Scientific Method

What information is provided by the numerical value of the Pearson correlation?

Pearson correlation is a measure of the degree of association between two variables. The degree of association becomes stronger as it approaches the values 1 and -1. It also suggest something about the direction of the association, whether it is downward or upward.

In the following data, there are three scores (x, y, and z) for each of the n =5 individuals.

• Sketch a graph showing the relationship between X and Y, compute the Pearson correlation between X and Y.

Pearson correlation (r) = (?XY – (?X?Y/N))/(?(?X2-(?X)2/N)(?Y2-(?Y)2/N)

= (36-(10*15)/5)/(?(30-100/5)(55-(225/5))

= (36-30)/(?(30-20)(55-45))

= 6/(?(10*10))

= 6/10

= 0.6

• Sketch a graph showing the relationship between Y and Z. compose the pearson correlation between Y and Z.

Pearson correlation (r) = (?YZ – (?Z?Y/N))/(?(?Z2-(?Z)2/N)(?Y2-(?Y)2/N)

= (66-(20*15)/5)/(?(90-(400)/5)(55-(225/5))

= (66-60)/(?(90-80)(55-45))

= 6/(?(10*10))

= 6/10

= 0.6

• Given the result of parts A and B, what would you predict for the correlation between X and Z?

            The Pearson correlation in parts A and B are equal. Then we would predict that the correlation between X and Z are the same with parts A and B.

• Sketch a graph showing the relationship between X and Z. compute the Pearson correlation for these data.

Pearson correlation (r) = (?XZ – (?X?Z/N))/(?(?X2-(?X)2/N)(?Z2-(?Z)2/N)

= (38-(10*20)/5)/(?(30-100/5)(90-(400)/5))

= (38-40)/(?(30-20)(90-80))

= -2/(?(10*10))

= -2/10

= -0.2

• What general conclusion can you make concerning relationship among correlations? If X is related to Y and Y is related to Z, does this necessarily mean that X is related to Z?

            Transitivity is not applicable relationship among correlations. When one speaks of transitivity, this only means that for any variables a, b and c, if a = b and b = c then a = c. This definition is not applicable to correlations of variables. One cannot predict correlation between two variables based from correlations of other set of variables. For example, X is related to Y, and Y is related to Z. Then, one cannot predict or say that X is related to Z based on the given correlations.

•  Sketch a graph showing the line for the equation Y=2x-3 on the same graph, show the line for Y= -2x+8

A set of scores produces a regression equation of F=7x-2. Use the equation to find the predicted value of Y for each of the following X scores: 0, 2, 5, 8, 10

F = 7x – 2

F(0) = 7(0) – 2

= 0 – 2 = -2

F(2) = 7(2) – 2

= 14 – 2 = 12

F(5) = 7(5) – 2

= 35 – 2 = 33

F(8) = 7(8) – 2

= 56 – 2 = 54

F(10) = 7(10) – 2

= 70 – 2 = 68

For the following data:

• find the regression equation for predicting Y from X

? = bX + A

b = r(Sy/Sx), where r is the pearson correlation, Sy and Sx are the standard deviation of Y and X.

r = (?XY – (?X?Y/N))/(?(?X2-(?X)2/N)(?Y2-(?Y)2/N)

= (170-(25*30)/5)/(?(135-(625)/5)(346-(900)/5))

= (170-150)/(?(135-125)(346-180))

= (20)/?(10)(166)

= 20/?1660

= 20/40.7431

= 0.4909

b = 0.4909(6.4420/1.5811)

= 2.0001

A = My – bMx

= 6 – 2.0001(5)

= 6 – 10.0005

= -4.0005

? = 2.0001X – 4.0005

• use the regression equation to find a predicted Y for each X
  ? = 2.0001X –  4.0005

X
?

7
10.0002

5
6.0000

6
8.0001

3
1.9998

4
3.9999

• find the difference between the actual Y value and the predicted Y value for each individual, square the differences, and add the squared values to obtain residual.

?
Y
Y – ?
(Y – ?)2

10.0002
16
5.9998
35.9976

6.0000
2
-4.0000
16.0000

8.0001
1
-7.0001
49.0014

1.9998
2
-0.0002
0.0000

3.9999
9
5.0001
25.0010

SSresidual = ?(Y – ?)2 = 126

• calculate the Pearson correlation for these data. Use r² and SS? to compute SSresidual, with equation 15.13. You should obtain the same value as in part c.

r = (?XY – (?X?Y/N))/(?(?X2-(?X)2/N)(?Y2-(?Y)2/N)

= (170-(25*30)/5)/(?(135-(625)/5)(346-(900)/5))

= (170-150)/(?(135-125)(346-180))

= (20)/?(10)(166)

= 20/?1660

= 20/40.7431

= 0.4909
SSy = ?(Y – ?)2

= 166

SSresidual = (1 – r2)SSy

= (1 – 0.49092)(166)

= (1 – 0.2410)(166)

= (0.759)(166)

= 125.994 or 126

A professor noticed that the representatives on the college student government consist of 31 males and only 9 females. The general college population on the other hand, consists of 55% females and 45% males. Is the gender distribution for student government representatives significantly different from the distribution for the population? Test at the 0.5 level of significance.

Null Hypothesis

The gender distribution for student government representatives is not significantly different from the distribution for the population.

Alternative: The gender distribution for student government representatives is significantly different from the distribution for the population.

Observed
Expected

Male
31
18

Female
9
22

X2 (computed)= ?(observed – expected)2/(expected)

= (31-18)2/18 + (9-22)2/22

= 9.3889 + 7.6818

= 17.0707

X2 (critical)  = 3.841

Decision rule, reject null hypothesis if X2 ? 3.841. Otherwise, fail to reject the null hypothesis.

At a = 0.05, X2 = 17.0707 ? 3.841, then we reject the null hypothesis.

We are 95% confident that the gender distribution for student government representatives is significantly different from the distribution for the population.

Data from the department of motor vehicles indicate that 80% of all licensed drivers are older than 25.

• In a sample of n= 50 people who recently received speeding tickets, 32 were older than 25 years and the other 18 were age 25 or younger. Is the age distribution for this sample significantly different from the distribution for the population licensed divers? Use ? =.05.

Null Hypothesis: The age distribution for this sample is not significantly different from the distribution for the population licensed divers.

Alternative: The age distribution for this sample is not significantly different from the distribution for the population licensed divers.

Licensed Drivers
Observed
Expected

> 25 years old
32
40

? 25 years old
18
10

X2 (computed)= ?(observed – expected)2/(expected)

= (32-40)2/40 + (18-10)2/10

= 1.6 + 6.4

= 8

X2 (critical)  = 3.841

Decision rule, reject null hypothesis if X2 ? 3.841. Otherwise, fail to reject the null hypothesis.

At a = 0.05, X2 = 8 ? 3.841, then we reject the null hypothesis.

We are 95% confident that the age distribution for this sample is significantly different from the distribution for the population licensed divers.

• In a sample of n=50 people who recently received parking tickets. 38 were older than 25 years and the other 12 were age 25 or younger. Is the age distribution for this sample significantly different from the distribution for the population of licensed drivers? Use ? =.05.

Null Hypothesis: The age distribution for this sample is not significantly different from the distribution for the population licensed divers.

Alternative: The age distribution for this sample is not significantly different from the distribution for the population licensed divers.

Licensed Drivers
Observed
Expected

> 25 years old
38
40

? 25 years old
12
10

X2 (computed)= ?(observed – expected)2/(expected)

= (38-40)2/40 + (12-10)2/10

= 0.1 + 0.4

= 0.5

X2 (critical)  = 3.841

Decision rule, reject null hypothesis if X2 ? 3.841. Otherwise, fail to reject the null hypothesis.

At a = 0.05, X2 = 0.5 ? 3.841, then we fail to reject the null hypothesis.

We are 95% confident that the age distribution for this sample is not significantly different from the distribution for the population licensed divers.

A researcher obtained a random sample of n = 60 students to determine whether there were any significant preferences among three leading brands of colas. Each student tasted all the brands and then selected his or her favorite. The resulting frequency distribution is as follows:
Are the data sufficient to indicate any preferences among the three brands? Test with ? =.05.
Null hypothesis:  There are no significant preferences among three leading brands of colas.

Alternative: There are significant preferences among three leading brands of colas.

Brand
Observed
Expected

A
28
20

B
14
20

C
18
20

X2 (computed)= ?(observed – expected)2/(expected)

= (28-20)2/20 + (14-20)2/20 + (18-20)2/20

= 3.2 + 1.8 + 0.2

= 5.2

X2 (critical)  = 5.991

Decision rule, reject null hypothesis if X2 ? 5.991. Otherwise, fail to reject the null hypothesis.

At a = 0.05, X2 = 5.2 ? 5.991, then we fail to reject the null hypothesis.

We are 95% confident that there are no significant preferences among three leading brands of colas.

• A social psychologist suspects that people who serve on juries tends to be much older than citizens in the general population. Jurors are selected from the list of registered voters, so the ages for jurors should have the same distribution as the ages for voters. The psychologist obtains voters registration records and finds that 20% of registered voters are between 18 and 29 years old, and 35% are age 50or older. The psychologist also monitors jury composition over several weeks and observes the following distribution of ages for actual juries.

Age categories for juniors

18-29 30-49 50 and over
12 36 32
Are the data sufficient to conclude that the age distribution for the jurors is significantly different from the distribution for the population of registered voters? Test with ? =.05.
Null hypothesis: The age distribution for the jurors is not significantly different from the distribution for the population of registered voters.

Alternative: The age distribution for the jurors is not significantly different from the distribution for the population of registered voters

Age
Observed
Expected

18-29
12
16

30-49
36
36

50 and over
32
28

X2 (computed)= ?(observed – expected)2/(expected)

= (12-16)2/16 + (36-36)2/36 + (32-28)2/28

= 1 + 0 + 0.5714

= 1.5714

X2 (critical) = 5.991

Decision rule, reject null hypothesis if X2 ? 5.991. Otherwise, fail to reject the null hypothesis.

At a=0.05, X2 = 1.5714 ? 5.991. Thus, we fail to reject the null hypothesis.

We are 95% confident that the age distribution for the jurors is not significantly different from the distribution for the population of registered voters.

• A psychology professor is trying to decide which test-book to use for next year’s introductory class. To help make the decision, the professor asks the current students to make to review three texts and identify which one they prefer. The distribution preference for the current class is as follows:
  Do the data indicate any significant preferences among the three books? Test with ? =.05.

Null hypothesis: There are no significant preferences among the three books.

Alternative: There are significant differences among the three books.

Book
Observed
Expected

1
52
40

2
41
40

3
27
40

X2 (computed)= ?(observed – expected)2/(expected)

= (52-40)2/40 + (41-40)2/40 + (27-40)2/40

= 3.6 + 0.025 + 4.225

= 7.85

X2 (critical) = 5.991

Decision rule, reject null hypothesis if X2 ? 5.991. Otherwise, fail to reject the null hypothesis.

At a=0.05, X2 = 7.85 ? 5.991. Thus, we reject the null hypothesis.

We are 95% confident that there are significant differences among the three books.

Chapter 3: Research Design

3.1 Introduction

The intent of the survey is to look into the phenomenon of hooky and so to qualify the nature and associated factors, to guarantee appropriate direction thereof. It is envisaged that, with more penetration, effectual intercession schemes can be implemented. Furthermore, secondary -education decision-makers may take consequences into history when school-attendance policies are reviewed.

The reappraisal of the literature presented in the predating chapters reveals that secondary school scholars continue to play awol and lose the educational chances provided by mandatory school ordinances. Learners who play awol limit their ain opportunities of geting the necessary accomplishments to fix themselves for future employment. We besides looked at the different types of hooky, insouciant factors and assorted attacks that have been used to cut down hooky. This chapter describes the manner the empirical survey is planned and conducted, and will concentrate on the undermentioned facets:

• The research inquiries ;
• The research method ;

3.2 Research Questions

The research worker together with the community of educationists are concerned about the fact that scholars continue to remain off from school by either losing the whole twenty-four hours of school or by losing certain lessons without permission from the school governments and parents. Students who play awol licking the purposes of the vision of the educational system which is based on fixing scholars for effectual citizenship and employability. The follow are the chief research inquiries that will be confronted in this survey.

• What is the extent and grade of hooky in footings of the frequence and figure of learns involved?
• What are the forms, type or nature of hooky?
• Where make hooky players travel when non at school or in category?
• What steps are used to supervise and pull off hooky?

3.3. The research instruments

3.3.1. The Questionnaire

To reply the research inquiries presented above, information was collected by agencies of a questionnaire ( See Appendix 1 ) . Using a questionnaire is appropriate for a assortment of grounds, viz. because it is economical, it ensures namelessness, peculiarly when covering with a big sample and it helps in developing a general image to a phenomenon within a community sing similar conditions ( Nardi, 2003 ; Punch, 1998 ) . Upon careful consideration of bing questionnaires from the literature, the research worker has decided to roll up a new one for the current survey. The points that are included in this questionnaire are based on facts that are likely to lend to researching the phenomenon of hooky and are derived from the literature reappraisal. Some of these points are determined by the research inquiries set for this survey.

The questionnaire employed for this first portion of the survey follows a quantitative attack. The determination to follow this design helps to research grounds of absenteeism from pupils ‘ positions, chiefly through the followers:

• Absenteeism against gender differences ;
• Absenteeism due to age and signifier ;
• Absenteeism due to school background ;
• Absenteeism with no ground ;
• Absenteeism due to household constructions ;
• Absenteeism related to repeated primary category ;

3.3.2. Pilot survey

Two processs were carried out during the pre-testing stage of the questionnaire. Harmonizing to Cooper and Schinder ( 2003 ) , the research worker may trust on experts when flying the instrument to place alterations that can be made with confusing points. Experts and co-workers included a caput of school and my supervisor who are experienced in research were heartily requested to analyze the questionnaire to look into whether there were any points that needed to be changed or rephrased, every bit good the rightness of the clip set for finishing the questionnaire. The following process involved completion of the questionnaire by a sample of 20 Form 1 pupils and 20 Form 2 pupils non included the sample. The points in the questionnaire were hence considered to be satisfactory in footings of both diction and format.

3.3.3. Validity and Reliability

Cogency refers to the extent to which an instrument measures what is it is supposed to mensurate. In order to set up its cogency the questionnaire was given to experts to find content and face cogency. Harmonizing to Johnson and Christensen ( 2004 ) , content cogency is a judgmental act where experts check whether the points represent the concept which is being studied every bit good as the diction, arranging and marking of the instruments. On the other manus, face cogency refers to the extend to which the respondents will comprehend the instruments as being valid to prove what it is suppose to prove ( Black, 1999 ) .

The extent to which the instrument will supply the same consequences on subsequent disposal known as dependability was statistically obtained. The Cronbach Alpha correlativity expression was used to cipher dependability. The value obtained is 0.83, which indicates that the dependability of the instrument is satisfactory.

3.4 Sampling

The focal point of choice of participants for this survey centres on pupils and educational professionals in schools. For the quantitative survey, the mark population is Form 1 and Form 2 pupils in the secondary school degree. Schools identified for this research include State secondary schools and Church schools. State secondary schools include two types of schools, viz. the Junior Lyceum and the Area secondary school pupils. In the coming of the educational reform presently being undertaken in the Maltese educational system, a new construction of colleges is integrating both Junior Lyceum and Area secondary school pupils into one school. Data for this survey is collected from presently amalgamated schools and non-amalgamated schools.

For the qualitative survey of this research, a focal point group with a figure of educational professionals is carried out. Participants for the focal point group include ; capable instructors, PSD instructors, Guidance instructors and one Young person Worker. The qualitative survey besides includes two semi-structured in-depth interviews with one Head of School whose school has besides been targeted for the quantitative survey every bit good as a Guidance instructor from the Guidance Unit in the province educational sector.

3.4.1 The Quantitative Study

Six different schools have been selected at random, integrating two Junior Lyceum schools, two Area Secondary schools and two Church schools. An mean equal sample of respondents was collected from each class of schools and is including a balanced sample from male childs and misss schools.

The study questionnaire was administered to Form 1 and Form 2 pupils of the three school classs. The disposal of the study was carried out after reception of blessing both by the Planning and Development Department within the Directorate for Quality and Standards in Education ( DQSE ) every bit good as by the schools targeted for informations aggregation. The existent disposal of the study was carried out in coaction with the school disposals and was chiefly distributed through the support of the Personal and Social Development ( PSD ) instructors who administrated the study and collected the duly filled questionnaires. The research worker finally made agreements with the school disposals to roll up the studies. The research worker besides engaged in informations aggregation processs to roll up informations from schools within her range.

The questionnaire consisted of three chief subdivisions, including ( I ) demographics, ( two ) forms, type and nature of hooky and ( three ) policies undertaken at a school degree to battle hooky and school absenteeism. Following the necessary clean-up of unsatisfactorily filled questionnaires, the entire figure of questionnaires employed for analysis consisted of 1000 to the full returned studies.

3.4.2 The Qualitative Study

The qualitative survey incorporates two research designs. The first will be a focal point group and the 2nd will affect semi-structured in-depth interviews. The focal point group is intended to roll up informations from educational professionals on the manifestation and policies adopted across the three school classs on hooky and school absenteeism. Identified participants for this focal point group includes capable instructors, P.S.D. instructors, counsel instructors and one young person worker who is employed on a parttime footing by the Directorate of Educational Services. The interview agenda for the focal point group is here presented in Appendix 2.

The semi-structured in-depth interviews were carried out with a Head of School and a Counselor from the counsel and reding unit. The interview agenda for these interviews was developed from the literature reappraisal carried out in chapter 2, the informations obtained from the quantitative survey and the feedback obtained from the focal point group. These interviews are intended to endorse up the quantitative analysis and to supply extra penetration on current patterns and schemes adopted to battle school hooky and absenteeism ( See Appendix 3 ) .

3.5 Restrictions

The initial program was to include all pupils registered for Form1 and Form2 categories in the identified schools. A little figure of pupils in each category were regarded as absent at the clip of informations aggregation. When using for the relevant permissions, the Research Planning and Development section clearly indicated the research worker to curtail the research to a lower limit of pupils, instructors, decision makers, schools and to avoid any waste of clip during the visits to schools. As a effect to the limitations made upon the research worker, a purposive sample of participants undertook the procedure of informations aggregation for this survey.

3.6 Datas Processing

The nature of the information here being investigated reflects features in the general population that should non convey out any differences between males and females on behaviors of hooky and school absenteeism. In this respect the statistical analysis will use the non parametric step of rating viz. Chi square. Any ascertained differences will function to accept or reject the void hypotheses on these parametric quantities. Any important differences ensuing from this analysis would assist to accept the alternate hypotheses from the sample of participants in this survey, hence bring forthing evidences for accepting the alternate hypotheses.

All the informations shall be analysed utilizing the Statistical bundle for the Social Sciences ( SPSS ) version 17.

3.7 Decisions

This chapter provided an overview of how this survey was planned and conducted. This chapter has besides presented research inquiries for the current survey and identified the research instruments to roll up informations in this respect. The research design is constructed upon a quantitative survey and a qualitative survey with well-thought-of instruments. Elementss of cogency and dependability of the questionnaire and the pilot testing of this instrument present an first-class scenario for a valid information aggregation procedure. The participants for this survey include pupils every bit good as instructors and other professionals working in close contact with the phenomenon here under probe. This chapter presented a elaborate reappraisal of the trying method every bit good as the features of both surveies to be carried out. Finally restrictions in the research design are besides presented. The consequences of the empirical survey are presented in the following chapter.

Fuzzy TOPSIS method This is an approach based on the TOPSIS technique (Technique for Order Preference by Similarity to Ideal Solution) and the fuzzy set theory. The TOPSIS method is based on the concept that the optimum option has the least distance from the positive ideal solution. It is a linear weighting technique, which was first proposed, in its crisp version by Chen and Hwang(1992), with reference to Hwang and Yoon(1981).

Since then, this method has been widely adopted to solve MCDM problems in many different fields. Because decision information is uncertain instead of certain in most environments, further extension for group decision making problems under fuzzy environment was published by Cheng(2000),known as Fuzzy TOPSIS. The selection of the third-party provider is a typical MCDM problem. In this method firstly we screen out providers that have not minimal qualifications by the selection criteria.

Then closeness coefficient of contractors to each proposal will be computed by Fuzzy TOPSIS method and finally these coefficients as successful indicators for each provider will be fed in to a linear programming to select most profitable projects and providers with respect to the constraints. The stages are described blow: Stage1: Eliminate contractors that haven’t minimal qualifications. For the purpose of analysis, selection criteria need to be rationally selected at first. There are a lot of researches with respect to the decision criteria for evaluating the supplier.

Such as the study of Dickson(1966), Ellram (1990),Weber et al. (1991), ,Grupe (1997), and Akomode et al. (1998). According to an empirical survey, the top four selection criteria are responsiveness to service requirements, quality of management, track record of ethical importance, and ability to provide value-added services. The less important selection criteria are listed in a descending order as below: low cost, specific channel expertise, knowledge of market, personal relationship with key contacts, willingness to assume risk, investment in state-of- art technologies, size of firm, and national market coverage.

Keeping the outcomes of the supplier selection literature review as a guideline, we derived the relevant factors to evaluate in the provider selection process based on the outsourcing view. However selection of criteria is totally industry specific and based on each case and the criteria are changed and replaced. Then opinions of decision makers on criteria were aggregated and weights of all criteria have been calculated by organizing the expert meeting. Meanwhile, the outcomes of the supplier selection literature review should be kept as a guideline.

Stage2: Computing closeness coefficient (CC) for each project by fuzzy TOPSIS method So after we have obtained the important evaluation criteria and the qualified provider candidates to form the MCDM problem,the ranking of the shortlisted vendor providers will be done using the fuzzy TOPSIS approach. First,choose the appropriate linguistic variables for the importance weight of the criteria ,asses the importance of each contractor in each project with respect to each criterion by DM, using linguistic variables.

Convert these evaluation into triangular fuzzy numbers with fuzzy weight for each criterion. Fuzzy weight wj of criterion C j are obtained with regard to DM’s opinions. Then the importance of the criteria and the rating of alternatives with respect to each criterion and the aggregated rating Xij under criteria C j can be calculated as: Wj=1K[Wj1+Wj2+…+Wjk] xij=1K[xij1+xij2+…+xijk] Wjk is the importance weight of the kth decision maker. xijk is the rating of the kth decision maker. Construct the normalized fuzzy decision matrix.

If we describe the linguistic variables by triangular fuzzy numbers, xij=(aij,bij,cij) and wij=(wj1,wj2,wj3)then we can get the fuzzy decision matrix denoted by R, and R= R=[rij]m? n. rij=(aijcj,bijcj,cijcj) rij=(aj-aij,aj-bij,aj-cij) Next, the weighted normalized fuzzy decision matrix is constructed by : V=[vij]m? n, i=1,2,…,m j=1,2,…,n Where vij=rij(. )wj After all of these analysis and calculation ,a positive-ideal solution (PIS, A+) and a fuzzy negative-ideal solution (NIS,A-) as the criterion are chosen.

The best alternative solution should be the closest to the Positive Ideal Solution (PIS) and the farthest from the Negative Ideal Solution (NIS). A+=(v1*,v2*,…,vn*) A-=(v1-,v2-,…,vn-) vj*=1,1,1 vj-=0,0,0 Calculate the total distance of each components from the fuzzy positive ideal and negative ideal: ? If A and B are two fuzzy numbers as follows, distance between these fuzzy numbers is calculated by equation below: A=(a1,b1,c1) B=(a2,b2,c2) Equation : DA,B=13[a2-a12+b2-b12+c2-c12]

Given the above description on how to calculate the distance between fuzzy numbers, the distance of components from positive and negative ideas can be derived respectively as: di*=j=1nd(vij,vj*), i=1,2,…,m di-=j=1nd(vij,vj-), i=1,2,…,m In the end,the relative closeness coefficient (CC)of each contractor-project in each criterion can be calculated as: CCi=di*di-+di+, i=1,2,…,m Stage3: Selecting the best projects and related contractors Select the best projects and related contractors by ranking options based on the descending cci.

An alternative with index cci approaching 1 indicates that the alternative is close to the fuzzy positive ideal reference point and far from the fuzzy negative ideal reference point. A large value of closeness index indicates a good performance of the alternative. A case study: The proposed methodology for supplier selection problem, composed of TOPSIS method, consists of three Steps: (1) Identify the criteria to be used in the model; (2) weigh the criteria by using expert views; (3) evaluation of alternatives with TOPSIS and determination of the final rank.

The case is that of a major company operating in the dairy products field. In the first phase, the project team operated mainly through roundtable discussions on developing their main selection criteria. After identity the criteria attributed under consideration, five alternatives suppliers are written in the list. There are several criteria need to be considered, and each vendor’s information under each criteria are collected, calculating each vendor’s overall rating weight, shown in Table 2. (Mohammad Saeed Zaeri,2010) Finally, the closeness coefficient was calculated to rank alternatives.

The results obtained are shown in Table 4: (Mohammad Saeed Zaeri,2010) The order of rating among those vendors is Supplier3;gt; Supplier 4;gt; Supplier 1;gt; Supplier2;gt;Supplier5, the best vendor would be Supplier3. To conclude, the TOPSIS method had several advantages. First, TOPSIS makes it possible to appraise the distances of each candidate from the positive and negative ideal solutions. Second, it allows the straight linguistic definition of weights and ratings under each criterion, without the need of cumbersome pairwise comparisons and the risk of inconsistencies.

It evaluates the projects and each provider more precisely by expert decision makers in each stage of the whole process. Moreover, the method is very easy to understand and to implement. All these issues are of fundamental importance for a direct field implementation of the methodology by logistics practitioners. However TOPSIS is proved to be insensitive to the number of alternatives and has its worst performance only in case of very limited number of criteria. In order to apply fuzzy TOPSIS to a MCDM problem, selection criteria have to be monotonic.

“The study of man contains a greater variety of intellectual styles than any other area of cultural endeavor. How different social scientists go about their work, and what they aim t accomplish by it, often do not seem to have a common denominator … Let us admit the case of our critics from the humanities and from the experimental sciences: Social science as a whole is both intellectually and morally confused. And what is called sociology is very much in the middle of this confusion. ” Wright Mills Images of Man

Abstract

The quest for knowledge has always been at the forefront of societies mind.

What makes us tick as a society or an individual, what circumstances have to come about to lead to different phenomena to occur? Sociologists, psychologists, philosophers and social scientists have spent eons of time pondering on these questions. Research is the way in which these questions may be answered, but the question remains, as to what type of research leads us to the right answer or, if there is a right answer, what is the one true answer? If different research methods produce different answers, which is the right, the true answer and if we find it does this render all the other answers null or wrong?

These are some of the questions that I will be asking in this paper through examining concepts such as the symbolic order in research, the role of emotions in research, the grouping together of different methodologies to create a clearer picture of the research and the importance of reflexivity during the research process.

• Keywords: emotions, symbolic order, reflexivity.

The word research originates from the late 16th century French word recerche, re (expressing intensive force) and cherchier to search.

It means the systematic investigation into and study of materials and sources in order to establish facts and reach new conclusions. In this essay I will be focusing on qualitive research methods, examining some of the problems that may be encountered when conducting social research and how these problems may be overcome and used to advantage. Qualitative research takes an interpretive, naturalistic approach to its subject matter; qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings that people bring to them.

This process begins by understanding that there are a variety of ways of making sense of the world and therefore focuses on discovering the meanings that are seen by those who are being researched, to better understand their view of the world. The methodology used in research will vary in accordance with the research being conducted, this can be limiting if a type of methodology is decided on and rigidly adhered to throughout the research leaving no room for reconsideration or change of view.

Different Methodological Approaches

The manner in which sociologists study society varies greatly between individual sociologists.

There are many reasons for these varying views such as backgrounds, culture, family influences, religion and experiences with these experiences leading them to come to certain conclusions about certain situations. For this reason it is important not to rely on one type of sociological perspective which may constrict the researcher in the researching of certain phenomena. In research however objectively the reality of the social world was approached, its meaning was never self evident but always subject to interpretation with this interpretation being subject to the researchers biases formed out of the afore mentioned factors.

Some of the different methods of researching or research style are Positivism which means scientific; positivists would argue that it is possible and desirable to study social behavior in ways similar to those used by natural scientists when studying the natural world. The interpretive approach to research has been gaining attention in recent years as an alternative to the more traditional positivist approach (Lee 342). Lee describes the interpretive approach as “such procedures as those associated with ethnography, hermeneutics, phenomenology and case studies. By the positivist he refers to inferential statistics, hypothesis testing, mathematical analysis and experimental and quasi experimental design. Ethnography is a qualitative research method often used in the social sciences, particularly in anthropology and in sociology. It is often employed for gathering empirical data on human societies/cultures. Data collection is often done through participant observation, interviews, questionnaires, etc.

Ethnography aims to describe the nature of those who are studied (i. e. to describe a people, an ethnos) through writing. In the biological sciences, this type of study might be called a “field study” or a “case report,” both of which are used as common synonyms for “ethnography. Lee states that the difference between positivist and interpretive approaches has been described as objective versus subjective (Burrell and Morgan 1979), outsider versus insider (Evered and Louis 1981), quantitive versus qualitive (Van Mannen 1979) and etic versus emic (Morey and Luthans 1984).

In literature it may seem that these 2 methods of research are opposed and irreconcilable and there is some concern over what Morey and Luthans call the “widening gap between the two major orientations to organizational research” (1984, 84). Lee puts forward the idea of joining the two methodologies together as he argues that they both have something to offer the researcher. He devised a framework called three levels of understanding.

The first level belongs to the observed human subjects, this consists of common sense and meanings which are true for these subjects and how they see themselves, which give rise to the behavior that they manifest in socially constructed settings. The second belongs to the observing organizational researcher. This understanding according to Lee is the researchers reading and interpretation of the first level, common sense understanding where the researcher may use concepts such as subjective interpretation, the circle or thick description.

The third level of understanding also belongs to the researcher. This understanding is one that the researcher creates and tests in order to explain the empirical reality that he is investigating. This explanation is called scientific theory is made up of constructs that belong only to the observing researcher. This explanation consists of formal positions that typically posit the existence of unobservable entities such as social structure, issues that may attempt to account for the influence of certain factors of which the observed subjects may not even be aware.

The above diagram shoes the flow of ideas and understanding between the three levels of understanding and the relevance of the two methods of research in question. This illustrated the importance of varying the methods of research used, to create a legitimate piece of research work it is vital to come at the work from different angles rather than taking a blinkered approach. This is vital all there can be no definite knowledge in research as there are so many variables and researchers take the research on for so many different reasons with so many different worldviews.

Identifying applicable research strategies is almost as difficult as methodologies tend to differ according to the various factors found within the desired outcome. Yet methods cannot be orchestrated to generate this outcome from the data, but merely facilitate its collection and synthesis. Any successful research methodology does not, therefore, create knowledge, but rather is an applicable strategy for identifying and processing the information which exists.

Hathaway (1995) stresses that there are decisions embedded within the creation and conduct of research methodologies that are generated both within the research setting and within the perceptions of the researcher. The concept of an unbiased methodology is thus inherently impossible: Are we creatures of reason and logic? Or are we better characterized as the victims of unconscious drives, forces and emotions? Does the different language we use really make such a difference in what we have to say? Are we saying something better and more academic if it is considered almost too technical for the reader to understand?

Are texts considered more valid if they are difficult to understand and read? Are these technical essays and writings elitist, written by elitist academics just to be appreciated by like minded and like educated individuals? Why publish research ideas that are inaccessible to society? All researchers come to the experimentation process with preconceived opinions of how and why the research process should transpire. “When one chooses a particular research approach, one makes certain assumptions concerning knowledge, reality, and the researcher’s role.

These assumptions shape the research Endeavour, from the methodology employed to the type of questions asked. ” (Hathaway 1995). So how do we carry out the most informed research possible? It is important not to take a ‘sat nav’ approach to the research, asking a question that you already know the answer to and not be prepared to change course along the way, the research process is the information that the researcher finds along the pathway to the research, the phenomena the researcher encounters along the pathway is as relevant as the final conclusion and it is vital to include this in the research process.

If the research question is not working is it preferable to change the question or come at the research from a different angle rather than trying to fit your research question into every area of the study? Reynolds argues that the methodologist turns research technician, in spite of himself, and becomes an aimless itinerant, moving in whatever direction his research techniques summon him, studying changing patterns of voting because these are readily accessible to his techniques rather than the workings of political institutions and organizations for which he has not evolved satisfying techniques of investigation. Reynolds 190). In my own research on texting differences between adults and teens I will be using field work which will consist of focus groups with informal questioning and conversation, individual interviews and data analysis in the form of analyzing a number of text interactions in both focus groups.

Bourdieu and the Importance of Reflexivity in Social Research

Is knowledge independent of the situation of the knower, or a product of it? Bourdieu stresses the importance in reflexivity while conducting social research.

The sociologist must at all times be aware of their own habitus, their position of thought and in life and how bringing this to research will affect the research outcome. According to Bourdieu it is impossible for our objectivity to remain unbiased and unprejudiced due to our preconceived habitus. It is only by maintaining such a continual vigilance that the sociologists can spot themselves in the act of importing their own biases into their work. Reflexivity is, therefore, a kind of additional stage in the scientific epistemology.

If there is a single feature that makes Bourdieu stand out in the landscape of contemporary social theory’, wrote Loic J. D. Wacquant (1992: 36), ‘it is his signature obsession with reflexivity. ’ For Bourdieu, reflexivity is an epistemological principle which advises sociologists, as ‘objectifying subjects’, to turn their objectifying gaze upon themselves and become aware of the hidden assumptions that structure their research. Without this reflexive move, sociology cannot escape the ‘fallacies of scholasticism’ and loses its chances to provide a truly scientific analysis of the social world. Reflexivity requires an awareness of the researcher’s contribution to the construction of meanings throughout the research process, and an acknowledgment of the impossibility of remaining ‘outside of’ one’s subject matter while conducting research. Reflexivity then, urges us “to explore the ways in which a researcher’s involvement with a particular study influences, acts upon and informs such research. ” (Nightingale and Cromby, 1999, p. 228). In the rush of interest in qualitative research in the past 15 years, few topics have developed as broad a consensus as the relevance of analytic “reflexivity. ” (Macbeth 2001).

Macbeth argues that contemporary expressions of reflexivity have attachments to critical theory, standpoint theory, textual deconstruction and sociologies and anthropologies of knowledge and power and agency with theorists such as Bourdieu and Wacquant at the forefront of this type of thinking. Bourdieu has problematised social research in relation to his concept of habitus stating that the researcher must at all times be aware of his habitus,(prevailing and long learned personal norms and biases, formed over a lifetime) and take steps to acknowledge this habitus by looking back on himself and his research with a critical eye.

The postmodern condition is such that there are no certainties in social research as norms and values become intertwined, identities and culture intermingle and clash as do gender and sexualities, power is gained and lost through means of popularity alone and social researchers can only strive to explore every avenue of their research subject reflexively in the quest for knowledge and answers.

In research this reflexivity can be put into two categories, personal reflexivity, which involves the researcher acknowledging their own habitus and how this is affecting their research and in turn affecting the researcher carrying out the research. The second is epistemological reflexivity which requires us to ask questions of the research such as: “How has the research question defined and limited what can be ‘found? ‘ How have the design of the study and the method of analysis ‘constructed’ the data and the findings?

How could the research question have been investigated differently? To what extent would this have given rise to a different understanding of the phenomenon under investigation? Thus, epistemological reflexivity encourages us to reflect upon the assumptions (about the world, about knowledge) that we have made in the course of the research, and it helps us to think about the implications of such assumptions for the research and its findings. ” (Willig, 2001).

The Use of Emotion in Social Research

Williams and Bendelow (1996), map the field of sociology of emotions onto the concerns of sociology: “emotions have fundamental implications for a range of pertinent sociological themes and issues including social action, agency and identity; social structure; gender, sexuality and intimacy; the embodiment of emotions across the life-course (from childhood to old age); health and illness; and the social organization of emotions in the workplace (formal and informal). Emotions play an important part in the field at a number of levels. It is important to realize that the researcher’s identity and experiences shape the ideas with which they go into the field, their political and ideological stance, and there is an analytic cost if this interplay of person and research is not taken into consideration. The researcher takes assumptions and emotions into and generates emotions in the field about the researched.

Kleinman and Copp (1993) suggest that if a researcher experiences negative emotions about their participants they would prefer to ignore, or repress those feelings, since to admit them might constitute a threat to their professional and personal identity. But these can be the very feelings (anger and disappointment perhaps) that could help the researcher to understand their own assumptions and their participants. It is clear to me that emotions are very important in fieldwork, both those of the participants and of the researchers.

The researcher’s emotions can have effects at the personal and professional levels, in relation to their understanding of their self and identity, and their capacity to perform in a fashion that they would themselves regard as professional, and these effects can be long term. A considerable amount of emotion work is called for in qualitative research, and often the dangers consequent on this are not recognized. In some instances researchers have been made quite ill (physically or emotionally) through their experiences of denying, ignoring or managing emotions.

The emotions experienced by respondents in the field are data and need to be drawn into analysis and interpretation. It has been suggested here that emotions are important in the production of knowledge from a number of perspectives. In most cases, despite some unpleasant experiences, researchers value the extra power in understanding, analysis and interpretation that the emotions they experience in the field can bring to the research. In his article Hidden Ethnography: Crossing emotional Borders in Qualitive Accounts of Young People’s Lives. Shane Blackman concludes that different ethnographic episodes show how powerful feelings of emotions from love to hate grip both the researcher and the researched. He states that his fieldwork consisted of constant negotiation and respect with participants who allowed him access to their public and private spaces. He advises that to advance more open, reflexive approaches that explain how research is conducted and written, sociology needs greater disciplinary understanding and recognition of the real challenges and opportunities faced by qualitive research, which demands emotion.

The Symbolic Order in Social Research. “The “Symbolic Order” achieved its currency in Anglo-Saxon human sciences by way of Jacques Lacan’s psychoanalytic theory but originated in Claude Levi-Strauss’s Les structures elementaires de la parente (1949) [translated into English as Elementary Structures of Kinship, 1969] which used the term to group the many different codes which constitute human societies—from social identities and kinship relations to cooking and feasting rituals and religious observances—in short all cultural practices and inscriptions, whatever their language.

Levi-Strauss showed that patterns we can observe in one level are invariably linked to and determined by similar patterns in other levels”. (Clark 2004) How important is the symbolic order in social research? There are many factors to take into account when discussing the symbolic order in relation to research. Gusfield and Michalowicz argue that in recent years, sociologists and anthropologists have conducted “significant studies of modern life using concepts and perspectives derived from symbolic anthropology.

Among anthropologists words like ritual, myth, ceremony and symbolism are central to the study of social life in primitive societies. In contemporary society they have been peripheral terms and the activities they denote have not usually been studied in modern societies. ” (Gusfield and Michalowicz 1984). The symbolic is of huge importance in social research and cannot be separated from it. When researching we must ask, what is happening here? Recognizing the potentially multiple responses to this question illuminates the way in which meaning is mediated by cultural categories and structures of thought.

This awareness of the social construction of reality, which Richard Brown calls symbolic realism (Brown 1977), implies that any segment of human, social activity can be experienced in different and in multiple ways by diverse actors and observers. David Blacker in his thesis argues that for Gadamer, all understanding — whether of a text or of another person — is interpretive. This means is that, whatever else it is and does, understanding moves in what Heidegger called a “hermeneutic circle. ” This circle is productive of meaning.

To generate meaning from a text, for example, one must always move around from whole to part and back again. The “whole” may be the language in which the text was written, the literary tradition to which it belongs, its historical period, the life circumstances of its author, and so on. This “whole,” then, provides the backdrop against which one gives significance to the “part,” e. g. , the particular words comprising the text, the individual work in question or the specific period of the author’s life. A helpful analogy is with understanding an ambiguous word within a sentence.

If the meaning of the word itself is not immediately obvious, one must find it in its larger context. The newly appreciated meaning of the part (the word) then alters to a degree the meaning of the whole (the sentence). One never escapes “outside” this whole-part circuit — even the dictionary only relates words to other words. In my own research on ‘the difference in meaning of texting between teens and adults’ the symbolic order plays a large part. The mobile phone will mean different things to these two groups and these issues must be taken into account when formulating the research.

Mobile telephone has been widely adopted by many people in society. As it integrates into daily life, it alters the way people communicate, identify their personalities and relate to others in social system. It affects socio-economic structures as well as individual life. Mobile telephone enables accessibility, emancipation, security and micro-coordination and serves as a symbol of prestige, pride and self-identity. The aim of this study is to explore the symbolic factors influencing the use of mobile telephone among teens and dults where in the case of adults the phone may be vital for communication; the teen may find it impossible to function socially without the use of the phone and the texting facility. Conclusion There is no way of determining a sure path for arriving at sociological knowledge; there is unlikely to be, just over the horizon, a new approach, paradigm or perspective to rescue us from the intellectual difficulties involved in a sociological theorizing which can give us a better understanding of our social world. As researchers we must be aware of our limitations in the social world in so much that we cannot really promise to theorize in a way that explains everything. This is not possible in life as there are too many different collective and individual ideas that are thousands of years in formation. In social research these variables and ideas must be acknowledged and given importance within the research area and with their relevance acknowledged the researcher may move on to the findings of her own particular studies.

Karl Mannheim answers critics in letter to the members of a seminar on the sociology of knowledge, by stating that “if there are contradictions and inconsistencies in my paper this is, I think, not so much due to the fact that I have over looked them but because I make a point of developing a theme to its end even if it contradicts some other statements. I use this method because I think that in this marginal field of human knowledge we should not conceal the inconsistencies, so to speak covering up the wounds, but our duty is to show the sore spots in human thinking at its present stage.

In a simple empirical investigation or straightforward logical argument, contradictions are mistakes; but when the task is to show that our whole thought system in its various parts leads to inconsistencies, these inconsistencies are the thorn in the flesh from which we have to start. The inconsistencies in our whole outlook, which in my presentation only become more visible, are due to the fact that we have two approaches which move on a different plane. (Mannheim in Reynolds 1970) David Hume held that we can never be absolutely sure that what we know is true. (Bernard 2006).

He argues that we come to understand what is true from what we are exposed to. This reiterates the fact that research is personal even when we try our best to avoid this being the case. We can never be sure according to Hume what we know is true, Humes brand of skepticism is a fundamental principle of social science according to Bernard, “the scientific method, as it is understood today, involves making improvements in what we know, edging towards the truth, but never quiet getting there and always being ready to have yesterday’s truths overturned by today’s empirical findings.. ” (Bernard).

In the social sciences we can see sociologists, philosophers and social psychologists such as Michael Foucault, Fredriech Nietzsche, Pierrie Bourdieu and others changing their views on subjects and seemingly contradicting themselves but I would consider that this is paramount when conducting any type of research, as society evolves, technology changes and people become more individualized the world is changing rapidly so we as researchers must be open to change and not be afraid to re-examine our research and research motives to ensure that we are generating the most informed and comprehensible research possible.

In the case of Foucault , Tom Keenan argues that “these contradictions and paradoxes do serve a very important strategic purpose since they allow to articulate a critique of the juridical discourse on a theoretical level. Foucault’s work produces paradoxa since it struggles against doxa, it seeks to place in question orthodoxies of political thought and leftist critique. It is contradictory since it contradicts dominant forms of critique that itself functions as a constraint for imagining political alternatives (Keenan 1987)”.

References

1. Blacker, D. (1993). Article on Education as the Normative Dimension of Philosophical Hermeneutics. University of Illinois, Urbana-Champaign, USA.
2. Bourdieu, P & Wacquant (1992). An Invitation to Reflexive Sociology. University of Chicago Press, Chicago.
3. Burrell, G. , & Morgan, G. Sociological Paradigms and Organizational Analysis, Heinemann, 1979
4. Cuff. E. C, Sharrock. W. W, Francis. D. W (1998) Perspectives in Sociology. Fourth Edition.
5. Routledge, London. Clark, R. (2004) “The Symbolic Order”. The Literary Encyclopedia. March 2004.
6. Evered, R. , Louis, M. R. (1991), “Research perspectives”, in Craig
7. Smith, N. , Dainty, P. (Eds), The Management Research Book, Routledge, London
8. Gusfield. J & Michalowicz. J (1984). Secular Symbolism: Studies of Ritual, Ceremony and the Symbolic Order in Modern Life. Annual Reviews Inc 1084
9. Holland, J (2007) International Journal of Social Research Methodology. Volume 10 Issue 3. July 2007.
10. Keenan, T, (1987) The ‘Paradox’ of Knowledge and Power: Reading Foucault on a bias, in: Political Theory, Vol. 5, No. 1, 1987.
11. Kleinman, S. & Copp, M. A. (1993) Emotions and fieldwork.
12. Sage, Newbury Park, CA Macbeth, D. (2001). On “reflexivity” in qualitative research: Two readings, and a third. Qualitative Inquiry.
13. Morey, N. , and Luthans, F. (1984) “An Emic Perspective and Ethno Science Methods for Organizational Research,” Academy of Management Review (9:1), 1984.
14. Nightingale, D. & Cromby, J. (Eds) (1999). Social constructionist psychology: A critical analysis of theory and practice. Buckingham: Open University Press.
15. Reynolds, L & J (1970). The Sociology of Sociology. Analysis and Criticisim of the Thought, Research and Ethical Folkways of Sociology and its Practitioners. David McKay Company INC, New York.
16. Van Maanen, J, (1979). “Reclaiming Qualitative Methods for Organizational Research: A Preface,” Administrative Science Quarterly, Vol. 24
17. Williams, S. J. & Bendelow, G. A. (1996b) Emotions and ‘sociological imperialism’: A rejoinder to Craib.
18. Willig. C, (2001) Introducing Qualitative Research in Psychology (p. 10).

Research can be generally described as the systematic enquiry that seeks answers to a problem or the methodical study that seeks to prove a hypothesis which aims to refine existing knowledge and generate new knowledge. Consecutively, we encounter research in our everyday lives. In fact information and knowledge disseminates to all different platforms and is mostly only deduced from the results of a research. These research findings can be presented on various platforms such as the social media and the broadcasting media.

From the TV programs we watch, newspaper articles & books we read, reports we synthesize—we assess the information, make our own judgments, then decide our choices—yet although unaware of it, we ourselves would seek answers, confirmations, and validities from these research findings based on different considerations that must have taken influence into forming our conclusions; to name a few: what & how we understand the problem, the way the findings are presented, why the research was conducted, and how the findings relate to us. In short, the fact that we acquire knowledge and information and make our decisions from it illustrates that research is a cyclic pattern that we deal in our everyday lives, even if we’re not aware we’re doing it.

As Socrates have once said, “Life without inquiry is not worth living for a human being.” It is at this very mantra lies the essence of research. It is at the awareness of our own ignorance that stems the need to know, the need to inquire, that we question things which results in gathering knowledge. However, it must be significantly noted that the act of just gathering and confirming data is not considered research, as the data collection itself is what’s crucial in the research process. This further expands as to how acquiring knowledge is the most crucial part of the research process.

Research begins when we wonder—when we want to critically know something. It doesn’t mainly regard in knowing the (absolute) answers, but to rather increase our understanding and to provide solutions. Research provides the information and knowledge that helps us in solving problems and making decisions. This practically guides us in facing real-world problems, whether by carrying it out to further our knowledge (pure/basic research) or by applying pre-existing knowledge (applied research).

Although there are many ways of acquiring knowledge such as from our experiences and logical reasoning, the scientific method is the most sophisticated and reliable. All in all, the research process can be generally seen as a linear progress of identifying and formulating a problem (research topic), clarifying the problem or research topic (review of related literature) , clearly stating your question/s and hypothesis (statement of the problem) ,designing your research and planning strategies ,collecting data , analyzing the data by exploring relationships, and lastly ,drawing conclusions and acknowledging the limits of your research.

It is important to acknowledge that the process of research is not dependent on following this linear pattern as new ideas arise and practical problems are presented in the process. That’s why regardless of the route taken, we need to significantly be aware of the most effective research method to choose that can best provide information in developing our question/s.

Research excites me because it correlates directly to new discoveries and to the history. From the beginning till the end, it is an exciting adventure because you are directly involved to what you are doing and to what you want to know, and it is the most rewarding as you can never know what new knowledge or information you could gain in the end. Research is also the foundation of learning. Our everyday personal and professional experiences may lead us to identify or encounter a problem that we would like a solution or an answer—thus implicating the essence of research.

We gather, assess, apply, and renew all together our ideas as new knowledge and information is found. In history, research helps to explain the past events in relation to predict the future events. Research is also what makes sense of the world. Theoretically and/or practically, research is the main basis of the process of seeking answers from problems, whether relying on scientific principles and assessments or in different modes of other disciplines.

Mathematically and economically, it is also the practical basis of our decision-making in our lives. Theories and basic laws are also tied in research whether through different approaches (social research). Different discussions are evoked through research thus mainly influencing the basis of our knowledge and beliefs. We always learn because it presents new challenges and new results that provoke new discussions that follow new topics for new researches.

It’s also interesting how a research, in time, can be done simultaneously by different people which then can be presented in different contexts depending on its relevance and on how it correlates to the particular time and situation (scientific discoveries). It creates the framework of understanding that helps us to further understand the relationship of the existence of this world to the information and knowledge we acquire. These all illustrate how research is an evolving process—as new knowledge and information arises, we grow for the betterment of our world. Research then, as I abstractly conclude, is the essence of the world.

The chapter 2 of Research Methods of Business by Uma Sekaran speaks of scientific investigation detailing on the eight hallmarks of science and the limitations of scientific research in management along with the hypothetico-deductive method of research. The hallmarks or main distinguishing characteristics of scientific research can be the following .

1. Purposiveness: The research should have a purposive focus i. e. some definite purpose will be served after the research . Rigor means carefulness, scrupulousness and the degree of exactitude in research investigations good theoretical base and a sound methodological design will add rigor to a purposive study.
2. Testability: if a certain hypothesis gets developed through unstructured interview or library search, then the hypothesis can be tested by applying certain statistical tests to the data collected for the purpose.
3. Replicability: The results of the test of hypotheses should be supported again and again when same type of research is repeated in other similar circumstances. The researchers will gain confidence in the scientific nature of the research.
4. Precision and confidence: Precision refers to closeness of the findings to “reality” based on a sample. It reflects the degree of accuracy or exactitude of the results on the basis of the sample to what it really exists in the universe. Confidence refers to the probability that the estimations are correct.
5. Objectivity: The conclusions drawn through the interpretation of the results of data analysis should be objective i. e. they should be based on facts of the findings of the actual data. The more objective the interpretation of data , the more scientific the research investigation becomes.
6. Generalizability: This refers to the scope of applicability of the research findings in one organizational setting to other settings. The wider the range of applicability of the solutions generated by research, the ore useful the research is to the users.
7. Parsimony: Simplicity in explaining the phenomenon or the problem that occur and in generating solutions for the problems is always preferred to complex research frameworks.

In the management and behavioral areas , it is not possible to conduct investigations that are 100% scientific because of measurement and collection of data in the subjective areas like feelings, emotions, attitudes and perceptions. These problems occur whenever one tries to quantify human behavior. Thus , the eight hallmarks of science cannot be achieved in full . The deduction and induction processes are explained as follows Deduction: it is the process of arriving at a reasoned conclusion by logical generalization of a known fact. Induction is the process where a certain phenomenon is observed and then a conclusion is arrived at.

The seven step processes in hypothetico-deductive method are

1. Observation: It is the very first stage in which one senses that certain changes are occurring or some new behaviors , attitudes and feelings are surfacing. When the observed phenomenon are seen to have potentially important consequences , then one will proceed to preliminary information gathering.
2. Preliminary information gathering: Preliminary information gathering involves seeking of information in depth of what is observed. Through interviews and library search , the mass of information can be gathered.
3. Theory formulation: It is a step which attempts to integrate all information in a logical manner so that the factors responsible for the problem can be conceptualized and tested. The theoretical framework formulated is often guided by experience and intuition. Here the critical variables are examined as to their contribution or influence in explaining why the problem occurs and how it can be solved.
4. Hypothesizing: From the theorized network of associations among the variables, certain testable hypotheses or educated conjectures can be generated. The hypothesis thus generated is tested to determine of the statement is supported.
5. Further scientific data collection- After the development of the hypothesis, data with respect to each variable in the hypothesis need to be obtained.
6. Data analysis- The data gathered are statistically analyzed to see if the hypotheses that were generated have been supported.
7. Deduction – It is a process of arriving at conclusions by interpreting the meaning of the results of the data analysis.

Susan Fiske submitted an expert report on behalf of the plaintiffs proclaiming that “At Home Depot, the session-making criteria are decentralized, unspecified, vague, discretionary, not public, and not validated… ” And that such criteria “specifically perpetuate stereotypes” (Fiske, 1997, p. 24). The basis for class certification in EYE lawsuits often involves the presentation of adverse-impact statistics, expert testimony related to the appraisal system, and anecdotal evidence of discrimination.

Class certification substantially increases the defendants’ exposure to liability and the motivation to settle claims that may have little or no merit. This so-called in terror effect of ratification has been successful for plaintiffs who have been the beneficiaries of large out-of-court settlements against some of the largest U. S. Companies, which has in turn increased the rate of petitions for certification in other EYE cases (Babbles, 2002). The Coca-Cola and Ford Motor Company settlements are two recent examples.

The strategy on the part of the plaintiffs regarding performance appraisal in such cases is to foster an inference that the ambiguity in the performance-rating criteria either directly or indirectly caused the adverse impact and thus the illegal coordination in the personnel decisions. For example, Circuit City lost a Title VII case based at least to some extent on adverse-impact statistics regarding promotion decisions and expert criticism of their “highly subjective” appraisal system.

The plaintiff’s expert in this case rendered the opinion that among the “kinds of practices that could be expected to result in discrimination include… No weighting factors to performance dimensions for the appraisal process. ” (McKnight. V. Circuit City Stores Inc. , 1997, p. 3). The same “subjectivity theory’ is also proffered in disparate retirement cases of discrimination to buttress claims of intentional discrimination (Kane et al. , 1998). The Supreme Court held in Watson v. Fort Worth Bank and Trust (1988) that the plaintiff’s burden in establishing prima facie discrimination “… Goes beyond the need to show that there are statistical disparities in the employer’s work force; plaintiff must identify specific employment practices allegedly responsible for observed statistical disparities and prove causation. ” Since this 1988 decision, there has been a great increase in the use of statistics and expert testimony to argue a causal connection between the subjectivity in the appraisal process and deleterious outcomes to support individual claims of discrimination (Violation et al. 2002; Emerson, 1997). The presentation of adverse-impact statistics along with expert opinion criticizing the subjectivity of the decision-making process and some anecdotal evidence of discrimination is also often the basis of successful petitions for class certification despite the Supreme Court’s emphasis on the “commonality’ and “predominance” requirements of Rule 23 of the Federal Rules of Civil Procedure in General Telephone v.

Falcon (1982) and Emcee Products, Inc. . Windsor (1997). The “commonality’ requirement stipulates that specific questions of law or fact must be common to members of a class Performance Appraisal Criterion Specificity and Discrimination 145 and that such questions must predominate over any questions affecting individual members.

Footnote 1 5 in General Telephone, often cited to support the “commonality’ argument for certification, states that “significant proof that an employer operated under a general policy of discrimination conceivably could Justify a class of both applicants and employees if the discrimination manifested itself in ring and promotion practices in the same general fashion, such as through entirely subjective decision making processes. Expert testimony to support plaintiffs’ claims espouses the theory that defective performance appraisal systems foster the common “entirely subjective” processes necessary for certification. “Subjectivity’ in performance appraisal is obviously a matter of degree. Truly objective performance criteria do exist, usually in the form of independently counted units of tangible output produced in specified periods of time. Their focus is almost exclusively on the quantity of work performed.

For the majority of Jobs, however, the output of work is either intangible or incremental, or the quality of the work is not easily measured in discrete units. Qualitative dimensions of work performance can be very difficult to measure in truly objective terms. By their very nature, these qualitative dimensions must be assessed using subjective criteria. Therefore, most “more objective” performance appraisals, in the sense plaintiffs’ attorneys refer to such, simply use qualitative evaluation, without otherwise materially changing the pattern of observation, Judging, and reporting by the supervisor.

The implication of the “subjectivity’ theory espoused by plaintiffs in discrimination cases is that if the company had done appraisal using more specific, precise, or objective criteria and/or had employed other “best practices” related to performance appraisal, that the discrimination, operationally defined by the adverse-impact statistics, would not have occurred. As the theory goes, at least a less disproportionate number of decisions deleterious to the plaintiffs would have been made.

For example, the theory may imply that the 80% rule would not have been violated or that a greater proportion of retorted class members would have been promoted were it not for the relatively more subjective criteria used to make decisions about personnel. This was precisely the argument made by the plaintiffs in the recently settled race discrimination case against Coca-Cola and the gender discrimination case against Home Depot. It is clear that such testimony has an impact on the outcome of EYE cases and out-factor settlements (e. G. Bernardino ; Tyler, 2001; Bernardino ; Socio, 1988; Socio ; Bernardino, 1980; Field ; Holey, 1982; Linebacker ; Post, 2000; Malls, 1998; McElroy ; Beck-Dudley, 1991; Ritchie ; Lib, 1994). The problem, however, is that the limited, published research relevant to the general expert theme that the rating instrument or the specificity of the criterion measures is related to negative outcomes for protected class members does not support this definitive view (Powell ; Butterflies, 1997). Bernardino et al. (1995) found little research supporting the notion that criterion specificity will result in greater accuracy and less rating bias (e. . , Walden ; Viola, 1986) with consequent reductions in adverse impact. It is not unreasonable to assume that a racist, sexist, or ageist rater is more likely to manifest these indecencies in personnel decisions when the performance criteria that are supposed to be the basis of these decisions are relatively more ambiguous. Substantial research effort has been directed at the specific issue of isolating the effect of recessed rater bias, and most of it has concluded that raters tend to rate persons of their own race higher.

Krieger and Ford’s (1985) meta-analysis summarized a large amount of the research on this issue, as did Pulaski, White, People, and Barman’s largesse study of military ratings (1989) and the re-analysis of the same data by People, Campbell, Pulaski, and Barman (1992). The analyses of the military ratings revealed significant interactions between rater and rate race when variance in narrating measures was removed from the ratings. Jackets and Dubos (1991) challenged these results based on re-analysis of the Pu- … Cost “more objective” performance appraisals, in the sense plaintiffs’ attorneys refer to such, simply use some form of anchoring or standards to provide additional structure to the act of qualitative evaluation, without otherwise materially changing the pattern of observation, Judging, and reporting by the supervisor. 46 The purpose of this research was to investigate the impact of performance criterion specificity on adverse impact resulting from performance appraisal. Lakes et al. Data to isolate supervisor-subordinate ratings from peer ratings, and with the inclusion of additional data.

Their conclusion when considering only supervisor ratings was “Black rates consistently received lower ratings than White rates from both White and Black raters. Also notable is the fact that White and Black raters differed very little in their ratings of White rates but differed much more in their ratings of Black rates. Jackets and Dubos were also able to identify a substantial number of instances in which the same individual was rated by both Black and White raters, and therefore were able to conduct within-subject analyses.

These were significant in that their results were virtually identical to the analyses in which random assignment of raters had to be assumed. In all cases here, however, the rating format itself was constant, which must lead to the conclusion that any effect found was not due to the rating format. In fact, there has been little research that has directly assessed the extent to which rating content, format, or criterion pacifistic moderated the statistical relationship between rate race, gender, or age and personnel decisions.

In the only related materialness on the subject, Ford, Krieger, and Scotchman (1986) reported nearly identical correlations between race and performance for objective criteria (e. G. , absenteeism, productivity) and subjective criteria (e. G. , ratings). They concluded, “the relatively high degree of consistency in overall effect sizes found across multiple criterion measures suggests that the race effects found in subjective ratings cannot be solely attributed to rater bias” (p. 334). However, in their comparison of the effect sizes for subjective ratings versus objective indices of performance (e. . , productivity, customer complaints), significantly stronger race effects were found for the subjective ratings. Their meta-analysis does not compare differing subjective appraisal systems for race effects, which is the focus of the present work. Other research has actually concluded that the greatest differences between African-Americans and Whites occur on relatively more objective performance measures such as knowledge tests and work samples (Bernardino, 1984; People, Campbell, Pulaski, & Barman, 1992). Bernardino et al. 1995) found only four studies that specifically addressed the criterion issue using real performance appraisal data, all of which were small sample studies. No published study was located that made comparisons between performance appraisal systems for their effects on adverse impact. Hellman, Block, and Staccatos (1997) found a strong affirmative action “stigma” against women in situations in which the performance criteria were relatively more ambiguous and no such stigma when the performance criteria were clear and unambiguous. However, like many studies involving illustrations of various forms of rating bias (e. , Huber, 1989; Lenten, Mitchell, & Browning, 1983), this was not a study involving real performance appraisal data. Very limited information about the programmers’ performance to be rated and labels for some of the hypothetical performers as “hired through women/minority recruiting program. ” While strong effects were found for the “stigma” theory, because of the contrived nature of the study, the very limited performance information, and the labeling manipulation, we do not believe this study provides much support for the theory that greater criterion specificity will reduce or eradicate discrimination.

Field research is needed to assess the effects of appraisal characteristics and criterion specificity on protected class outcomes using administratively significant appraisal data. The purpose of this research was to investigate the impact of performance criterion specificity on adverse impact resulting from performance appraisal. Borrowing from the expert theme and the models presented by the EEOC and described below, our overall hypothesis is that greater appraisal criterion specificity will result in less adverse impact against protected class members in performance appraisal ratings.

We tested this hypothesis using a large database of performance appraisals from a state government. Our study sheds some light on this important issue using performance appraisal data from two very different appraisal systems that dif- 147 fear on criterion specificity: a less-specific category-based system with simple adjectival descriptions of performance levels versus a more specific standards-based Work Planning and Review (WAP) system where unique performance standards must be written for each rate and the rating criterion levels are carefully defined for he occupation.

Accepted definitions of adverse impact, such as the 80% rule, contemplate dichotomous decisions: the employee is hired or not hired, fired or not fired. However, performance appraisals tend to include intermediate gradations. This arises from the dual nature of the use of performance appraisals, which has been the subject of long discussion in that literature (e. G. Meyer, Kay, ; French, 1964). Performance appraisals are used for developmental feedback to employees, which typically requires finer gradations in measurement than a simple acceptable/ unacceptable.

But, they are also used as the basis and formal Justification for personnel actions-? and these are the binary decisions that may result in adverse impact. Receiving a lowermost-expected performance appraisal may be emotionally costly to the employee by itself, but it would not have a measurable economic impact and would not be actionable unless the employer changed some element of compensation or the employment relationship based on the results of that appraisal. Adverse impact in compensation level is another non-dichotomous variable. Current EEOC practice, as illustrated in Chapter 10 of the EEOC Compliance Manual (U.

S. Equal Employment Opportunity Commission, 2000), calls for the calculation of median median and those at or below the median, in order to dichotomize the data for conventional adverse-impact analysis. We applied the same partitioning strategy to the performance appraisal ratings under each of the rating types we investigated. Using this dichotomize outcome variable, we were then able to formulate hypotheses that are consistent with the usual adverse-impact measures, and also to hypothesize more complex relationships between demographics, rating format, and the outcome.

This dichotomizing as an additional benefit, in that it allows us to compare the results of ratings made using two very different scales of measurement in a single analysis. With the use of a single pass/fail criterion, it becomes possible to test for the interaction of rating format and race, gender, or age, and directly answer the question of whether or not the change to a more specific rating format offers advantages in the reduction of adverse impact in ratings.

The presence of a meaningful interaction between a basis for discrimination and type of rating would indicate, depending on the type of interaction, that there was a differential effect due to both rating type and the discriminatory effect. The absence of such an interaction would not preclude effects due solely to rater bias or rating format on rating levels, but there would be little reason to conclude that one was linked to the other. This is the central point of the expert witness arguments described above.

We hypothesize generally that the criterion specificity of the rating systems under consideration will moderate the effect of race, gender, or age on the achievement of above-median performance evaluation rating. Hypothesis 1: Ratings made under the Work Planning and Review (WAP) system will exhibit less adverse impact in performance evaluation on Black rates than ratings made under the category ratings system. Hypothesis 2: Ratings made under the Work Planning and Review (WAP) system will exhibit less adverse impact in performance evaluation on female rates than ratings made under the category ratings system.

Hypothesis 3: Ratings made under the Work Planning and Review (WAP) system will exhibit less adverse impact on rates aged 40 and older than ratings made under the category ratings system. Methods Sample We had access to the computerized records of performance appraisals for all employees We hypothesize generally that the criterion specificity of the rating systems under consideration will moderate the effect of race, gender, or age on the achievement of above-median performance evaluation rating. 48 The two appraisal systems under study here were a simple, pinpoint category’s rating format and a Work Planning and Review (WAP&R) system. Database, the state began a conversion, on an agency-by-agency basis, to a standardized, WAP&R type of performance appraisal process from its previous impel, category-based system. While extensive in number, the records provide only limited information. Besides the rating itself, we had available the date of the rating and demographic and identifying information on the rated person only.

The files did not contain identifying information on raters, so we were unable to examine rater- rate interactions across the transition. We selected eight occupational groups with the largest numbers of available rating events for analysis. These naturally coincide with the groups with the most incumbents. We statistically controlled for occupational group by including dummy-coded covariates in our regression analyses in order to respect the differences in Job content and context that are reflected in standards-based appraisal formats.

For consistency, we controlled for the same effects in the regression analysis of category-based ratings, even though a common rating format and scales were used across all occupation groups. For analyses of racial bias, we restricted our analysis to African-American and White employees only, since the records available to us reflected comparatively small numbers of other ethnic groups. The combination of these restrictions reduced our sample size to 12,177 category-based rating events and 236,693 standards-based rating events made on 69,026 unique individuals. These individuals were 69. 76% White and 30. 4% African-American, 59. 34% female, and 51 . 12% over 40 years of age. We outmoded race, sex and age as O, 1 variables. In each case, 1 was assigned to the category of interest: African-American, female, or person over 40. When ratings of the same individuals were compared across the two rating formats (same person, subsequent year) the Pearson r was . 3414 (p < . 0001, n = 11286) and the polychoric correlation (an ndex of association between ordinal variables) was . 4520 (ASE < . 0104, n = 11,286), indicat- ing substantial but not extreme consistency in ratings across occasions and rating formats.

We were reluctant to pursue this analysis further, since we could not identify the raters in this sample. An analysis that controlled for both the rater and the rating instrument would be a more powerful test of our hypotheses, but that was not available to us. As noted above, Jackets and Dubos (1991) have found support for the safety of the assumption of random assignment of raters when the sample is as large as this one. Instruments The two appraisal systems under study here were a simple, five-point category-based rating format and a Work Planning and Review (WAP&R) system.

The category-based approach called for a summary rating with the following criterion anchors: “outstanding,” “above satisfactory,” “satisfactory,” “conditionally satisfactory,” and “unsatisfactory. ” No other information was made available to raters for defining these performance levels. This summary rating was used for major personnel decisions such as promotions, probationary decisions, and terminations. Prior to making the summary rating, raters also made ratings on the annuity and quality of work using the same undefined criteria and no definitions of either quality or quantity.

The WAP&R system called for the generation of performance procedures described by Carlyle and Ellison in Appendix B of Bernardino & Beauty (1984, up. 343-348). This appendix describes guidelines for writing performance standards, selecting standards, designating critical elements, deriving evaluative weights, and making summary ratings. Raters, in consultation with the rate, defined acceptable standards of performance on all critical elements for a particular performance period. Each performance standard had to be defined in terms of a specific measure of quantity, quality, cost, or timeliness.

These guidelines also prescribed five criteria for developing and 149 critiquing performance standards and provided examples of acceptable standards. All standards had to be written prior to the start of an appraisal period and rates had to indicate that they had reviewed each standard and agreed with its use for evaluation. Standards were written to describe a fully satisfactory level of performance. Each rater and rate was required to write and agree on at least four reference standards, at least one of which had to be designated as a “critical element. The guidelines provided four criteria for designating a required element as “critical. ” Raters and rates also agreed on the relative importance of all performance elements and distributed 100 points among the standards. One review of the quality of the performance standards found that over 65% of a sample of the standards written under the new WAP&R system met the criteria for acceptable standards presented in the training guidelines (Bernardino, Hogan, & Kane, 1998) In addition, an average of over five performance standards was written per rate.

An example of an acceptable performance standard is “all legal briefs are submitted to the Court pursuant to their imposed deadlines. ” At the conclusion of the appraisal period, raters were required to make a Judgment on a three-point scale defined as “exceeds the standard,” “achieves the standard,” or “below the standard. ” “Exceeds the standard” was defined as “consistently exceeds the fully satisfactory standard” and “achieves the standard” was defined as “consistently achieves the fully satisfactory bevel of performance. After performance on all standards were evaluated, raters then made a summary rating on the extent to which the standards were exceeded or achieved using the same threatening scale. Raters were instructed to consider performance on all performance standards but to place greater weight on performance on the critical elements. The correlation between this rating and the weighted sum of the individual standard ratings for a sample of the data was . 89 (p < . 001). We use only the summary rating in subsequent analysis because the only ata maintained in the statewide database was the summary rating.