VALIDATION AND STANDARDIZATION OF SOFT SKILL SCALE (SSS) FOR EDUCATIONAL AND INDUSTRIAL USE IN NIGERIA

VALIDATION AND STANDARDIZATION OF SOFT SKILL SCALE (SSS) FOR EDUCATIONAL AND INDUSTRIAL USE IN NIGERIA

VALIDATION AND STANDARDIZATION OF SOFT SKILL SCALE (SSS) FOR EDUCATIONAL AND INDUSTRIAL USE IN NIGERIA

 

BY

 

Uwah, I., V

 

Phone: +234-7066737052, 234-8114787297

E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

&

Dr. Orluwene, G W

 

Phone: +234-8139465475, 234-8055474248

E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Department of Educational Psychology, Guidance & Counselling, University of Port Harcourt, Rivers State, Nigeria.

 

 

 

Abstract

 

The emergence of soft skill in the workplace has brought a shift in paradigm to the world of work. This means that there should be an effective instrument that is designed to measure accurately this important attribute. In this light, the current study developed and standardized Soft Skill Scale (SSS) for educational and industrial use in Nigeria. Triangulation research design based on Classical test theory was used to sample 887 participants from both educational and industrial sectors in Nigeria. One hundred and fifty (150) initial items of SSS were developed. Through Principal Component Analysis (PCA), the items were reduced to 110 at pilot study and 69 at post field level. Analysis was done with factor analysis, Pearson Product Moment Correlation as well as Split-Half method. The findings of the study showed that 69 items loaded into the five factors. This includes communication 14 items, Organization 14 items, Teamwork 21 items, Creativity 7 items and Adaptability 13 items. Validity through hypotheses testing evidence demonstrated significant differences in tribal scores (p=0.00<.05), age (p=.00<.05) as well as educational level (p=.00<.05). On the contrary, there were insignificant differences in the mean scores of respondents by gender (p=.07>.05) and workplace (p=.09>.05). Split-half reliability was .87. Based on the standardized scores at the post field level, 538 respondents had positive z-scores indicating the values were above the mean by the respective z-values. Based on this, it was recommendations among others that SSS should be recognized and used in school and industrial settings.

Keywords: Soft Skills, Validity, Reliability, Test, Standardization.

 

 

 
 

 

 

 

Introduction

The following conversation exposes the indispensability of soft Skills in organizations. “I don’t know what to do. I am seriously in need of a teacher to replace an out-going one in my school. I have conducted an interview and there is one that has the requisite paper qualifications and has also performed excellently well. Despite these, my husband is worried and is questioning his ability to have the leadership and people skills needed to fit into the position we want him to”.

As stated earlier, the interaction above is a clear pointer to the indispensability and emergence of Soft skills as part of requisite human capital needs in organizations today. The question from the above conversation may be; “why would the potential employers doubt this potential employee’s ability even with his excellent paper qualification and great interview performance? The possible answer to that question is his questionable soft skills ability. In the society today and especially in Nigeria, it is observed that for many organizations, paper and on-the-job competence (hard skills) are usually top of the list when it comes to hiring. It is also noted that emphasis is always placed on the specific skills that are specific to the job itself, as well as the training or experience needed to perform the job well with little or no attention of underlying skills that are needed to carry others along. To be precise, while emphasis is laid on hard skills which involve the technical abilities which often come from education, the provision of certificates, training, work experiences and which can be taught, soft skills as covert as they may be seen are the real forces that make things happen both in schools and organizations. It is also noted that, while these hard skills could be gotten easily through training over the years, soft skills are more personality related and which individuals though could be taught need more determination to put to use. In other words, while the importance of relevant education, training and job experience cannot be understated, the all-encompassing importance of soft skills is needed in achieving the goals and objectives of the organization.

 

According to Vasanthakumari (2019), soft skills are “a combination of people skills, social skills, communication skills, character or personality traits, attitudes, career attributes, social intelligence and emotional intelligence which individuals possess which help them to interact freely and achieve organizational objectives”. In other words, they could primarily be said to be “people skills”. People kills are those skills which when possessed can help the individuals to work with people irrespective of their differences. They are also a cluster of personal qualities, habits, attitudes and social graces that make someone a good employee and compatible to work with. The flexible nature of soft skills helps people to adapt and behave positively in such a way that they can deal effectively with the challenges of their professions and everyday life. Soft skills make people flexible in a world which keeps changing. Little wonder, Mitchell, Skinners & White, (2010) reported that employers usually prefer to see fine blend of competencies in their staff, and in addition to disciplined-based knowledge, thus adequate levels of soft skills are considered desirable for moving forward in any career. Despite the professional and technical skills, some persons are found in an organization doing things better than others and the question is what skills are they using? Soft skills are inborn, everyone possesses it but the difference is the application to everyday life. In a competitive labor market today, technical and academic skills alone do not always guarantee jobs; employers are looking beyond the “hard” skills. They instead tend to search for those special interpersonal traits, mindsets or work characteristics and approaches such as dependability, professionalism, leadership, communication skills and more to make a hiring decision. Such skills are often set out in contrast to hard skills of factual, technical, or disciplinary knowledge, as being “soft” skills, and these are incredibly important for 21st century education and employment (Andrews & Higson, 2008) Unlike hard or technical skills that are specific to the work context, soft skills can be applied in any work setting.

 

It should be note that in organizations whether profit or non-profit making, human capital development is the bane of progress and development. In schools, hospitals, industries, and elsewhere, it can be argued that real change comes in two forms: either development or deterioration. In whatever way that may be, it is obvious that it is only through the skills, attitude, values and personal characteristics of the workers that such changes may come.

 

 

 

Every employee in any organization whether it is school or non-school setting are expected to possess some degree of soft skills which will make him or her contribute meaningfully to the progress of the organization. This fundamental ability is what actually enables them to progress as an individual first and secondly as an organization in general. It I so evidenced that those who know best how to interact with others, understand them and socialize effectively are the ones who can bring out the best from them. Furthermore, it is the reasoning of the researcher that the personality and character of individuals in the work place are solely responsible for the smooth functioning of such individuals in the organization. According to Khiavi, Dashti, and Mokhtari (2016), individual characteristics are important factors influencing organizational commitment. There is an observation that in the current era, human resources are the most important asset of any organization. There are indications that if organizations would fully achieve their objectives, they require competent and committed human resources. It seems that irrespective of the nature of the organization, its size, desires, intrinsic as well as the extrinsic level of motivations of its employees, achieving the organizational goals will be very difficult without matching the right people with better human skills with the job. To this end, Ekeh (2018) stated that rather than what employees know in a professional capacity, soft skills focus on “Who” they are, as opposed to “Whatthey are. Put succinctly, soft skills are interpersonal skills hardwired to an individual’s personality, and they characterize how they interact with other people in the workplace.  Soft skills are basically the people skills, personality skills, and communication abilities any employee needs for the long-term success of the organization.

 

To be successful, having good soft skills is required (Gillard, 2009). It plays a significant role to build specific person’s personality (Schulz, 2008). Personal qualifications of soft and hard skills certainly facilitate the graduates to hunt any jobs (Wye & Lim, 2009). Softs skills support workers to work smoothly. There are many experts’ suggestions and studies trying to specify particular skills to utilize in highly educational administration. The result of Tang’s (2013) study suggested that soft skills are divided into seven skills as follows: Innovation invention and development skills, Communicative skills, Critical and problem solving skills, Teamwork skills, Leadership skills, Life-long learning and information management skills, and Ethics, moral and professional skills.

 

The value of soft skills in the workplace whether in academic setting or industrial is an issue of global importance, with research on soft skills conducted around in world, including Latin America, the Middle East and Europe (Andrews & Higson, 2008; Groh, Mckenzie & Vishwanath, 2015,  Prince, 2017). Nationally, the need to teach soft skills to be prepared for the workforce has increased as the number of jobs that require soft skills continues to grow (Hirsch, 2017), more jobs require interactions among multiple individuals or departments to complete tasks, and a changing economy has made specific technical skills less valuable (Mitchell, Skinner & White, 2010).

 

It is also noted that in the educational system, the employee personal characteristics play a pivotal role in affecting the climate, attitude and reputation of their schools. They are the foundation which drives learning abilities. With the right employee characteristics and skills, schools become effective incubators of learning, places where students are not only educated but challenged, nurtured and encouraged to also develop adequate skills needed in other organizations. It is often remarked that one special teacher can make a student feel inspired, as though he can do anything in the world if he sets his mind to it. Unfortunately, this student may attend another teacher's class with a sense of total frustration. One teacher can make a spirit soar while the other seems destined to destroy. The difference between the two teachers may be in their soft skills and abilities.

 

According to Allen (2019) “it is worth noting that sometimes, most people don't just prefer to leave their jobs, they leave their superiors/ bosses”. This ugly truth however could be directly traced to the fact that those superior lack people skills nagging at them at every slight opportunity they have. Employee drive and commitment are often directly linked to the individual's relationship with management. The question to ponder about is; If competitive success is achieved through people, then doesn't it follow that the people skills of those who lead and manage are critical?

 

In any organization, there are a variety of soft skills needed to effectively carry every one along. These include communication ability, organization, teamwork ability, creativity and critical thinking ability, adaptability, social intelligences as well as other factors that keep people going. These are key factor that controls the relationship between people. They are important skill which individuals especially leaders are expected to possess. For instance, Doyle (2019) stated that the ability to communicate effectively with superiors, colleagues, and staff is essential, no matter what industry you work in. Workers in the digital age must know how to effectively convey and receive messages in person as well as via phone, email, and social media. However, in all these forms of communication, the most important is the face to face interaction which individuals must have.

 

In organizations today, it is observed that the recruitment of employees with soft skills has been a major problem. These problems are that which an effective measuring instrument can help in resolving. In education and other sciences, the identification of special traits in humans have always relied in appropriate test instrument or rating scales. Similarly, the identification of basic soft skills to a large extent will similarly depend on a well-developed and properly administered questionnaire. To this end, Fabrigar and Ebel-Lam, (2007) stated that a questionnaire is a set of items designed to measure one or more underlying constructs or latent variables. These latent traits to a great extent are invisible to mere observation and are somewhat abstract thereby requiring special tools carefully constructed before they could be unfolded. In other words, it is a set of objective and standardized self-report questions whose responses are then summed up to yield a score. According to Zumbo, Gelin and Hubley (2002), the scale items are indicators of the measured construct and hence the score is also an indicator of the construct.

 

Chadla (2009) evinced that scale development or construction, is the act of assembling or/and writing the most appropriate items that constitute test questions for a target population. It is noted that the effective scale construction has a serious impact on the research extrapolations, touching first the quality and the size of the effects obtained and second the statistical significance of those effects or in other words the accuracy and sensitivity of the instruments (Price, 2017). According to Irwing and Hughes (2018), generally, successful tests are developed due to some combination of the three following conditions; the theoretical advances, empirical advances as well as the practical or market need. In anyway one may look at, all these procedures or purposes should be in line with a standard.

 

According to Popham (1999), a standardized test is that which is administered and scored in a consistent, or "standard", manner. They are designed in a manner that questions, conditions for administering, scoring procedures, as well as the interpretations are consistent and are administered and scored in a predetermined, standard manner. In other words, any test in which the same test is given in the same manner to all test takers, and graded in the same manner for everyone, is a standardized test. The standardization of any instruments involves some basic process which the developer must adhere to. As stated by Trochim (2006), this process is completed in five steps. They include defining the measured trait, Generating a pool of potential Likert items, having the items rated by a panel of experts, selecting the items to retain for the final scale and finally, administering the scale which will involve reversing items that measure something in the opposite direction of the rest of the scale. Similarly, Furr (2011) also described it as a process completed in five steps: (a) Defining the Construct as well as the context of measurement, (b) Choosing a response format, (c) Assembling the initial item pool, (d) Select and revise items and (e) Evaluate the psychometric properties. Steps (d) and (e) are an iterative process of refinement of the initial pool until the properties of the scale are adequate.

Constructs in psychology are not directly observable (Goleman, 1995), thus developers have first to define a general philosophical foundation to connect the construct to a set of observable traits or behaviors in line with the views of Trochim (2006) before the construct can be operationalized.

 

Through the observation of the researcher generally, there are several educational and industrial testing programmes that take place every year. In Nigeria in particular, these educational and vocational placement test are often too dependent on the cognitive domain of the individual. Experts have suggested that effective measurement include that which tests the all-round development and competence of an individual especially with regards to the purpose of such a test. As stated before, over the years, it has been carefully observed by the researcher that educational sector as well as industries upon hiring of their employees lay much emphasis on hard skills. These unceasing quests for cognitive superiority in employees have consciously or unconsciously eroded affective abilities of the employees. In schools and industries presently, it is a common occurrence where employees get so busy at the sight of superiors only to drop such zeal for work when he is away. Instances abound where employees no longer feel comfortable with the fellow colleagues at work. In all these, the problem of using the right tool in determining employees for certain jobs and position have continued to remain unattended.

 

These have resulted in situations where companies have depended solely on mere aptitude test which deals only with the cognitive domain of the employees. The consequential effect of this is the production of intelligent, highly skilled but poor affective workers. In relation to the educational sector, this has led to a staff population of academic intellectuals who lack the ability to communicate what they know well to the students. In this regard, comment like “he is intelligent but cannot transfer the knowledge” is common among students. In the industries as well, poor management-staff relationship have defined the activities of the employees.

As an effect, many industries have fold. Some have achieved limited targets while some have struggled to retain their staff after few months of employment. This unwise or poor recruitment option of “just” cognitive ability have from year to year cost organizations millions of naira through constantly re-advertisement, re-recruiting and retraining of staffs. Probably this problem could have been avoided if there was an effective instrument that can measure this important skill.

 

Unfortunately, lack of adequate soft skills testing instruments have deprived companies opportunity of producing efficient and effective manpower to deal with the problem associated with human capacity. It is also observed that the department of Human Resource Management (HRM) in every organization may possess all it takes to recruit highly intelligent and skill workers but have also failed in filtering employees with good people skill which is even more important in modern organizations. The consequence as noted earlier is the production of intelligent workers and supervisors who merely shout at each other before getting things done. They lack the requisite people skills of empathy, conflict resolution ability, teamwork ability, emotional sensibility, as well as work motivation etc. have been a major concern for educational and other industries.

On the other hand, the filtering of better employees, teachers, students as well as lecturers may suffer setbacks if the wrong persons are placed in the wrong places. This point to the fact that there is urgent need to place some people in jobs that demands emotional stability and “firmness of heart” in situations that calls for such.

 

Based on the background and the indispensability of soft skills to the work place environment, the inappropriateness or total unavailability of a localized and current soft-skills test placement instruments as well as the paucity of research in the development and standardization of such an affective scale compared to other domains in Nigeria, the researchers were motivated to develop and standardize the Soft Skill Scale (SSS) for use in educational and other industrial organizations.

 

Aim and Objectives

It I upon the backdrop of the discussions above that the researchers aimed at validating and standardizing Soft Skill Scale (SSS) for use in educational and industrial organizations in Nigeria. To be specific, the study had the following objectives;

  1. Determine the sub-scales and items of SSS
  2. Determine the construct validity of the Soft Skill Scale (SSS) Hypothesis testing Evidence
  3. Determine the internal consistency of Soft Skill Scale (SSS) using Split Half method.
  4. Determine the norm of the SSS with the use of Z-score and T-score

 

Research Questions

The following research questions will guide the study;

  1. What are the sub-scales and items of SSS?
  2. What is the construct validity of SSS using Hypothesis testing evidence?
  3. What is the internal consistency of SSS using Split Half method?
  4. What is the Norm of the SSS with the use of Z-score and T-score

Hypotheses

The following hypotheses were formulated to guide the researcher in the study;

  1. The SSS scores of respondents from Igbo, Hausa, Yoruba and minority tribes does not differ significantly.
  2. There is no significant difference in the SSS scores of respondents from the various age groups.
  3. There is no significant difference in the SSS scores of respondents from the various educational level.
  4. There is no significant difference in the SSS scores of male and female respondents.
  5. There is no significant difference in the SSS scores of respondents in the educational and industrial settings.

 

Methodology

The current study adopted triangulation research design in carrying out the study. As Kpolovie (2010) stated, this design uses “multiple research methodologies, measurement instruments and statistical tools that are related to the theoretical construct of interest to more comprehensively investigate a particular phenomenon”. In particular, the study involves both theoretical, space and methodological triangulation design. These designs involve using more than one theoretical scheme, more than one category of respondents and more than one method in gathering data for the study. The researcher adopted this design because it will allow for the combination of various design and methods like ANOVA, split-half on determining its internal consistency and factor analysis in terms of determining the factor structures if the instrument. The area of the study is Nigeria. The study area was considered vast enough and with wide variety of sample characteristics which proofs important in the goal of achieving a wider generalization of the result of the study.

 

The population of the study covered a wide range of individuals including teachers and students of secondary and tertiary institutions as well as workers from industries across the six geo-political zones in the Nigeria. Hence, the age range of the population is 15 to 60 years. As released by the National Bereau of Statistics (NBS, 2020), individuals between this age range in these geo-political zones were 31,228,145.

A sample size of 1,020 comprising of 720 respondents drawn from educational institutions and 300 respondents drawn from industrial institutions was used. The researcher used the multi-stage sampling procedure to draw the sample from the study. First the researcher used simple random sampling technique through balloting to draw the three geographical zones in the study. thy included the North-central, south-south and South-East. Also, convenience sampling technique was used to draw two states which included North-central; Abuja (FCT) and Benue State, South-South: Rivers state and Akwa-Ibom and South-East: Imo State and Abia States. The researcher also applied disproportionate sampling technique to draw three institutions (2 educational and 1 industrial) from each of the state giving a total of eighteen (18) institutions (12 educational and 6 industrial institutions). Similar non-proportionate sampling technique was used in selecting 60 respondents from educational institution. This means that each state has 120 respondents while each zone has 240 respondents from educational institution. This gives a total of 720 respondents from educational institutions across the three geo-political zones drawn. In similar manner, the researcher also drew fifty workers from one industry from each state. This meant that each zone had 100 respondents from an industry giving a total of 300 respondents.

 

In planning and development of the soft skills, the items were done under communication, organization, teamwork, creativity as well as adaptability sub-scales. In general, SSS adopted the Never (N), Rarely (R), Sometimes (S), Often (O) and Always (A).Likert multi-variate scale of divided into two sections (A and B). Section A of the instrument contains personal details of the respondents including, their tribe, age range, gender, educational level, place of work as well as organizational level. Trail testing of the item was carried out with 100 respondents with a total of 150 items generated. After trial analysis, the items were reduces to 110 items. The split-half reliability revealed was 0.87during the pilot study. Analysis of the study was carried out using Factor analysis, split-half, Pearson Product Moment Correlation, Spearman Brown Prophesy formula as well as ANOVA.

 

Results

Research Question One: What are the sub-scales of SSS based on factor analysis via Principal Components Analysis (PCA)?

Before this analysis, the KMO test of sample adequacy was performed to endure the adequacy of the items in order to proceed with factor analysis. Kpolovie (2021) stated that the Bartletts Test of Sphericity must be statistically significant at less than 0.0005 alpha. From analysis KMO was .92 and Bartletts Test of Sphericity (5995, p < .0005) had confirmed the appropriateness of the data for proceeding with factor analysis.

 

Table 1: A brief section showing Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

19.300

17.545

17.545

19.300

17.545

17.545

9.983

9.076

9.076

2

3.132

2.847

20.392

3.132

2.847

20.392

8.968

8.153

17.229

3

2.281

2.073

22.465

2.281

2.073

22.465

3.813

3.467

20.696

4

2.108

1.916

24.382

2.108

1.916

24.382

3.396

3.087

23.783

5

1.861

1.692

26.074

1.861

1.692

26.074

2.520

2.291

26.074

6

1.719

1.563

27.636

 

 

 

 

 

 

7

1.687

1.534

29.170

 

 

 

 

 

 

8

1.648

1.498

30.668

 

 

 

 

 

 

9

1.535

1.396

32.064

 

 

 

 

 

 

The various components of soft skills scales were investigated. With Principal Component Analysis, five underlying factors were forced on the basis of scree test and eigenvalues greater than 1 criterion. The five extracted factors had Extracted Sum of Squared Loadings Cummulative % of 26.074. The factor 1, factor 2, factor 3, factor 4 and factor 5 had 19.30, 3,132, 2,281, 2.108 and 1.86 Total Extracted Sum of Square Loadings that respectively accounted for 17.545, 2.847, 2.073, 1.916 and 1.692 percent of the total variance explain before the Varimax orthogonal rotation. The Rotation Sum of Squared Loading Total and % of Variance explained were respectively were 9.983 and 9.076 for factor 1, 8.968 and 8.15 for factor 2, 3.81 and 3.467 for factor 3, 3.39 and 3.087 for factor 4 and finally 2.52 and 2.291 for factor 5. Which amounted to 26.074 Cumulative % of total variance explained by the five rotated factors.

Fig. 1.1 Shows condensed version of the scree-plot of the analysis

The scree-plot above depicts that the number of critically extracted underlying factors above the point of discrimination is five telling us that the five components overwhelmingly explains or account for the total variance when all the 110 items were considered. The point on the scree-plot that is a little below 20 eigenvalues is the factor 1 (Component Number 1), the point on the scree-plot that is a little above 3 eigenvalues is the factor 2 (Component Number 2) while the three point following it closely represents factor 3, 4 and 5 respectively. It is only these five critically extracted factors that were subjected to the ultimate factor analysis process chiefly factor rotation

 

Table 2Showing merged Rotated Component Matric of ONLY items into the various Factors. Note: Non-selected items were deleted from the table

Factors

Sub-Scales

Loaded Items

Coefficient Range

1

Communication

84, 51, 57, 71, 82, 56, 83, 64, 81, 70, 58, 52, 72, 69, 68, 73, 75, 50, 65, 60, 67, 54, 63, 85, 66, 74, 80, 86 and 76

.40-.51

2

Organization

18, 40, 41, 26, 24, 16, 32, 39, 27, 35, 36, 34, 28, 19, 31, 25, 42, 29, 17, 9, 15 and 37

.41-.51

3

Teamwork

107, 106, 108, 110, 99, 105 and 98

.41-.52

4

Creativity

95, 94, 92, 93 and 96

.45-.58

5

Adaptability

5, 6, 7, 8 and 4

.43-.53

 

 

The Varimax Orthogonal Rotated Component Matrix revealed that the 29 items loaded highest in factor 1. Twenty two (22) items including in factor 2, eight items in factor 3. Five items in factor 4 and five items in factor 5. On the other hand, 41 did not load in any of the factors. In summary, this means that 69 items met up the .40 criterion loading mark and as such were suitable for inclusion in the SSS for educational and industrial use in Nigeria while 41 were dropped for lack of meeting the criteria.

Research Question Two: What is the construct validity of SSS Via Hypotheses Testing Evidence?

  1. Hypotheses One: The SSS scores of respondents from Igbo, Hausa, Yoruba and minority tribes does not difference significantly.

 

 

 

 

 

Table 3 shows One-Way ANOVA of the difference in SSS scores of Hausa, Yoruba, Igbo and Other tribes.

Tribe

N

Mean

Std.D

 

Hausa

Yoruba

Igbo

Others

Total

68

202

349

268

887

346.60

338.13

336.30

309.01

329.26

42.59

52.33

53.11

61.10

56.35

 

 

ANOVA

 

 

Sum of Sq.

d.f

Mean Sq

F

Sig

Result

 

Between Group

Within Group

Total

163471.43

2650665.882

2814137.32

3

883

886

54490.47

3001.88

 

18.15

 

0.000

Significant

(Reject Ho)

 

                         

 

Summary of the ANOVA showed calculated F of 18.15 and sig. value of 0.000. Hence, since sig. (p = 0.000< 0.05) is less than .05 alpha, the null hypotheses is rejected meaning that The SSS scores of respondents from Igbo, Hausa, Yoruba and minority tribes do differ significantly. Based on the fact that the finding was significant, multiple comparism was conducted in line with Kpolovie (2010) stand. The result showed an insignificant difference except comparism between Hausa and others, Yoruba and Others as well as Igbo and Others

 

  1. Hypothesis Two: There is no significant difference in the SSS scores of respondents from the various age groups.

Table 4: One-Way ANOVA Showing Differences in SSS Scores of Respondents from Various Age Groups.

AGE

N

Mean

Std.D

 

10-15 Years

16-25 years

26-35 years

36 & Above

76

326

318

167

329.83

317.50

334.88

341.26

52.09

58.31

56.02

50.96

 

 

 

ANOVA

 

Sum of Sq.

d.f

Mean Sq

F

Sig

Result

Between Group

Within Group

Total

79217.17

2734920.14

2814137.31

3

883

886

26409.72

3097.30

 

8.52

 

0.000

Significant

(Reject H0)

                         

In the table, Summary of the ANOVA showed calculated F of 8.52 and sig. value of 0.000.

 

 

Hence, since sig. (p = 0.000< 0.05) is less than .05 alpha, the null hypothesis is rejected meaning that the SSS scores of respondents from the various age groups differ significantly. Furthermore, multiple comparism between groups showed an insignificant difference except comparism between age bracket of 10-15 & 36 and Above years, 16-25 & 36 and Above years as well as 16-25 & 36 and Above years.

  1. Hypotheses Three: The SSS scores of respondents from various educational Level does not differ significantly.

Table 5 shows One-Way ANOVA of the difference in SSS scores based on educational Level.

Educational Level

N

Mean

Std.D

 

SSCE

NCE-Bsc

PGDE-Masters

Ph.D

211

391

221

64

313.76

324.54

347.52

346.17

56.87

59.83

42.86

55.90

 

 

ANOVA

 

 

Sum of Sq.

d.f

Mean Sq

F

Sig

Result

 

Between Group

Within Group

Total

151365.28

2662772.03

28114137.31

3

883

886

50455.09

3015.59

 

16.73

 

0.000

Significant

(Reject H0)

 

                         

The table shows that respondents with SSCE, NCE-B.sc, PGDE-Maters as well as Ph.D were 211, 391, 221 and 64 respectively. Their means values were 313.76, 324.54, 347.52 and 346.17 respectively. This mean values showed that respondents with PGDE-Masters had higher scores of soft skills, followed by those with Ph.D and followed by those with NCE-B.sc and lastly by those with SSCE. Summary of the ANOVA showed calculated F of 16.73 and sig. value of 0.000. Hence, since sig. (p = 0.000< 0.05) is less than .05 alpha, the null hypothesis is rejected meaning that the SSS scores of respondents from various educational level differs significantly.

The analysis showed that all other multiple comparism showed a significant difference except comparism between SSCE & NCE-B.sc and comparism between PGDE-Masters & Ph.D. This means that only these groups contributed insignificantly to the result in the ANOVA test.

  1. Hypothesis Four: There is no significant difference in the SSS scores of male and female respondents.

Table 6:          Mean, standard deviation and t-test of the difference in male and female SSS  Scores.

 

Gender 

N

Mean

Std

df

t-crt

µ

t-cal

Sig.

Result

Male  

369

333.22

53.25

 

885

 

1.960

 

0.05

 

1.769

 

0.07

Insignificant (RetainedH0)

Female

518

326.72

58.35

 

 

 

 

 

 

 

The table showedcalculated t value was 1.769 while sig. value was 0.07. Hence, since sig. (p=0.07>0.05) is greater than 0.05 alpha at 885 degrees of freedom, the null hypothesis is retained meaning that there is no significant difference in the SSS scores of male and female respondents.

  1. Hypothesis Five: There is no significant difference in the SSS scores of respondents in the educational and industrial settings.

Table 7: Mean, standard deviation and t-test of the difference in scores of those from educational and industrial settings.

Institution 

N

Mean

St.D

df

t-crt

µ

t-cal

Sig.

Result

Educational  

598

331.46

56.31

 

885

 

1.960

 

0.05

 

1.672

 

0.09

Insignificant (RetainedH0)

Industrial

289

324.72

56.72

 

 

 

 

 

 

 

From the analysis, calculated t value was 1.672 while sig. value was 0.09. Hence, since sig. (p=0.09>0.05) is greater than 0.05 alpha at 885 degrees of freedom, the null hypothesis is retained meaning that there is no significant difference in the SSS scores of respondents in the educational and industrial settings.

 

 

Research Question Three: What is the internal consistency of SSS using Split-Half Method?

Table 8: Split-Half Coefficient Reliability

N of items (Half)

N of Items

Valid N cases

Rht.

Rft.

Remarks

55

110

886

.77

.87

High Reliability

Note: 1 Excluded Case

The table above revealed that split-half reliability of the half test (rht) was .77. However, when spearman brown prophecy formula was used to substitute the half test, a Guttman reliability test (rft) was .87. Again Kpolovie (2010) also indicated that this was a high coefficient that guarantees the reliability of SSS for use in schools and industries.

Research Question Four: What is the Norm of the SSS with the use of a. Z-score,b. T-score?

Table 9: A Sectional Overview of Normalized Scores of Respondents by Sub-Scales. (Communication, Organization, Teamwork, Creativity and Adaptability)

 

A. Comunication

Mean=42.06

Std.D=13.74

       

B. Organization

Mean=39.65

Std.D=15.42

   

Raw Scores

Frequency

Z-scores

T-scores (To whole Numbers)

 

Raw Scores

Frequency

Z-scores

T-scores (To Whole Numbers)

 70

1

2.032

70

 

70

1

1.967

70

64

1

1.596

66

 

66

1

1.708

67

63

4

1.523

65

 

64

1

1.578

66

62

3

1.450

65

 

62

3

1.448

64

60

1

1.305

63

 

61

2

1.383

64

59

4

1.232

62

 

60

2

1.319

63

58

3

1.159

62

 

59

3

1.254

63

57

7

1.086

61

 

58

5

1.189

62

56

6

1.014

60

 

57

5

1.124

61

55

12

0.941

59

 

56

10

1.059

61

54

14

0.868

59

 

55

13

0.995

60

53

20

0.795

58

 

54

9

0.930

59

52

24

0.723

57

 

53

17

0.865

59

51

24

0.650

56

 

52

27

0.800

58

50

19

0.577

56

 

51

22

0.735

57

49

33

0.504

55

 

50

24

0.671

57

48

42

0.432

54

 

49

27

0.606

56

47

35

0.359

54

 

48

45

0.541

55

46

41

0.286

53

 

47

52

0.476

55

45

48

0.213

52

 

46

46

0.411

54

44

44

0.141

51

 

45

55

0.346

53

 

C. Teamwork

Mean=58.75

Std.D=21.53

       

D. Creativity

Mean=21.00

Std.D=8.51

   

Raw Scores

Frequency

Z-scores

T-scores (To Whole Numbers)

 

Raw Scores

Frequency

Z-scores

T-scores (To Whole Numbers)

105

1

2.148

71

 

35

1

1.644

66

98

1

1.823

68

 

34

2

1.527

65

95

2

1.683

67

 

33

3

1.409

64

92

1

1.544

65

 

32

4

1.292

63

91

1

1.498

65

 

31

7

1.174

62

89

1

1.405

64

 

30

11

1.057

61

88

2

1.358

64

 

29

17

0.940

59

87

1

1.312

63

 

28

35

0.822

58

86

1

1.265

63

 

27

44

0.705

57

85

3

1.219

62

 

26

70

0.587

56

84

5

1.173

62

 

25

53

0.470

55

83

7

1.126

61

 

24

56

0.352

54

82

7

1.080

61

 

23

59

0.235

52

81

11

1.033

60

 

22

74

0.117

51

80

12

0.987

60

 

21

58

0.000

50

79

17

0.940

59

 

20

61

-0.117

49

78

16

0.894

59

 

19

60

-0.235

48

77

17

0.848

58

 

18

41

-0.352

46

Raw Scores

Adaptability

Mean=37.45

Std.D=13.87

Frequency

Z-scores

T-scores (To Whole Numbers)

65

1

1.985

70

59

2

1.552

66

58

3

1.480

65

57

5

1.408

64

56

3

1.336

63

55

2

1.264

63

54

4

1.192

62

53

10

1.120

61

52

16

1.048

60

51

12

0.976

60

50

21

0.904

59

49

30

0.832

58

48

37

0.760

58

47

34

0.688

57

From the table in the paired sub-scales, it could be noticed raw scores that produces certain standard scores in the various groups differs. This means that a raw score that could constitute a certain standard score in one sub-scale may not produce similar standard score in another.

Table 10: A Sectional Overview of Normalized Scores of Respondents By tribe. (Hausa, Yoruba, Igbo & Others)

 

Hausa

Mean=287.12

Std.D=67.63

     

Yoruba

Mean=292.20

Std.D=78.79

   

Scores

F

Z-scores

T

Scores

F

Z-scores

T

409

2

1.802

68

459

1

2.12

71

372

1

1.255

63

432

1

1.77

68

371

1

1.240

62

427

1

1.71

67

365

2

1.151

62

423

1

1.66

67

363

1

1.122

61

416

1

1.57

66

362

3

1.107

61

414

1

1.55

65

361

1

1.092

61

411

1

1.51

65

357

1

1.033

60

410

2

1.49

65

356

2

1.018

60

407

1

1.46

65

354

2

0.989

60

402

1

1.39

64

352

1

0.959

60

398

1

1.34

63

351

1

0.944

59

396

1

1.32

63

350

2

0.930

59

393

1

1.28

63

347

1

0.885

59

389

1

1.23

62

 

Igbo

Mean=275.33

Std.D=87.99

     

Others

Mean=271.41

Std.D=83.11

   

Raw Scores

Frequency

Z-scores

T-scores

Raw scores

Frequency

Z-scores

T-score

483

1

2.360

74

550

1

3.352

84

458

1

2.076

71

499

1

2.738

77

456

1

2.053

71

459

1

2.257

73

449

1

1.974

70

453

1

2.185

72

447

1

1.951

70

425

1

1.848

68

440

1

1.871

69

412

1

1.692

67

423

1

1.678

67

405

1

1.607

66

420

1

1.644

66

403

1

1.583

66

417

1

1.610

66

394

1

1.475

65

416

1

1.599

66

388

1

1.403

64

413

2

1.564

66

387

1

1.391

64

412

1

1.553

66

386

1

1.379

64

407

1

1.496

65

384

1

1.355

64

                 

 

Table 11: A Sectional Overview of Normalized Scores of Respondents By Age. (10-15,16-25, 26-35 & 35 & Above Years)

 

 

10-15 Years

Mean=280.42

Std.D=77.08

       

16-25 Years

Mean=274.51

Std.D=78.67

   

 

Raw Scores

Frequency

Z-scores

T-scores (To whole Numbers)

 

Raw Sores

Frequency

Z-scores

T-scores (To whole Numbers)

 

425

1

1.876

69

 

456

1

2.307

73

 

402

1

1.577

66

 

453

1

2.269

73

 

384

2

1.344

63

 

432

1

2.002

70

 

377

1

1.253

63

 

414

1

1.773

68

 

376

1

1.240

62

 

412

1

1.748

67

 

372

1

1.188

62

 

410

1

1.722

67

 

371

1

1.175

62

 

407

1

1.684

67

 

370

1

1.162

62

 

403

1

1.633

66

 

364

1

1.084

61

 

402

1

1.621

66

 

362

1

1.058

61

 

399

1

1.582

66

 

360

2

1.032

60

 

398

1

1.570

66

 

358

2

1.006

60

 

397

1

1.557

66

 

357

1

0.993

60

 

395

1

1.532

65

 

355

1

0.967

60

 

393

1

1.506

65

 

354

1

0.955

60

 

388

1

1.443

64

 

350

1

0.903

59

 

386

1

1.417

64

 

347

2

0.864

59

 

384

1

1.392

64

 

346

1

0.851

59

 

382

1

1.366

64

 

345

1

0.838

58

 

375

1

1.277

63

 

26-35 Years

Mean=287.00

Std.D=87.66

       

36 & Above

Mean=280.94

Std.D=82.31

   

Raw scores

Frequency

Z-scores

T-scores (Whole Numbers)

 

Raw scores

Frequency

Z-scores

T-scores (To whole Numbers)

550

1

2.993

80

 

459

2

2.163

72

499

1

2.411

74

 

416

1

1.641

66

483

1

2.229

72

 

413

1

1.604

66

458

1

1.944

69

 

410

1

1.568

66

449

1

1.841

68

 

409

2

1.556

66

447

1

1.818

68

 

405

1

1.507

65

440

1

1.738

67

 

402

1

1.471

65

427

1

1.590

66

 

395

1

1.386

64

423

2

1.544

65

 

394

1

1.374

64

420

1

1.510

65

 

393

1

1.361

64

417

1

1.476

65

 

391

1

1.337

63

                           

Table 12: A Sectional Overview of Normalized Scores of Respondents By Educational Level. (SSCE, NCE-Bsc, PGDE-Masters & Ph.D)

 

SSCE

Mean=261.65

Std.D=78.28

     

NCE-B.sc

Mean=282.16

Std.D=86.18

   

Raw Scores

Frequency

Z-scores

T-scores (To Whole Numbers)

Raw scores

Frequency

Z-scores

T-scores (To Whole Number)

410

1

1.895

69

550

1

3.108

81

409

1

1.882

69

499

1

2.516

75

402

2

1.793

68

483

1

2.330

73

389

1

1.627

66

456

1

2.017

70

384

1

1.563

66

453

1

1.982

70

382

2

1.537

65

447

1

1.913

69

379

1

1.499

65

432

1

1.738

67

377

1

1.474

65

425

1

1.657

67

373

1

1.422

64

423

1

1.634

66

372

1

1.410

64

417

1

1.564

66

371

1

1.397

64

414

1

1.530

65

370

1

1.384

64

413

1

1.518

65

369

1

1.371

64

412

1

1.506

65

 

PGDE-MASTERS

Mean=288.29

Std.D=80.19

     

Ph.D

Mean=306.73

Std.D=78.77

   

Raw Scores

Frequency

Z-scores

T-scores (To Whole Numbers)

Raw scores

Frequency

Z-scores

T-scores

458

1

2.116

71

459

2

1.933

69

449

1

2.004

70

440

1

1.692

67

427

1

1.730

67

423

1

1.476

65

420

1

1.642

66

404

1

1.235

62

416

2

1.592

66

402

1

1.209

62

413

1

1.555

66

396

1

1.133

61

412

1

1.542

65

395

1

1.120

61

407

1

1.480

65

391

1

1.070

61

405

1

1.455

65

388

1

1.032

60

399

1

1.380

64

387

1

1.019

60

395

1

1.331

63

384

1

0.981

60

 

Table 13: Sectional Overview of Overall SSS Standard  Scores Mean=280.13

Std.D=91.94

Raw scores

Frequency

Z-scores

T-scores ( To Whole Numbers)

550

1

2.935

79

499

1

2.380

74

483

1

2.206

72

459

2

1.945

69

458

1

1.934

69

456

1

1.913

69

453

1

1.880

69

449

1

1.837

68

447

1

1.815

68

440

1

1.739

67

432

1

1.652

67

427

1

1.597

66

425

1

1.576

66

423

2

1.554

66

420

1

1.521

65

417

1

1.489

65

 

Discussions

The current study dealt with the development and standardization of Soft Skill Scale (SSS) which is a new instrument develop in Nigeria for both educational and industrial use. The final scale contains 69 items with five factors or subscales. These sub-scales consisted of Communication, Organization, Teamwork, Creativity as well as Adaptability.  As noted earlier, the scale can generally be used in both educational and industrial settings. From finding one, it was noted that most items were force out of the factors which was conducted via Principal Component Analysis (PCA). These items included item 78, 79, 53, 62, 55, 61, 59, 77, 49, 46, 48, 43, 10, 38, 33, 45, 44, 11, 88, 30, 23, 87, 103, 20, 13, 100, 104, 101, 102, 97, 91, 90, 12, 21, 2, 1, 47, 3, and 14. These items were eliminated from the final structures of the PCA for basically one reason. This is the inability to meet up with their expected Factor loading which was set on 0.40.

As said earlier, 69 items including items 84, 51, 57, 71, 82, 56, 83, 64, 81, 70, 58, 52, 72, 69, 68, 73, 75, 50, 65, 60, 67, 54, 63, 85, 66, 74, 80, 86, 76, 18, 40, 41, 26, 24, 16, 32, 39, 27, 35, 36, 34, 28, 19, 31, 25, 42, 29, 17, 9, 15, 37, 107, 106, 108, 110, 99, 105, 98, 95, 94, 92, 93, 96, 5, 6, 7, 8 and 4 were included as components. Among these, 14 items loaded comfortably in sub-scale 1 which is Communication. Sub-scale 2 which was Organisation had 14 items well loaded into it. Subscale 3 which was Teamwork had 21 items loaded. Sub-scale 4 which was Creativity had seven items loaded and finally sub-scale 5 tagged Adaptability had 13 items loaded comfortably into it. From this loading, it is seen that Teamwork/Social skills prevail the most when the skills of both industrial and educational setting workers are assessed. On the other hand, Creativity had less loading. This means that these skills are less possessed by industrial and educational setting workers. The reason for this high skill in terms of teamwork and social skill in both educational and industrial sectors may be explained by a number of reasons. This could be because many of the industrial workers especially in Nigeria always organized themselves into unions, teams, clubs and societies which makes them to have a conscious or unconsciously development in the skills of teamwork, collaboration, and social attitudes. On the other hand, it could be that the reason why the educational and industrial setting workers perform poorly in critical thinking and creativity skill could be because of the lack of positive attitude for critical thinking as well as thinking out solutions for things. It could be that respondent in these two sectors have neglected critical thinking skills and creativity. For those in the industrial setting, it could be that because they have already gained employment and has a meaningful source of livelihood, they may have less need to be creative or think critically outside the box. On the part of the student, it could also be that the lack of attitude for reading, lack of attitude for excellence and lack of attitude for positive achievement could also be a reason why they do not score high in Creativity and Critical. Factors like high compromise in the educational sector as well the relegation of excellence and the downward trend in educational values may also contribute to why those in the educational sector develop less skill for creativity and critical thinking. However, it was somehow surprising to the researcher that communication skills which seem to be a common skill have less factors loaded into it.

 

For the hypothesis testing evidence, it was revealed that there's a significant difference in the soft skill scale of respondents from the various tribes. This means that there's a difference in the acquisition of soft skills scale of people from the various major tribes under investigation. This also implies that people from Hausa, Yoruba, Igbo and Other minor tribes do possess significant differences in their soft skills abilities. The findings of this study may come because majority of the respondents are aware of the importance of soft skill scale. Majority of them in some of the tribes are also aware of the need to develop soft skills more than some from other tribes. It also indicates that tribal influence is a factor that interfere with the acquisition of this skills. The result is surprising to the researcher because majority of the respondents despite their tribes is observed by the researcher to have the ability possess similar skills just like those in any of the tribes. The finding of the study is also in disagreement with that reported by Rashid and Muhammad (2009) who reported and insignificant difference in the soft skills of respondents from various tribes including Japanese, Portuguese etc.

 

Hypothesis testing evidence two revealed that there is a significant difference in the soft skill mean scores of respondents from the various age groups. This finding means that the chronological age of an individual has an influence on the development of some of personal and people skills. It also means that as individual matured with age, certain attributes and skills that help him or her to interact freely with people are developed. Specifically, from the mean scores of the respondents, it is seen that those between 36 years and above have higher mean scores. This could only suggest that the higher an individual goals, the more such individual will develop people skills and interpersonal skills which will help him or her to interact with people. The finding of the study is not surprising to the researcher in any sense because to the best of his knowledge individuals mature with age. As individuals grow, they learn new things, have new experiences and apply such experiences in dealing with people. These findings may come because individuals despite their age may have learnt how to relate with people effectively. They must have learnt that age is a major factor that helps individuals to interact. The finding of the study is in line with that reported by Rashid and Muhammad (2009). Epstein, Ryser and Pearson (2002) also found out similar findings when they noted that there is no significant difference between age and soft skills of respondent.

 

From hypothesis testing evidence three, it was reported that there is a significant difference in the soft skill mean scores of respondents from various educational level. This finding means that the level of education which an individual attain can help in building people skills in individuals which they could use in order to interact. Specifically, the mean scores revealed that individuals with PhD degrees have higher scores in soft skills. It was also shown that those with the least level of education which is SSCE considered in this study also have the least means scores. The implication here is that as individuals grow educationally, they may learn new experiences, gain more knowledge, gain more understanding and wisdom in order to interact and deal with people since soft skills is otherwise known as people-skills. As individuals grow educationally, they might have learnt through various means that these skills are necessary in order to interact with people. The finding also means that educational institutions are a good source of people-skills. It suggests that individuals with less people-skill are those who have not attended higher educational status. On the contrary, those with higher people skills are those that have attained a higher level of education. The finding of the study is not surprising to the researcher in any sense because to the best of his knowledge, he is aware that educational status of individuals exposes individuals to experiences that will help them to interact freely with people. The finding of the study it's not surprising to the researcher once more because all other research like that by Aworanti, Taiwo and Iluobe (2005) have all reported significant differences in people-skills of individuals drawn from various educational levels.

 

From hypothesis testing evidence four, it was revealed that there is no significant difference in the soft skill mean scores of male and female respondents. This finding means that gender is not a factor that influence the development of soft skills in individual. It means that both male and female respondent are capable of either developing or not developing software skills. The finding of the study means that whether one is male or female, he or she through the right exposure can develop soft skills necessary to interact with people freely in their various sectors. The finding of the study to some extent is not surprising to the researcher because gender differences in most educational empirical studies are insignificant in the development of certain skills. On the contrary, the finding of the study is also to some extent surprising because some researchers like Marwalla (2018) and Aworanti, Taiwo and Iluobe (2015) cited earlier have reported gender differences in the development of basic skills or people skills in individuals.

 

From hypothesis testing evidence five, it is revealed that there is also no significant difference in the soft skills mean score of individuals in the educational and industrial settings. This finding means that whether individuals are in educational sector or industrial settings, with the right exposure to certain soft skills, they could learn and they could interact freely. The findings suggest that work place does not have any influence on the acquisition of soft skills or people skills among individual. To some extent, the finding is surprising to the researcher because to the best of his knowledge he thought that certain environment exposes individuals to certain skills that can help them best interact with people. In other words, it was the thinking of the researcher that individuals who work in an industrial settings where they are hierarchy of employment, some level people-skills will be exposed to them like teamwork and social skills, communication ability etc. that can keep them above every other person in any other sector. On the contrary, it was also certain that those in the educational sector just like the findings from the influences of educational level revealed should possess more people skills especially does that have attained higher educational status. Also, worker like lecturers, professors etc. who interact freely with people including their colleagues and students were expected by the researcher to possess some certain people skills which will help them to be successful in their field. The finding of the study here also is in line with that reported earlier by Aworanti, Taiwo and Iluobe (2015) and also Chiu, Mahat, Rashid, Razak and Omar (2016) who all reported insignificant differences in the soft skill scores of individuals from various work setting. Similarly, split-half reliability showed a high reliability index of 0.87 further indicating that the instrument was reliable enough. This was further in ie with the findings of Aworanti, Taiwo and Iluobe (2015)

 

From findings four, study have reported through the standard scores that 349 respondents had their Z-scores scores below the mean while 538 respondent have there's a score above the mean. This means that majority of the respondents have above average soft skill mean in both educational and industrial settings. The scores were also transformed into T-scores which were then transformed to the nearest whole number.

 

Conclusion

Based on the findings of the study, the following conclusions are made;

  1. The SSS is a 69 item instruments for assessing soft skills of individuals in both educational and industries in Nigeria.
  2. In terms of validity, the SSS possess through the hypotheses evidences had major difference in scores of respondents by tribe, age as well as educational level. Both male and female respondents as well as those from educational and industrial settings possess similar soft skills.
  3. SSS has high reliability in terms of Split Half index
  4. SSS has been standardized using both z-scores and t-scores for use in Nigeria.

 

Recommendations

The following recommendations are made or the study;

 

  1. SSS should be used as a basis to compare, assess, or used in the convergent and divergent validation process in similar and opposite instruments. This is to establish proper validity of related and unrelated instruments intended to be constructed by other researchers for use in Nigeria.
  2. Based on the hypotheses testing evidences, it is recommended that;
  1. More of social skills should be taught to tribes that have less skill as identified through the means scores. In other words, other minority tribes should be taught to develop soft skills. This could be achieve through infusing these skills in the school curriculum of these places.
  2. Similarly, it is expected that education on soft skills should be focused more on individuals between the ages of 16-25, followed by those between 26-35 years and lastly targeted on those between 10-15 years. This is so because certain age were found to perform more on these skill than others.
  3. Since it was reported that there was a significant difference in the mean scores of the respondents from various educational level, it is recommended that sift skills should be concentrated in educational levels with less skills. In this study, it is revealed that those with SSCE have less. Hence, these skills should be part of the secondary school curriculum.
  4. Developers or users of SSS should not pay special focus on gender and workplace environment (educational or industries) hen developing soft skill scales.
  1. It is recommended that developers of similar scales should use SSS to determine the reliability of their instruments since it has been establish that SSS has high Split half reliability.
  2. Since the scores of SSS is normed and standardized, performance of individuals can be attributed relatively to their normed group.

 

 

 

 

 

References

Demaio, T., &Landreth, A. (2004). Do different cognitive interview methods produce different results. In S. Presser, J. Rothgeb, M. Couper, J. Lessler, E. Martin, J. Martin, & E. Singer (Eds.), Questionnaire Development and Testing Methods. Hoboken, NJ: Wiley.

 

Chadha, N. K. (2009). Applied psychometry. New Delhi, IN: Sage Publications.

 

Dimitrov, D. M. (2012). Statistical methods for validation of assessment scale data in counseling and related fields. Alexandria, VA: American Counseling Association.

 

Doyle, A. (2019). These are the communication skills employers look for in employees. Retrieved Feb. 17, 2019, from https://www.coursehero.com/file/p6ghk7k/wikidotcomaviationshell-model-Doyle-A-2019-January-30-These-Are-the/.

 

Fabrigar, L. R., & Ebel-Lam, A. (2007). Questionnaires. In N. J. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 808-812). Thousand Oaks, CA: Sage.

 

Goleman, D. (2005). Emotional intelligence: Why it can matter more than IQ. New York, NY: Bantam Dell.

 

Irwing, P., & Hughes, D. J. (2018). Test Development. In P. Irwing, T. Booth, & D. J. Hughes (Eds.), The Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development, V.I (pp. 4-47). Hoboken, NJ: Wiley

 

Khiavi, F.F, Dashti, R &Mokhtari, S. (2016). Association between organizational commitment and personality traits of faculty members of Ahvaz Jundishapur University of Medical Sciences. Electron Physician. 8(3) 2129–2135.

 

Kpolovie, P. J. (2010). Advanced research methods. Owerri: Springfield publishers Ltd.

 

Kpolovie, P. J. (2021). Factor Analysis: Excellent Guide with SPSS. Monee, IL. USA.

 

Popham, W. J. (1999) Why Standardized Tests Don't Measure Educational quality. Educational Leadership. 56, 8-15

 

Price, L. R. (2017). Psychometric methods: Theory into practice. New York: The Guilford Press.

 

Rifkin, J. (1995). The end of work: The Decline of the global labor force and the dawn of the Post-Market Era. New York: G.P. Putnam's Sons.

Trochim, W. M. (2006). The research methods knowledge base (2nd ed.).
http://www.socialresearchmethods.net/kb

 

Zumbo, B. D., Gelin, M. N., & Hubley, A. M. (2002). The construction and use of psychological tests and measures. In Encyclopedia of Life Support Systems. France: United Nations Educational, Scientific, and Cultural Organization Publishing (UNESCO-EOLSS Publishing).

Andrews, J., & Higson, H.(2008). Graduate employability, ‘soft skills’ versus ‘hard’ business: A European study. Higher Education in Europe. 33(4), 411–422

Price, L. R. (2017). Psychometric methods: Theory into practice. New York: The Guilford Press.

 

Allen, T. (2019) How Bad Bosses Compel Good Employees To Leave. Retrieved from https://www.forbes.com/sites/forbes-personal-shopper/2021/12/21/new-years-eve-dress-outfits/?sh=7d6996d05ccf on 15-12-2021.

Aworanti, O. A., Taiwo, M. B. & Iluobe, O. I (2015). Validation of Modified Soft Skills Assessment Instrument (MOSSAI) for Use in Nigeria. Universal Journal of Educational Research, 3 (11) 847-861

Ekeh, R., S. (2018) Workplace Ethics and Professional Development.  Owerri. Vision Prints.

 

Vasanthakumari, S. (2019) Soft skills and its Application in Work Place. World Journal of Advanced Research and Review, 3 (2) 66-72

 

 

 

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