they spend searching the Internet. We

measure individual depression scores on an instrument with proven valid and reliable

scores. Assume that there are 15 questions about depression on the instrument with a

rating scale from 1 ( strongly disagree) to 5 ( strongly agree). This means that the summed

scores will range from 15 to 45.

As shown in Figure 11.1, hypothetical scores for 10 students are collected and plot￾ted on the graph. Several aspects about this graph will help you understand it:

◆ The “hours of Internet use” variable is plotted on the X axis, the horizontal axis.

◆ The “depression” variable is plotted on the Y axis, the vertical axis.

FIGURE 11.1

Example of a Scatterplot

10

20

30

40

50

Depression Scores

Y = D.V.

Hours of

Internet

Use per

Week

Depression

(Scores from

15 to 45)

17

13

 5

 9

 5

15

 7

 6

 2

18

Laura

Chad

Patricia

Bill

Rosa

Todd

Angela

Jose

Maxine

Jamal

Mean Score

30

41

18

20

25

44

20

30

17

45 Hours of Internet Use Per Week

X = I.V.

5

M

M

+ –

–

+

10 15 20Each student in the study has two scores: one for hours per week of Internet use

and one for depression.

◆ A mark (or point) on the graph indicates the score for each individual on depres￾sion and hours of Internet use each week. There are 10 scores (points) on the

graph, one for each participant in the study.

The mean scores ( M) on each variable are also plotted on the graph. The students

used the Internet for an average of 9.7 hours per week, and their average depression

score was 29.3. Drawing vertical and horizontal lines on the graph that relate to the

mean scores ( M), we can divide the plot into four quadrants and assign a minus (–) to

quadrants where scores are “negative” and a plus (+) to quadrants where the scores are

“positive.” In our example, to have a depression score below 29.3 ( M) is positive because

that suggests that the students with such a score have less depression. To score above

29.3 ( M) indicates more severe depression, and this is “negative.” Alternatively, to use the

Internet less than 9.7 ( M) hours per week is “positive” (i.e., because students can then

spend more time on homework), whereas to spend more time than 9.7 hours is “nega￾tive” (i.e., overuse of Internet searching is at the expense of something else). To be both

highly depressed (above 29.3 on depression) and to use the Internet frequently (above

9.7 on Internet use) is what we might have predicted based on past literature.

Note three important aspects about the scores on this plot. First, the direction of

scores shows that when X increases, Y increases as well, indicating a positive association.

Second, the points on the scatterplot tend to form a straight line. Third, the points would

be reasonably close to a straight line if we were to draw a line through all of them. These

three ideas relate to direction, form of the association, and the degree of relationship that

we can learn from studying this scatterplot. We will use this information later when we

discuss the association between scores in correlation research.

A Correlation Matrix

Correlation researchers typically display correlation coeffi cients in a matrix. A correlation

matrix presents a visual display of the correlation coeffi cients for all variables in a study.

In this display, we list all variables on both a horizontal row and a vertical column in the

table. Correlational researchers present correlation coeffi cients in a matrix in publishedWHAT ARE THE STEPS IN CONDUCTING

A CORRELATIONAL STUDY?

From our discussion about the key characteristics of correlational research, we can begin

to see steps emerge that you might use when planning or conducting a study. The fol￾lowing steps illustrate the process of conducting correlational research.

Step 1. Determine If a Correlational Study Best Addresses

the Research Problem

A correlational study is used when a need exists to study a problem requiring the identi-

fi cation of the direction and degree of association between two sets of scores. It is useful

for identifying the type of association, explaining complex relationships of multiple fac￾tors that explain an outcome, and predicting an outcome from one or more predictors.

Correlational research does not “prove” a relationship; rather, it indicates an association

between two or more variables.

Because you are not comparing groups in a correlational study, you use research

questions rather than hypotheses. Sample questions in a correlational study might be:

◆ Is creativity related to IQ test scores for elementary children? (associating two variables)

◆ What factors explain a student teacher’s ethical behavior during the student-teaching

experience? (exploring a complex relationship)

◆Step 2. Identify Individuals to Study

Ideally, you should randomly select the individuals to generalize results to the popula￾tion, and seek permissions to collect the data from responsible authorities and from

the institutional review board. The group needs to be of adequate size for use of the

correlational statistic, such as N = 30; larger sizes contribute to less error variance and bet￾ter claims of representativeness. For instance, a researcher might study 100 high school

athletes to correlate the extent of their participation in different sports and their use of

tobacco. A narrow range of scores from a population may infl uence the strength of the

correlation relationships. For example, if you look at the relationship between height of

basketball players and number of baskets in a game, you might fi nd a strong relationship

among K–12th graders. But if you are selecting NBA players, this relationship may be

signifi cantly weaker.

Step 3. Identify Two or More Measures for

Each Individual in the Study

Because the basic idea of correlational research is to compare participants in this single

group on two or more characteristics, measures of variables in the research question

need to be identifi ed (e.g., literature search of past studies), and instruments that mea￾sure the variables need to be obtained. Ideally, these instruments should have proven

validity and reliability. You can obtain permissions from publishers or authors to use the

instruments. Typically one variable is measured on each instrument, but a single instru￾ment might contain both variables being correlated in the study.

Step 4. Collect Data and Monitor Potential Threats

The next step is to administer the instruments and collect at least two sets of data from

each individual. The actual research design is quite simple as a visual presentation. Two

data scores are collected for each individual until you obtain scores from each person in

the study. This is illustrated with three individuals as follows:

Participants: Measures or Observations:

Individual 1 01 02

Individual 2 01 02

Individual 3 01 02

This situation holds for describing the association between two variables or for predicting

a single outcome from a single predictor variable. You collect multiple independent vari￾ables to understand complex relationships.

A small sample database for 10 college students is shown in Table 11.3. The inves￾tigator seeks to explain the variability in fi rst-year grade point averages (GPAs) for these

10 graduate students in education. Assume that our investigator has identifi ed these four

predictors in a review of the literature. In past studies, these predictors have positively

correlated with achievement in college. The researcher can obtain information for the

predictor variables from the college admissions offi ce. The criterion, GPA during the

fi rst year, is available from the registrar’s offi ce. In this regression study, the researcher

seeks to identify which one factor or combination of factors best explains the variance

in fi rst-year graduate-student GPAs. A review of this data shows that the scores varied on

each variable, with more variation among GRE scores than among recommendation andfi t-to-program scores. Also, it appears that higher college GPA and GRE scores are posi￾tively related to higher fi rst-semester GPAs.

In this example, because the data were available from admissions offices, the

researcher need not be overly concerned about procedures that threaten the validity of

the scores. However, a potential for restricted range of scores—little variation in scores—

certainly exists. Other factors that might affect the researcher’s ability to draw valid infer￾ences from the results are the lack of standard administration procedures, the conditions

of the testing situation, and the expectations of participants.

 Step 5. Analyze the Data and Represent the Results

The objective in correlational research is to describe the degree of association between

two or more variables. The investigator looks for a pattern of responses and uses statisti￾cal procedures to determine the strength of the relationship as well as its direction. A sta￾tistically signifi cant relationship, if found, does not imply causation (cause and effect) but

merely an association between the variables. More rigorous procedures, such as those

used in experiments, can provide better control than those used in a correlational study.

The analysis begins with coding the data and transferring it from the instruments into

a computer fi le. Then the researcher needs to determine the appropriate statistic to use.

An initial question is whether the data are linearly or curvilinearly related. A scatterplot of

the scores (if a bivariate study) can help determine this question. Also, consider whether:

◆ Only one independent variable is being studied (Pearson’s correlation coeffi cient)

◆ A mediating variable explains both the independent and dependent variables and

needs to be controlled (partial correlation coeffi cient)◆ More than one independent variable needs to be studied to explain the variability

in a dependent variable (multiple regression coeffi cient)

Based on the most appropriate statistical test, the researcher next calculates whether

the statistic is signifi cant based on the scores. For example, a p value is obtained in a

bivariate study by:

◆ Setting the alpha level

◆ Using the critical values of an r table, available in many statistics books

◆ Using degrees of freedom of N = 2 with this table

◆ Calculating the observed r coeffi cient and comparing it with the r-critical value

◆ Rejecting or failing to reject the null hypothesis at a specifi c signifi cance level, such

as p 6 0.05

In addition, it is useful to also report effect size ( r 2). In correlational analysis, the

effect size is the Pearson’s correlation coeffi cient squared. In representing the results, the

correlational researcher will present a correlation matrix of all variables as well as a sta￾tistical table (for a regression study) reporting the R and R 2

 values and the beta weights

for each variable.

Step 6. Interpret the Results

The fi nal step in conducting a correlational study is interpreting the meaning of the

results. This requires discussing the magnitude and the direction of the results in a cor￾relational study, considering the impact of intervening variables in a partial correlation

study, interpreting the regression weights of variables in a regression analysis, and devel￾oping a predictive equation for use in a prediction study.

In all of these steps, an overall concern is whether your data support the theory, the

hypotheses, or questions. Further, the researcher considers whether the results confi rm

or disconfi rm fi ndings from other studies. Also, a refl ection is made about whether some

of the threats discussed above may have contributed to erroneous coeffi cients and the

steps that might be taken by future researchers to address these concern◆ More than one independent variable needs to be studied to explain the variability

in a dependent variable (multiple regression coeffi cient)

Based on the most appropriate statistical test, the researcher next calculates whether

the statistic is signifi cant based on the scores. For example, a p value is obtained in a

bivariate study by:

◆ Setting the alpha level

◆ Using the critical values of an r table, available in many statistics books

◆ Using degrees of freedom of N = 2 with this table

◆ Calculating the observed r coeffi cient and comparing it with the r-critical value

◆ Rejecting or failing to reject the null hypothesis at a specifi c signifi cance level, such

as p 6 0.05

In addition, it is useful to also report effect size ( r 2). In correlational analysis, the

effect size is the Pearson’s correlation coeffi cient squared. In representing the results, the

correlational researcher will present a correlation matrix of all variables as well as a sta￾tistical table (for a regression study) reporting the R and R 2

 values and the beta weights

for each variable.

Step 6. Interpret the Results

The fi nal step in conducting a correlational study is interpreting the meaning of the

results. This requires discussing the magnitude and the direction of the results in a cor￾relational study, considering the impact of intervening variables in a partial correlation

study, interpreting the regression weights of variables in a regression analysis, and devel￾oping a predictive equation for use in a prediction study.

In all of these steps, an overall concern is whether your data support the theory, the

hypotheses, or questions. Further, the researcher considers whether the results confi rm

or disconfi rm fi ndings from other studies. Also, a refl ection is made about whether some

of the threats discussed above may have contributed to erroneous coeffi cients and the

steps that might be taken by future researchers to address these concerns.s.HOW DO YOU EVALUATE A CORRELATIONAL STUDY?

To evaluate and assess the quality of a good correlational study, authors consider:

◆ An adequate sample size for hypothesis testing.

◆ The display of correlational results in a matrix or graph.

◆ An interpretation about the direction and magnitude of the association between

two (or more) variables.

◆ An assessment of the magnitude of the relationship based on the coeffi cient of

determination, p values, effect size, or the size of the coeffi cient.

◆ The choice of an appropriate statistic for analysis.

◆ The identifi cation of predictor and the criterion variables.

◆ If a visual model of the relationships is advanced, the researcher indicates the

expected direction of the relationships among variables, or the predicted direction

based on observed data.

◆ The clear identifi cation of the statistical procedures.The Defi nition, Use, and Development of Correlational Research

In some educational situations, neither the treatment nor the ability to manipulate the

conditions are conducive to an experiment. In this case, educators turn to a correla￾tional design. In correlational research, investigators use a correlation statistical technique

to describe and measure the degree of association (or relationship) between two or

more variables or sets of scores. You use a correlational design to study the relationship

between two or more variables or to predict an outcome.

The history of correlational research draws on the themes of the origin and develop￾ment of the correlation statistical test and the procedures for using and interpreting the

statistical test. Statisticians fi rst identifi ed the procedures for calculating the correlation

statistics in the late 19th century. In the late 1800s, Karl Pearson developed the familiar

correlation formula we use today. With the use of multiple statistical procedures such as

factor analysis, reliability estimates, and regression, researchers can test elaborate models

of variables using correlational statistical procedures.

Types of Correlational Designs

Although a correlation is a statistic, its use in research has contributed to a specifi c

research design called correlational research. This research has taken two primary forms

of research design: explanation and prediction. An explanatory correlational design

explains or clarifi es the degree of association among two or more variables at one point

in time. Researchers are interested in whether two variables co-vary, in which a change

in one variable is refl ected in changes in the other. An example is whether motivation

is associated with academic performance. In the second form of design, a prediction

design, the investigator identifi es variables that will positively predict an outcome or cri￾terion. In this form of research, the researcher uses one or more predictor variables and

a criterion (or outcome) variable. A prediction permits us to forecast future performance,

such as whether a student’s GPA in college can be predicted from his or her high school

performance.

Key Characteristics of Correlational Designs

Underlying both of these designs are key characteristics of correlational research.

Researchers create displays of scores correlated for participants. These displays are scat￾terplots, a graphic representation of the data, and correlation matrices, a table that shows

the correlation among all the variables. To interpret correlations, researchers examine

the positive or negative direction of the correlation of scores, a plot of the distribution of

scores to see if they are normally or nonnormally distributed, the degree of association

between scores, and the strength of the association of the scores. When more than two

variables are correlated, the researcher is interested in controlling for the effects of the

third variable, and in examining a prediction equation of multiple variables that explains

the outcome.

Ethical Issues in Conducting Correlational Research

Ethical issues arise in many phases of the correlational research process. In data collec￾tion, ethics relate to adequate sample size, lack of control, and the inclusion of as many

predictors as possible. In data analysis, researchers need a comjto include effect size and the use of appropriate statistics. Analysis cannot include mak￾ing up data. In recording and presenting studies, the write-up should include statements

about relationships rather than causation, a willingness to share data, and publishing in

scholarly outlets.

Steps in Conducting a Correlational Study

Steps in conducting a correlational study are to use the design for associating variables or

making predictions, to identify individuals to study, to specify two or more measures for

each individual, to collect data and monitor potential threats to the validity of the scores,

to analyze the data using the correlation statistic for either continuous or categorical data,

and to interpret the strength and the direction of the results.

Criteria for Evaluating a Correlational Study

Evaluate a correlational study in terms of the strength of its data collection, analysis, and

interpretations. These factors include adequate sample size, good presentations in graphs

and matrices, clear procedures, and an interpretation about the relationship among variables.

USEFUL INFORMATION FOR PRODUCERS OF RESEARCH

◆ Identify whether you plan to examine the association between or among variables

or use correlational research to make predictions about an outcome.

◆ Plot on a graph the association between your variables so that you can determine

the direction, form, and strength of the association.

◆ Use appropriate correlational statistics in your design based on whether the

data are continuous or categorical and whether the form of the data is linear or

nonlinear.

◆ Present a correlation matrix of the Pearson coeffi cients in your study.

USEFUL INFORMATION FOR CONSUMERS OF RESEARCH

◆ Recognize that a correlation study is not as rigorous as an experiment because

the researcher can only control statistically for variables rather than physically

manipulate variables. Correlational studies do not “prove” relationships; rather,

they indicate an association between or among variables or sets of scores.

◆ Correlational studies are research in which the investigator seeks to explain the

association or relationship among variables or to predict outcomes.

◆ Realize that all correlational studies, no matter how advanced the statistics, use a

correlation coeffi cient as their base for analysis. Understanding the intent of this

coeffi cient helps you determine the results in a correlational study.to include effect size and the use of appropriate statistics. Analysis cannot include mak￾ing up data. In recording and presenting studies, the write-up should include statements

about relationships rather than causation, a willingness to share data, and publishing in

scholarly outlets.

Steps in Conducting a Correlational Study

Steps in conducting a correlational study are to use the design for associating variables or

making predictions, to identify individuals to study, to specify two or more measures for

each individual, to collect data and monitor potential threats to the validity of the scores,

to analyze the data using the correlation statistic for either continuous or categorical data,

and to interpret the strength and the direction of the results.

Criteria for Evaluating a Correlational Study

Evaluate a correlational study in terms of the strength of its data collection, analysis, and

interpretations. These factors include adequate sample size, good presentations in graphs

and matrices, clear procedures, and an interpretation about the relationship among variables.

USEFUL INFORMATION FOR PRODUCERS OF RESEARCH

◆ Identify whether you plan to examine the association between or among variables

or use correlational research to make predictions about an outcome.

◆ Plot on a graph the association between your variables so that you can determine

the direction, form, and strength of the association.

◆ Use appropriate correlational statistics in your design based on whether the

data are continuous or categorical and whether the form of the data is linear or

nonlinear.

◆ Present a correlation matrix of the Pearson coeffi cients in your study.

USEFUL INFORMATION FOR CONSUMERS OF RESEARCH

◆ Recognize that a correlation study is not as rigorous as an experiment because

the researcher can only control statistically for variables rather than physically

manipulate variables. Correlational studies do not “prove” relationships; rather,

they indicate an association between or among variables or sets of scores.

◆ Correlational studies are research in which the investigator seeks to explain the

association or relationship among variables or to predict outcomes.

◆ Realize that all correlational studies, no matter how advanced the statistics, use a

correlation coeffi cient as their base for analysis.

CASE STUDY - A RESEARCH METHODOLOGY

CASE STUDY

The case study is a way of organising social data for the purpose of viewing social reality.It examine a social unit as a whole.The unit may be a person,a family,a social group,a social institution or a community.

The purpose is to understand the life cycle or an important part of the cycle of the unit.The case study probes deeply and analyzed interaction between the factors that explain present status or that influence change or growth.

Nature of case study

1. A descriptive study

a. (I.e. the data collected constitute descriptions of psychological processes and events, and of the contexts in which they occurred (qualitative data).

b. The main emphasis is always on the construction of verbal descriptions of behaviour or experience but quantitative data may be collected.

c. High levels of detail are provided.

2. Narrowly focused.

a. Typically a case study offers a description of only a single individual, and sometimes about groups.

b. Often the case study focuses on a limited aspect of a person, such          as their psychopathological symptoms.

3. Combines objective and subjective data

a. i.e. the researcher may combine objective and subjective data: All are regarded as valid data for analysis, and as a basis for inferences within the case study.

i. The objective description of behaviour and its context

ii. Details of the subjective aspect, such as feelings, beliefs,

impressions or interpretations. In fact, a case study is uniquely able to offer a means of achieving an in-depth understanding of the   behaviour and experience of a single individual.

4. Process-oriented.

a. The case study method enables the researcher to explore and describe the nature of processes, which occur over time.

b. In contrast to the experimental method, which basically provides a stilled ‘snapshot’ of processes, which may be continuing over time like for example the development of language in children over time.

5.Longitudinal approach

  The case study method showing development over a period of time.

6.The number of unit to be studied is small.

7.It studies a social unit deeply and thoroughly.

8.It is qualitative as well as quantitative.

9.It covers sufficient wide cycle of time.

    10.It has continuity in nature.
Meaning: The case study method is a very popular form of qualitative analysis and involves a careful and complete observation of a social unit, be that unit a person, a family, an institution, a cultural group or even the entire community. It is a method of study in depth rather than breadth. The case study places more emphasis on the full analysis of a limited number of events or conditions and their interrelations. The case study deals with the processes that take place and their interrelationship. Thus, case study is essentially an intensive investigation of the particular unit under consideration. The object of the case study method is to locate the factors that account for the behaviour-patterns of the given unit as an integrated totality.

Case study method of Data Collection
According to H. Odum, “The case study method of data collection is a technique by which individual factor whether it be an institution or just an episode in the life of an individual or a group is analysed in its relationship to any other in the group.” Thus, a fairly exhaustive study of a person (as to what he does and has done, what he thinks he does and had done and what he expects to do and says he ought to do) or group is called a life or case history. Burgess has used the words “the social microscope” for the case study method.” Pauline V. Young describes case study as “a comprehensive study of a social unit be that unit a person, a group, a social institution, a district or a community.” In brief, we can say that case study method is a form of qualitative analysis where in careful and complete observation of an individual or a situation or an institution is done; efforts are made to study each and every aspect of the concerning unit in minute details and then from case data generalisations and inferences are drawn.

Characteristics of Case Study method
The important characteristics of the case study method are as under:

Under this method the researcher can take one single social unit or more of such units for his study purpose; he may even take a situation to study the same comprehensively.
Here the selected unit is studied intensively i.e., it is studied in minute details. Generally, the study extends over a long period of time to ascertain the natural history of the unit so as to obtain enough information for drawing correct inferences.
In the context of this method we make complete study of the social unit covering all facets. Through this method we try to understand the complex of factors that are operative within a social unit as an integrated totality.
Under this method the approach happens to be qualitative and not quantitative. Mere quantitative information is not collected. Every possible effort is made to collect information concerning all aspects of life. As such, case study deepens our perception and gives us a clear insight into life. For instance, under this method we not only study how many crimes a man has done but shall peep into the factors that forced him to commit crimes when we are making a case study of a man as a criminal. The objective of the study may be to suggest ways to reform the criminal.
In respect of the case study method an effort is made to know the mutual inter-relationship of causal factors.
Under case study method the behaviour pattern of the concerning unit is studied directly and not by an indirect and abstract approach.
Case study method results in fruitful hypotheses along with the data which may be helpful in testing them, and thus it enables the generalised knowledge to get richer and richer. In its absence, generalised social science may get handicapped.
Evolution and scope: The case study method is a widely used systematic field research technique in sociology these days. The credit for introducing this method to the field of social investigation goes to Frederic Le Play who used it as a hand-maiden to statistics in his studies of family budgets. Herbert Spencer was the first to use case material in his comparative study of different cultures. Dr. William Healy resorted to this method in his study of juvenile delinquency, and considered it as a better method over and above the mere use of statistical data. Similarly, anthropologists, historians, novelists and dramatists have used this method concerning problems pertaining to their areas of interests. Even management experts use case study methods for getting clues to several management problems. In brief, case study method is being used in several disciplines. Not only this, its use is increasing day by day.

Assumptions: The case study method is based on several assumptions. The important assumptions may be listed as follows:

The assumption of uniformity in the basic human nature in spite of the fact that human
behaviour may vary according to situations.
The assumption of studying the natural history of the unit concerned.
The assumption of comprehensive study of the unit concerned.
Major phases involved: Major phases involved in case study are as follows:

Recognition and determination of the status of the phenomenon to be investigated or the unit of attention.
Collection of data, examination and history of the given phenomenon.
Diagnosis and identification of causal factors as a basis for remedial or developmental treatment.
Application of remedial measures i.e., treatment and therapy (this phase is often characterized as case work).
Follow-up programme to determine effectiveness of the treatment applied.
Advantages: There are several advantages of the case study method that follow from the various characteristics outlined above. Mention may be made here of the important advantages.

Being an exhaustive study of a social unit, the case study method enables us to understand fully the behaviour pattern of the concerned unit. In the words of Charles Horton Cooley, “case study deepens our perception and gives us a clearer insight into life…. It gets at behaviour directly and not by an indirect and abstract approach.”
Through case study a researcher can obtain a real and enlightened record of personal experiences which would reveal man’s inner strivings, tensions and motivations that drive him to action along with the forces that direct him to adopt a certain pattern of behaviour.
This method enables the researcher to trace out the natural history of the social unit and its relationship with the social factors and the forces involved in its surrounding environment.
It helps in formulating relevant hypotheses along with the data which may be helpful in testing them. Case studies, thus, enable the generalised knowledge to get richer and richer.
The method facilitates intensive study of social units which is generally not possible if we use either the observation method or the method of collecting information through schedules. This is the reason why case study method is being frequently used, particularly in social researches.
Information collected under the case study method helps a lot to the researcher in the task of constructing the appropriate questionnaire or schedule for the said task requires thorough knowledge of the concerning universe.
The researcher can use one or more of the several research methods under the case study method depending upon the prevalent circumstances. In other words, the use of different methods such as depth interviews, questionnaires, documents, study reports of individuals, letters, and the like is possible under case study method.
Case study method has proved beneficial in determining the nature of units to be studied along with the nature of the universe. This is the reason why at times the case study method is alternatively known as “mode of organising data”.
This method is a means to well understand the past of a social unit because of its emphasis of historical analysis. Besides, it is also a technique to suggest measures for improvement in the context of the present environment of the concerned social units.
Case studies constitute the perfect type of sociological material as they represent a real record of personal experiences which very often escape the attention of most of the skilled researchers using other techniques.
Case study method enhances the experience of the researcher and this in turn increases his analysing ability and skill.
This method makes possible the study of social changes. On account of the minute study of the different facets of a social unit, the researcher can well understand the social change then and now. This also facilitates the drawing of inferences and helps in maintaining the continuity of the research process. In fact, it may be considered the gateway to and at the same time the final destination of abstract knowledge.
Case study techniques are indispensable for therapeutic and administrative purposes. They are also of immense value in taking decisions regarding several management problems. Case data are quite useful for diagnosis, therapy and other practical case problems.
Limitations: Important limitations of the case study method may as well be highlighted.

Case situations are seldom comparable and as such the information gathered in case studies is often not comparable. Since the subject under case study tells history in his own words, logical concepts and units of scientific classification have to be read into it or out of it by the investigator.
Read Bain does not consider the case data as significant scientific data since they do not provide knowledge of the “impersonal, universal, non-ethical, non-practical, repetitive aspects of phenomena.” Real information is often not collected because the subjectivity of the researcher does enter in the collection of information in a case study.
The danger of false generalisation is always there in view of the fact that no set rules are followed in collection of the information and only few units are studied.
It consumes more time and requires lot of expenditure. More time is needed under case study method since one studies the natural history cycles of social units and that too minutely.
The case data are often vitiated because the subject, according to Read Bain, may write what he thinks the investigator wants; and the greater the rapport, the more subjective the whole process is.
Case study method is based on several assumptions which may not be very realistic at times, and as such the usefulness of case data is always subject to doubt.
Case study method can be used only in a limited sphere., it is not possible to use it in case of a big society. Sampling is also not possible under a case study method.

Response of the investigator is an important limitation of the case study method. He often thinks that he has full knowledge of the unit and can himself answer about it. In case the same is not true, then consequences follow. In fact, this is more the fault of the researcher rather than that of the case method.