Confirmatory Factor Analysis of the Life Skills Measurement Tool for University Students

Abstract

To measure life skills, tools have been developed for this purpose, and these tools were forms to measure these variables under study and research, and through this study we would like to confirm the life skills scale through the use of confirmatory factor analysis, which is used in testing assumptions that necessarily assume the existence of patterns or special factors of relationships in the data on the basis of which variables can be classified. The individual builds a model that supposedly describes and interprets empirical data in light of relatively few parameters, which gives us a model that is more likely to measure the variables under study.

1. Introduction

The topic of measuring life skills is one of the topics that researchers have addressed a lot, relying on its definitions and the foundations of its construction. Life skills are considered an end in themselves that must be available in people with whom you communicate and constantly continue life, and they actively contribute to the individual’s acquisition of a set of basic skills, which enable him to interact and deal with the environmental difficulties surrounding him and ensure his ability to think and make the right decision positively throughout his life, and does not stop at a certain period or at a certain age, but is constantly evolving during the stages of life and its changes, whether environmental or social. It is capable of giving the individual the privacy of positive adaptation to the situations and problems that he faces during his daily life and with which he deals positively with him and other people and situations. Thus, he is able to possess self-learning skills that enable him to learn at all times and longevity inside and outside the study place. Life skills are of particular importance, as they help in shaping and refining the personality of an individual, preparing him to face the issues of the Times and the problems of everyday life, to be a creative, productive and active person locally and globally, capable of development, development, change and local and international competition.

Hayek stated about Teo [1] in this context” that the possession of life skills provides an individual with the weapon of coexistence, adaptation, success, the ability to achieve effective communication with others and transfer what he has learned beyond the classroom [2].

Many researchers have been interested in studying and measuring the degree of acquisition of life skills among university students, i.e., higher education outputs, including the study of Sobhi Lulu and Qishta [3], Obeidat and Saada [4], Hartmann [5]. Longitudinal study on the quality of life and adjustment strategies of breast cancer patients and their “companion-referent.” Magdy [6], the effectiveness of using information technologies in achieving the dimensions of quality of life among samples of Omani students, Proceedings of the Psychology and Wellbeing Symposium; Gatab et al. [7], Students' life quality prediction based on life skills, and studies aimed at finding out the positive impact of some programs on the development of life skills, such as the study of Omar [8], Al-Hayek [9]; Ayyad and Al-Din [10], Slav [11], Al-Ajmi [12]; Quality of life and its relationship to the future orientation among students of the Faculty of Graduate Studies at Naif Arab University for Security Sciences "Factor Study", El-Sherbiny and Walid [13], Hala [14]; Life Skills for People with Mental Inertia from Students of the Faculty of Arts at Al-Qadisiyah University Uruk for Humanities Somaya and Dalia [15], Muhammad [16], Mohammadi [17], Amna [2]; these studies dealt with aspects and various fields have emphasized the importance of life skills in education for the student coming to graduation, in which the educational attitude based on life skills programs is the basis in the formation, and in this regard, the Ministry of education emphasizes the importance of learning based on life skills in” as it seeks to develop the abilities of students and develop them to adapt to real life situations, and develop their thinking skills before any work or task performance to ensure a useful life, and achieve sound and positive results” [18], as Kothar kujak stressed “the need to pay attention to life skills, and provide each learner with them, so that he can face the modern changes and challenges that characterize this era, and at the same time be able to perform the work required of him to the fullest, these skills bring him successful coexistence, adaptation, flexibility and success in his work and personal life, and these skills are multiple and varied, covering all areas of life” [19, 20].

Several studies have focused on measuring life skills and have developed tools for this purpose, and these tools were forms to measure these variables under study and research, and through this study we would like to confirm the measure of life skills, and this through a psychometric study of the life skills measurement tool. Emphasis has been placed on university training. On this basis, we put forward the following: What is the factor structure of the components of the life skills measurement tool for university students using confirmatory factor analysis.

1.1 Objectives

Identify the factor structure of the components of the life skills measurement tool for university students using confirmatory factor analysis.

1.2 Hypotheses

The factor structure of the components of the life skills measurement tool for university students using confirmatory factor analysis corresponds to the proposed tool structure.

2. Terminology

2.1 Life skills

In the language you know the skill, the dexterity of the thing, and you have (mastered) the thing - stared at it [21]. And Amhara is opening too ([22], p. 561). It is also defined by a term as “the ability to perform mental, emotional, motor activity or both, and its learning or acquisition requires ease, accuracy and time economy in its performance” ([23], p. 15) and ([24], p. 240), and is also defined in the dictionary of sociology as “a complex organization of behavior developed through the learning process and the direction towards a specific goal or focus on a specific activity” ([25], p. 116). Pedagogues defined it as “a series of movements that can be observed both directly and indirectly, performed by a certain person or a number of people in the course of pursuing a goal or performing a task” ([26], p. 25). Namely, the ease and accuracy in conducting work and grow as a result of the education process.

Namely, to carry out a certain process with a degree of speed and mastery with economy in the effort expended [27]. Ahmed Zaki Saleh defined it as “ease and accuracy in performing a work with a degree of speed and mastery with economy of effort and with the least possible time by understanding” ([28], p. 123). As defined by the World Health Organization (WHO),“the ability to adopt an adaptive and positive behavior that enables to deal effectively with the demands and challenges of everyday life” ([29], p. 03). It was reported It is defined by UNICEF as psychological, social, interpersonal skills, exchange skills, scientific and professional skills that an individual needs in facilitating communication with others, negotiating with them appropriately, critical thinking and problem solving skills” [30]. It is also expressed as a set of behaviors acquired by the student that help him adapt to different and changing life requirements and face everyday problems, and is expressed in this study in the results shown by the study tool from all the above, we can conclude that skill is the ability to carry out an activity or a set of life activities, including emotional, intellectual, motor or physical, easily, masterfully and accurately in the least time, and this is to reach the desired end.

2.2 University students

They are all students who have studied for 5 years at the University and are about to obtain a master’s degree in branch specialties at various universities in the National country.

2.3 Factorial analysis

Factorial analysis is a statistical technique aimed at interpreting the coefficients of positive correlations that have statistical significance between various variables. Or it is a mathematical process aimed at simplifying the correlations between the various variables involved in the analysis down to the common factors that describe the relationship between these variables and their interpretation. Therefore, factor analysis is a statistical method for analyzing multiple data that are related to each other with different degrees of correlation in the form of independent classifications based on qualitative bases of classification [31]. Factor analysis begins by calculating the correlation coefficients between a number of variables, and then we will get a matrix of correlations between these variables in the research sample on which the measurement was performed, and then this correlation matrix is followed by a factor analysis to reach the minimum possible number of axes or factors that enable us to express the greatest amount of variation between these variables.

2.4 Exploratory factor analysis

This type is used in cases where the relationships between the variables and the underlying factors are unknown and therefore the factorial analysis is aimed at discovering the factors to which the variables are described.

2.5 Confirmatory factor analysis (CFA)

It is used in testing hypotheses that necessarily assume the presence of special patterns or factors of relationships in the data on the basis of which variables can be classified. The individual builds a model that supposedly describes and interprets empirical data in light of relatively few parameters.

3. Actions

3.1 Method

The descriptive method was used in a survey style to suit the nature of the study.

3.2 Community

The research community is university students in various disciplines. We relied on (20) universities from different Algerian national countries, chosen randomly.

3.2.1 Study sample

We selected a random sample of 471 second-year master’s students. Their results were analyzed in confirmatory factorial analysis.

3.2.2 Life skills tool

After reviewing a number of studies that dealt with the topic of life skills, especially the study of Imran Taghreed and others life skills, 2001, as well as Hamad Hassan and Dua [32], Fahim [33] and UNICEF [34], WHO classification 1993, adding the classification of the Center for the development of curricula and educational materials of the Ministry of education in Egypt 2000, the Ministry of education of the kingdom of Saudi Arabia Ghanem Ghanem, Saad and others. The Palestinian Ministry of education and higher education in 2003, Fouad Ayad Ismail and Hedi Bassam Saad al-Din, Amor Omar in 2008, Nayef Mufti Nahar al-Jabour in 2012, Nidal Ahmed Ismail al-Ghafri in 2012, and Tutti Hayat study in 2014, as well as another study in 2018 [27].

These studies categorized life skills into dimensions, and each dimension contains a set of questions that measure each specific life skill. We have collected a set of questions in this context that represent life skills among university students and have developed an assessment scale for them as follows:

A Table 1 representing the answer keys to the questions of the tool.

Always	Often	Sometimes	Rarely	Never
05 points	04 points	03 points	02 points	01 points

Table 1.

Point of response to the question.

3.2.2.1 Study limitations

Spatial limitations: The study and its instrument were applied to a sample of university students on a national level, encompassing 20 universities from the east, west, north, and south.

Human limitations: The questions were administered to a sample of 1080 students on a national level, randomly selected from master’s level students.

Temporal limitations: The research was conducted during the academic year 2019/2020.

3.2.2.2 Presentation of results and discussion

A Table 2 showing the labels for the extracted factors and the number of expressions within each.

Number of expression	Factors	Number
12	Thinking and problem solving	01
8	Patriotism and Identity	02
7	Planning and time management	03
7	Psychological and self-awareness	04
6	Language control	05
7	Scientific and technological	06
5	Communication	07
5	Social and working with the group	08

Table 2.

Tool axes and number of questions.

3.2.2.3 Study results the tool by researchers

At the beginning of the exploratory factor analysis for the life skills assessment tool, and after completing the initial process for the core components of the factors that make up the tool, we wanted to know if these results from the exploratory factor analysis would apply to the confirmatory factor analysis using a different sample than the one used initially.

After conducting the statistical analysis, we have found the following:

We notice a deviation in the distribution of scores from the normal distribution (multivariate distribution) in the parallel table. The Maximum Likelihood (ML) method retains its accuracy (parameter estimation) in the presence of a moderate level of deviation in the distribution of scores from the normal distribution (multivariate distribution) (Table 3).”

Variable	Min	Max	Skew	c.r.	Kurtosis	c.r.
Q57	1000	5000	−1602	−14,490	2547	11,522
Q50	1000	5000	−1501	−13,577	2360	10,673
Q51	1000	5000	−2182	−19,736	4449	20,124
Q52	1000	5000	−1980	−17,913	3572	16,155
Q53	1000	5000	−1650	−14,930	2310	10,448
Q54	1000	5000	−2390	−21,618	5835	26,393
Q55	1000	5000	−2675	−24,202	7733	34,978
Q56	1000	5000	−1089	−9854	,983	4447
Q42	1000	5000	−,417	−3772	−,767	−3471
Q37	1000	5000	−,786	−7113	,007	,030
Q38	1000	5000	−,467	−4224	−,601	−2719
Q39	1000	5000	−,554	−5010	−,240	−1087
Q40	1000	5000	−1546	−13,988	1853	8383
Q41	1000	5000	−,860	−7779	,192	,867
Q43	1000	5000	−,454	−4108	−,167	−,755
Q44	1000	5000	−,725	−6561	,068	,308
Q45	1000	5000	−,881	−7973	,240	1084
Q46	1000	5000	−,995	−8998	,318	1436
Q47	1000	5000	−,249	−2250	−,410	−1855
Q48	1000	5000	−1046	−9467	,759	3434
Q49	1000	5000	−1250	−11,303	1248	5643
Q8	1000	5000	−1337	−12,097	2008	9083
Q9	1000	5000	−,758	−6861	−,124	−,562
Q10	1000	5000	−,180	−1631	−,179	−,808
Q11	1000	5000	−,604	−5466	−,198	−,896
Q12	2000	5000	−,654	−5914	−,340	−1537
Q13	1000	5000	−,439	−3971	−,252	−1138
Q14	1000	5000	−,732	−6621	−,048	−,218
Q15	2000	5000	−,298	−2699	−,709	−3208
Q16	1000	5000	−,658	−5952	−,002	−,011
Q17	1000	5000	−,800	−7240	,450	2037
Q5	1000	5000	−,179	−1623	−,420	−1898
Q1	1000	5000	,025	,226	−,535	−2419
Q2	1000	5000	−,501	−4534	−,394	−1784
Q3	1000	5000	−,740	−6698	−,091	−,413
Q4	1000	5000	−,938	−8482	,234	1057
Q6	1000	5000	−,068	−,614	−,509	−2301
Q7	1000	5000	−,915	−8273	,654	2957
Q18	1000	5000	−,154	−1393	−,683	−3091
Q19	1000	5000	−,714	−6459	,027	,124
Q20	1000	5000	−,872	−7889	,090	,406
Q21	1000	5000	−,386	−3489	−,664	−3006
Q22	1000	5000	−,383	−3464	−,676	−3060
Q23	1000	5000	−,319	−2882	−,632	−2861
Q24	1000	5000	−,427	−3865	−,681	−3079
Q25	1000	5000	−,162	−1468	−,124	−,563
Q26	1000	5000	−,097	−,880	−,612	−2768
Q36	1000	5000	−,377	−3414	−,542	−2452
Q35	1000	5000	−,182	−1643	−,336	−1520
Q27	1000	5000	,044	,396	−,618	−2794
Q32	1000	5000	−,117	−1055	−,859	−3884
Q28	1000	5000	−,265	−2400	−,630	−2849
Q29	1000	5000	−,027	−,243	−,692	−3132
Q30	2000	5000	−,289	−2610	−,814	−3680
Q31	1000	5000	−,050	−,448	−,735	−3323
Q33	1000	5000	−,318	−2874	−,541	−2445
Q34	1000	5000	−,684	−6186	−,101	−,459
Multivariate					389,522	52,622

Table 3.

Assessment of normality (Group number 1).

Results of factor analysis SPSS version 24 assessment of normality.

The ability of the model to estimate its parameters, or the designation of the model Identification Model:

It comes after the stage of building the model and determining it by the modeling method (Table 4 and Figure 1).

Number of distinct sample moments:	1653
Number of distinct parameters to be estimated:	122
Degrees of freedom (1653–122)	1531

Table 4.

Computation of degrees of freedom (Default model).

Confirmatory factor analysis Amos version 23.

Figure 1.

Computation of degrees of freedom (Default model) [25].

Through the table and through a purely mathematical expression, we can express the first number 1653 as the units of information in the data:

Units of information in the data * 57 × (57 + 1) /2 = 1653 Sub-subsections can also be used throughout the manuscript.

While the number 125 expresses the needs of the model or unrestricted free parameters that need to be estimated:

1. the number of variations of independent variables, whether measured or latent: 66 (independent variables in this model are latent variables)

2. the number of correlations or variations between factors, latent variables or measurement errors (number of deleted arrows): 0 we mean by this the model from which we will initially proceed and which, through the results that will be sorted at its level, enables us to modify this model.

3. number of tracks (straight and unidirectional arrows) unrestricted: 56 Thus the needs of the model are: (66 + 0 + 56 = 122). Degree of freedom” (1653–122 = 1531).

Therefore, the model is specific and therefore its parameters can be estimated.

Secondly, the study model’s outputs and quality indicators:

1. Estimation of the model’s free parameters:

In this stage, we will attempt to estimate the standardized and non-standardized regression coefficients, covariances, and variations for the study model, which is essentially a measurement model. We will rely on the maximum likelihood (ML) method.

• The initial confirmatory model shows all the factors involved in the study:

See (Figure 2).

Figure 2.

Planning factor on life skills.

The confirmatory prototype shows all the factors involved in the study:

Confirmatory factor analysis Amos version 23.

By the graph of the modular solution of the model under study, and by observing the standard regression coefficients, it is clear that all questions.

The saturation of the axes that you interpret has exceeded 40%, What is striking is the lack of saturation of the planning factor on life skills in the form sufficient -, 032 where there is almost no amount of information that is interpreted in this The axis.

3.2.2.4 Unstandardized regression coefficients or raw scores

See (Table 5).

			Estimate	S.E.	C.R.	P
Planning	<−−-	Lifeskills	-,043	,076	−,567	,571
Communication	<−−-	Lifeskills	,741	,086	8668	***
patriotism	<−−-	Lifeskills	,643	,087	7375	***
Social	<−−-	Lifeskills	,705	,085	8265	***
Psychological	<−−-	Lifeskills	,910	,096	9494	***
Scientific	<−−-	Lifeskills	,865	,095	9071	***
Thinking	<−−-	Lifeskills	1000
Languagecontrol	<−−-	Lifeskills	,866	,099	8735	***
Q31	<−−-	Thinking	,915	,066	13,760	***
Q21	<−−-	Scientific	,809	,068	11,868	***
Q24	<−−-	Scientific	,736	,072	10,228	***
Q19	<−−-	Scientific	,864	,062	13,871	***
Q22	<−−-	Scientific	1000
Q23	<−−-	Scientific	,909	,063	14,385	***
Q18	<−−-	Scientific	,802	,063	12,651	***
Q20	<−−-	Scientific	,854	,060	14,235	***
Q29	<−−-	Thinking	,776	,061	12,821	***
Q30	<−−-	Thinking	,782	,061	12,771	***
Q32	<−−-	Thinking	1000
Q28	<−−-	Thinking	,942	,063	14,889	***
Q27	<−−-	Thinking	,868	,062	13,976	***
Q34	<−−-	Thinking	,679	,058	11,619	***
Q35	<−−-	Thinking	,901	,065	13,879	***
Q26	<−−-	Thinking	,832	,059	14,175	***
Q25	<−−-	Thinking	,619	,059	10,465	***
Q36	<−−-	Thinking	,883	,065	13,668	***
Q33	<−−-	Thinking	,842	,067	12,587	***
Q4	<−−-	Planning	,757	,075	10,070	***
Q2	<−−-	Planning	,976	,084	11,566	***
Q3	<−−-	Planning	1000
Q5	<−−-	Planning	,850	,082	10,408	***
Q1	<−−-	Planning	,846	,074	11,459	***
Q7	<−−-	Planning	,551	,058	9424	***
Q6	<−−-	Planning	,696	,077	9077	***
Q15	<−−-	Social	1000
Q13	<−−-	Social	,784	,088	8957	***
Q16	<−−-	Social	,869	,094	9283	***
Q17	<−−-	Social	,992	,092	10,790	***
Q14	<−−-	Social	,863	,089	9662	***
Q10	<−−-	Communication	,669	,086	7758	***
Q8	<−−-	Communication	,823	,079	10,409	***
Q11	<−−-	Communication	,934	,088	10,567	***
Q12	<−−-	Communication	,798	,077	10,324	***
Q9	<−−-	Communication	1000
Q46	<−−-	Psychological	,959	,066	14,617	***
Q49	<−−-	Psychological	,986	,062	15,786	***
Q44	<−−-	Psychological	,637	,069	9297	***
Q47	<−−-	Psychological	,748	,061	12,169	***
Q48	<−−-	Psychological	,854	,060	14,217	***
Q43	<−−-	Psychological	,649	,057	11,294	***
Q45	<−−-	Psychological	1000
Q39	<−−-	Languagecontrol	,963	,045	21,250	***
Q37	<−−-	Languagecontrol	,736	,040	18,485	***
Q40	<−−-	Languagecontrol	,581	,048	11,988	***
Q41	<−−-	Languagecontrol	,631	,048	13,233	***
Q38	<−−-	Languagecontrol	1000
Q42	<−−-	Languagecontrol	,803	,049	16,369	***
Q53	<−−-	Patriotism	1000
Q56	<−−-	Patriotism	,668	,045	14,699	***
Q51	<−−-	Patriotism	,882	,037	23,897	***
Q54	<−−-	Patriotism	,871	,035	25,004	***
Q55	<−−-	Patriotism	,719	,035	20,469	***
Q50	<−−-	Patriotism	,727	,040	18,139	***
Q52	<−−-	Patriotism	,906	,038	23,891	***
Q57	<−−-	Patriotism	,689	,043	16,024	***

Table 5.

Regression weights: (Group number 1 - Default model).

Confirmatory factor analysis Amos version 23.

By the table of non-standard regression coefficients, i.e. saturation of questions on the axes that you interpret all came up as a function statistically (P is less than5%), and it is striking the lack of saturation of the planning factor on life skills.

Where his non-standard regression coefficient came non-statistically d,5710 (P greater than 5%) (Table 6).

			Estimate
Planning	<−−-	Lifeskills	-,032
Communication	<−−-	Lifeskills	,662
patriotism	<−−-	Lifeskills	,431
Social	<−−-	Lifeskills	,625
Psychological	<−−-	Lifeskills	,704
Scientific	<−−-	Lifeskills	,644
Thinking	<−−-	Lifeskills	,766
Languagecontrol	<−−-	Lifeskills	,542
Q31	<−−-	Thinking	,671
Q21	<−−-	Scientific	,584
Q24	<−−-	Scientific	,502
Q19	<−−-	Scientific	,686
Q22	<−−-	Scientific	,723
Q23	<−−-	Scientific	,713
Q18	<−−-	Scientific	,623
Q20	<−−-	Scientific	,705
Q29	<−−-	Thinking	,623
Q30	<−−-	Thinking	,620
Q32	<−−-	Thinking	,693
Q28	<−−-	Thinking	,730
Q27	<−−-	Thinking	,682
Q34	<−−-	Thinking	,562
Q35	<−−-	Thinking	,677
Q26	<−−-	Thinking	,693
Q25	<−−-	Thinking	,504
Q36	<−−-	Thinking	,666
Q33	<−−-	Thinking	,611
Q4	<−−-	Planning	,551
Q2	<−−-	Planning	,656
Q3	<−−-	Planning	,668
Q5	<−−-	Planning	,574
Q1	<−−-	Planning	,648
Q7	<−−-	Planning	,510
Q6	<−−-	Planning	,489
Q15	<−−-	Social	,674
Q13	<−−-	Social	,504
Q16	<−−-	Social	,527
Q17	<−−-	Social	,647
Q14	<−−-	Social	,554
Q10	<−−-	Communication	,419
Q8	<−−-	Communication	,593
Q11	<−−-	Communication	,605
Q12	<−−-	Communication	,586
Q9	<−−-	Communication	,690
Q46	<−−-	Psychological	,724
Q49	<−−-	Psychological	,789
Q44	<−−-	Psychological	,454
Q47	<−−-	Psychological	,597
Q48	<−−-	Psychological	,703
Q43	<−−-	Psychological	,553
Q45	<−−-	Psychological	,713
Q39	<−−-	Languagecontrol	,803
Q37	<−−-	Languagecontrol	,729
Q40	<−−-	Languagecontrol	,523
Q41	<−−-	Languagecontrol	,567
Q38	<−−-	Languagecontrol	,889
Q42	<−−-	Languagecontrol	,667
Q53	<−−-	Patriotism	,868
Q56	<−−-	Patriotism	,604
Q51	<−−-	Patriotism	,836
Q54	<−−-	Patriotism	,859
Q55	<−−-	Patriotism	,766
Q50	<−−-	Patriotism	,703
Q52	<−−-	Patriotism	,836
Q57	<−−-	Patriotism	,644

Table 6.

Standardized regression weights: (Group number 1 - Default mode).

Confirmatory factor analysis Amos version 23.

Through both tables, standardized and unstandardized regression coefficients, and the graphical representation, it is evident that all the questions are significantly associated with the axes they are meant to explain, with substantial saturations exceeding 40%. In this regard, all unstandardized regression coefficients are statistically significant (P < 5%). Notably, it’s interesting to observe the lack of saturation of the planning factor on life skills, with a coefficient of −0.032. This implies that there is minimal information explained by this factor. The unstandardized regression coefficient for this factor is also not statistically significant at 0.5710 (P > 5%).

3.2.2.5 The variations (common variance) and covariances

See (Tables 7 and 8).

			Estimate	S.E.	C.R.	P	Label
e56	<−−>	e57	,263	,025	10,382	***
e41	<−−>	e40	,220	,032	6851	***
e55	<−−>	e54	,068	,012	5633	***

Table 7.

Covariances: (Group number 1 - Default model).

Confirmatory factor analysis Amos version 23.

			Estimate
e56	<−−>	e57	,575
e41	<−−>	e40	,350
e55	<−−>	e54	,342

Table 8.

Correlations: (Group number 1 - Default model).

Confirmatory factor analysis Amos version 23.

These variations and Covariances in the two tables were obtained after modifying the model. This was done using adjustment indicators to achieve the maximum convergence or match between the model and the data. This results in a better interpretation of information by the factors, leading to an overall improvement in the quality of the fit.

Model testing, or testing the quality of model fit:

Through various types of fit indices, we obtain a general or overall assessment of how well the model matches the data. We will review the results of widely used fit indices together.

It is carried out through conformity indicators of various types and they provide us with a general or aggregate about the conformity of the model to the data, and we will review together the results of widely used or used conformity indicators (Table 9).

Model	NPAR	CMIN	DF	P	CMIN/DF
Default model	125	2499,040	1528	,000	1635
Saturated model	1653	,000	0
Independence model	57	12,911,951	1596	,000	8090

Table 9.

CMIN.

Confirmatory factor analysis Amos version 23.

We notice from the table that the first line is specific to our model, in the second line which is the saturated model while the last line is the independent model independent Model or the null model, which is based on the assumption that the variances of the observed variables on the level of society is equal to zero or zero and only the values of the variance of these variables remain (the model with independent variables).

Degree of Freedom = 1528, the value was 2499,040 = CMIN, which is a function statistically, 1635 = CMIN/DF, which is less than 3, it is good for these two indicators within the matching indicators absolute fit indices and indicate to what extent the information derived from the model is represented Presumably the information contained in the data of the research sample (Table 10).

Model	RMR	GFI	AGFI	PGFI
Default model	,041	,850	,837	,785
Saturated model	,000	1000
Independence model	,176	,272	,246	,263

Table 10.

RMR, GFI.

Confirmatory factor analysis Amos version 23.

We observe from the table that:

The value,850 = GFI, which is inappropriate is less than 0.90 within the absolute conformity indicators.

Absolute fit indices (Table 11).

Model	NFI Delta1	RFI rho1	IFI Delta2	TLI rho2	CFI
Default model	,806	,798	,915	,910	,914
Saturated model	1000		1000		1000
Independence model	,000	,000	,000	,000	,000

Table 11.

Baseline comparisons.

Confirmatory factor analysis Amos version 23.

We note through the table that the value of, 910 = TLI and the value of, 910 = CFI which are acceptable values for the two indices are bounded between 0.90 and 0.95 within the comparative or incremental Comparative fit matching indicators indices/fit indices incremental (Table 12).

Model	RMSEA	LO 90	HI 90	PCLOSE
Default model	,036	,033	,039	1000
Independence model	,120	,118	,122	,000

Table 12.

RMSEA.

Confirmatory factor analysis Amos version 23.

We note from the table that: the value of ,036 = RMSEA, which is a good indicator among the indicators of correcting the lack of economy or economic indicators correction indices parsimony (Table 13).

Model	AIC	BCC	BIC	CAIC
Default model	2749,040	2782,605	3273,596	3398,596
Saturated model	3306,000	3749,861	10,242,722	11,895,722
Independence model	13,025,951	13,041,257	13,265,149	13,322,149

Table 13.

AIC.

Confirmatory factor analysis Amos version 23.

We note from the table that:

The economic indicators AICO BCC and BIC of the study model reached lower values compared to the saturated model They are a good indicator of the economy; these indicators are among the indicators of correcting the lack of economy or economic indicators correction indices parsimony (Table 14).

Model	ECVI	LO 90	HI 90	MECVI
Default model	5610	5339	5898	5679
Saturated model	6747	6747	6747	7653
Independence model	26,584	25,851	27,329	26,615

Table 14.

ECVI.

Confirmatory factor analysis Amos version 23.

Through the table.

The ECVI economic index of the study model reached a lower value compared to the saturated model and the independent model, namely A good indicator of the economy, these indicators are among the indicators of correcting the lack of economy or economic indicators correction indices parsimony.

SRMR = 0.048.

The value of SRMR = 0.048 was a good indicator for the economy among the indicators for correcting the lack of economy or economic indicators correction indices parsimony (Table 15).

Match Indicators	Standard	Value	Judgment
CMIN (χ^2)	Non-significant	2060.40	Non-significant (p = 0.00)
Df	Degree of freedom	1165
Nc	Good (1–3) (3–5) Acceptable	1.76	Good
SRMR	Good (0–0.05) (0.05–0.08) Acceptable	0.05	Good
GFI (Goodness of Fit Index)	0.9–0.95, (Acceptable) 0.95–1(Good)	.86	Not suitable
TLI (Tucker-Lewis Index)		0.91	Acceptable
CFI (Comparative Fit Index)		0.92
CFI (Comparative Fit Index)		0.92
RMSEA (Root Mean Square Error of Approximation)	Good (0–0.05) (0.05–0.08) Acceptable	0.04	Good

Table 15.

A Fit indices for the initial model after modification.

Confirmatory factor analysis Amos version 23.

Based on the matching indicators results shown in the table, it appears that they are appropriate. This is confirmed by the results obtained from the confirmatory factor analysis, where the model demonstrated a good fit for the study sample data.

Bollen-Stine Bootstrap (Default model)

The model fit better in 9999 bootstrap samples.

It fit about equally well in 0 bootstrap samples.

It fit worse or failed to fit in 1 bootstrap samples.

Testing the null hypothesis that the model is correct, Bollen-Stine bootstrap p =,000.

The same results that we obtained previously indicate that the number of samples used for bootstrapping the matching indicators was 9999 out of 10,000 samples, with only one sample showing an inappropriate model-data fit.

Hypothesis Testing: Based on these results, the statistical difference is significant (p = 0.000), which is less than 0.05 (the significance level). Therefore, we reject the null hypothesis, indicating statistically significant differences in favor of the model’s quality and suitability. It demonstrated good data fit for the vast majority.

From the results obtained through the confirmatory factor analysis of the proposed model for estimating free parameters and the goodness of fit test, it is evident that the model is suitable for measuring life skills, pending the comparison between the two models.

4. The ability of the model to estimate its parameters, or the designation of the model identification model

It comes after the stage of building the model and determining it by the modeling method Units of information in the data * 50 × (50 + 1)/2 = 1275.

The number 107 expresses the needs of the model or unrestricted free parameters that need to be estimated: 1-the number of variations of independent variables, whether measured or latent: 58 (independent variables in this model are latent variables) 2-the number of correlations or variations between factors, latent variables or measurement errors (number of deleted arrows): 0 3. number of tracks (straight and unidirectional arrows) unrestricted: 49 Thus the needs of the model are: (58+ 0 + 49 = 107).

Degree of freedom” (1275–107 = 1168) Therefore, the model is specific and therefore its parameters can be estimated.

It is worth mentioning and what we should draw attention to is that the appointment of the model is before the researcher goes into collecting the results, otherwise he will get into trouble, he may finish collecting the results of the research, which will cost time, and then he comes to test his model, which he can find is not assigned (in case of absence of appointment) (Figure 3).

Figure 3.

A-modeling estimation of free parameters (Parameter estimation).

The study model after the exclusion of the planning axis.

Confirmatory factor analysis Amos version 23.

Computation of degrees of freedom (Default model).

Number of distinct sample moments: 1275

Number of distinct parameters to be estimated: 107

Degrees of freedom (1275–107): 1168

4.1 Outputs and indicators of the quality of the study model

4.1.1 A-modeling estimation of free parameters (Parameter estimation)

At this stage, we will try to estimate: standard and non-standard regression coefficients, correlations and variations of the study model, which is actually a measurement model, where we will rely on the maximum probability method (ML).

4.1.1.1 Outputs and indicators

Modified confirmatory factor model after exclusion of planning (Figure 4).

Figure 4.

Standardized regression weights.

Confirmatory factor analysis Amos version 23.

By the graph of the modular solution of the model without the diagram under consideration, and by observing the standard regression coefficients, clearly shows that all questions are saturated on the interlocutor that you interpret saturations.

Considering that she exceeded 40%, which is the same for all axes on her parent worker life skills above 40%.

Standard and non-standard regression coefficients (Table 16).

			Estimate	S.E.	C.R.	P
Social	<−−-	Life skills	,705	,085	8268	***
Psychological	<−−-	Life skills	,910	,096	9497	***
Scientific	<−−-	Life skills	,864	,095	9067	***
Thinking	<−−-	Life skills	1000
Language control	<−−-	Life skills	,865	,099	8732	***
Communication	<−−-	Life skills	,741	,086	8668	***
Patriotism	<−−-	Life skills	,643	,087	7375	***
Q31	<−−-	Thinking	,915	,066	13,760	***
Q21	<−−-	Scientific	,809	,068	11,868	***
Q24	<−−-	Scientific	,736	,072	10,226	***
Q19	<−−-	Scientific	,864	,062	13,870	***
Q22	<−−-	Scientific	1000
Q23	<−−-	Scientific	,909	,063	14,387	***
Q18	<−−-	Scientific	,802	,063	12,652	***
Q20	<−−-	Scientific	,854	,060	14,234	***
Q29	<−−-	Thinking	,776	,061	12,822	***
Q30	<−−-	Thinking	,781	,061	12,770	***
Q32	<−−-	Thinking	1000
Q28	<−−-	Thinking	,942	,063	14,891	***
Q27	<−−-	Thinking	,868	,062	13,977	***
Q34	<−−-	Thinking	,679	,058	11,619	***
Q35	<−−-	Thinking	,901	,065	13,878	***
Q26	<−−-	Thinking	,832	,059	14,175	***
Q25	<−−-	Thinking	,619	,059	10,466	***
Q36	<−−-	Thinking	,883	,065	13,668	***
Q33	<−−-	Thinking	,842	,067	12,586	***
Q15	<−−-	Social	1000
Q13	<−−-	Social	,784	,088	8957	***
Q16	<−−-	Social	,869	,094	9285	***
Q17	<−−-	Social	,992	,092	10,792	***
Q14	<−−-	Social	,863	,089	9661	***
Q10	<−−-	Communication	,670	,086	7758	***
Q8	<−−-	Communication	,823	,079	10,409	***
Q11	<−−-	Communication	,934	,088	10,565	***
Q12	<−−-	Communication	,798	,077	10,325	***
Q9	<−−-	Communication	1000
Q46	<−−-	Psychological	,959	,066	14,616	***
Q49	<−−-	Psychological	,986	,062	15,784	***
Q44	<−−-	Psychological	,637	,069	9299	***
Q47	<−−-	Psychological	,748	,061	12,171	***
Q48	<−−-	Psychological	,854	,060	14,216	***
Q43	<−−-	Psychological	,649	,057	11,295	***
Q45	<−−-	Psychological	1000
Q39	<−−-	Language control	,962	,045	21,249	***
Q37	<−−-	Language control	,736	,040	18,486	***
Q40	<−−-	Language control	,581	,048	11,988	***
Q41	<−−-	Language control	,630	,048	13,232	***
Q38	<−−-	Language control	1000
Q42	<−−-	Language control	,803	,049	16,369	***
Q53	<−−-	patriotism	1000
Q56	<−−-	patriotism	,668	,045	14,699	***
Q51	<−−-	patriotism	,882	,037	23,897	***
Q54	<−−-	patriotism	,871	,035	25,003	***
Q55	<−−-	patriotism	,719	,035	20,469	***
Q50	<−−-	patriotism	,727	,040	18,139	***
Q52	<−−-	patriotism	,906	,038	23,891	***
Q57	<−−-	patriotism	,689	,043	16,024	***

Table 16.

Regression weights: (Group number 1 - Default model).

Confirmatory factor analysis Amos version 23.

Through the table the non-standard regression coefficients i.e. the saturations the rough grades of the questions on the axes that you interpret came all of them are statistically a function (P is less than 5%), the same for all regressions that form between the axes involved in the study on the worker the mother or the basic axis (life skills) where the other regression coefficient her standard came statistically D (P less than 5%).

4.1.1.2 Standardized regression weights

See (Table 17).

			Estimate
Social	<−−-	Life skills	,625
Psychological	<−−-	Life skills	,704
Scientific	<−−-	Life skills	,643
Thinking	<−−-	Life skills	,767
Language control	<−−-	Life skills	,542
Communication	<−−-	Life skills	,662
patriotism	<−−-	Life skills	,431
Q31	<−−-	Thinking	,671
Q21	<−−-	Scientific	,584
Q24	<−−-	Scientific	,502
Q19	<−−-	Scientific	,686
Q22	<−−-	Scientific	,723
Q23	<−−-	Scientific	,713
Q18	<−−-	Scientific	,623
Q20	<−−-	Scientific	,705
Q29	<−−-	Thinking	,623
Q30	<−−-	Thinking	,620
Q32	<−−-	Thinking	,693
Q28	<−−-	Thinking	,730
Q27	<−−-	Thinking	,682
Q34	<−−-	Thinking	,562
Q35	<−−-	Thinking	,677
Q26	<−−-	Thinking	,693
Q25	<−−-	Thinking	,504
Q36	<−−-	Thinking	,666
Q33	<−−-	Thinking	,611
Q15	<−−-	Social	,674
Q13	<−−-	Social	,504
Q16	<−−-	Social	,527
Q17	<−−-	Social	,647
Q14	<−−-	Social	,554
Q10	<−−-	Communication	,419
Q8	<−−-	Communication	,593
Q11	<−−-	Communication	,604
Q12	<−−-	Communication	,586
Q9	<−−-	Communication	,690
Q46	<−−-	Psychological	,724
Q49	<−−-	Psychological	,789
Q44	<−−-	Psychological	,454
Q47	<−−-	Psychological	,598
Q48	<−−-	Psychological	,703
Q43	<−−-	Psychological	,553
Q45	<−−-	Psychological	,713
Q39	<−−-	Language control	,803
Q37	<−−-	Language control	,729
Q40	<−−-	Language control	,523
Q41	<−−-	Language control	,567
Q38	<−−-	Language control	,889
Q42	<−−-	Language control	,667
Q53	<−−-	Patriotism	,868
Q56	<−−-	Patriotism	,604
Q51	<−−-	Patriotism	,836
Q54	<−−-	Patriotism	,859
Q55	<−−-	Patriotism	,766
Q50	<−−-	Patriotism	,703
Q52	<−−-	Patriotism	,836
Q57	<−−-	Patriotism	,644

Table 17.

Standardized regression weights:(Groupnumber1-efaultmodel).

Confirmatory factor analysis Amos version 23.

Through the tables, the standard and non-standard regression coefficients and the graph clearly show that all the questions were saturated on the axes interpreted by significant saturations that exceeded 40%, so that the non- standard regression coefficients all came statistically (P less than 5%), we also note the saturations of factors or axes on their basic factor (life skills) all came at the level where they all exceeded 0.40.

Where all of its non-standard regression coefficients came up as a function statistically (P less than 5%).

4.1.1.2.1 Covariances (covariance) and correlations

See (Table 18).

	Estimate S.E.C.R.P Label
e56–e57	,263,02510,382***
e41–e40	,220,0326,852***
e55–e54	,068,0125,633***

Table 18.

Covariances: (Groupnumber1-Defaultmodel).

Confirmatory factor analysis Amos version 23.

These variances by the table were obtained after modifying the model and this is through the modification indices modification indicators, which is a function statistically (P is less than 5%) this is in order to achieve maximum convergence or congruence between the model and the data, which leads to a better interpretation of the information by the factors thus improve the quality of the match in general (Table 19).

	Estimate
e56–e57	,575
e41–e40	,350
e55–e54	,342

Table 19.

Correlations:(Groupnumber1-Defaultmodel).

Confirmatory factor analysis Amos version 23.

The correlations in the table as well were obtained after modifying the model and this through the indicators of modification indices modification where it was the correlations of these errors in the level leading to a better explanation for information by factors to improve correlations among themselves, which leads to improve the quality of matching (Table 20).

Model	NPAR	CMIN	DF	P	CMIN/DF
Default model	110	2060,401	1165	,000	1769
Saturated model	1275	,000	0
Independence model	50	11,762,350	1225	,000	9602

Table 20.

CMIN.

Confirmatory factor analysis Amos version 23.

We notice from the table that the first line is for our no-planning study form in the second line is the ideal saturated model while the last line is the independent model or null model null model which is based on the assumption that the variances of variables.

The observation at the community level is equal to zero or zero and only the values of the variance of these variables remain (The model with independent variables).

Degree of Freedom = 1165, the value was 2060,401 = CMIN, which is a function statistically.

1769 = CMIN/DF, which is less than 3, so these two indicators are good within the matching indicators absolute fit indices and indicate to what extent the information derived from the model is represented presumably the information contained in the data of the research sample (Table 21).

Model	RMR	GFI	AGFI	PGFI
Default model	,041	,856	,842	,782
Saturated model	,000	1000
Independence model	,195	,255	,225	,245

Table 21.

RMR, GFI.

Confirmatory factor analysis Amos version 23.

We note through a table within the outputs of the study model without a planning axis.

The value ,850 = GFI, which is inappropriate is less than 0.90 within the absolute conformity indicators.

Absolute fit indices.

The ability of the model to estimate its parameters, or the designation of the model Identification Model (Table 22).

Model	NFI delta1	RFI rho1	IFI delta2	TLI rho2	CFI
Default model	,825,816	,916	,911	,915
Saturated model	1000		1000		1000
Independence model	,000	,000	,000	,000	,000

Table 22.

Baseline comparisons.

Confirmatory factor analysis Amos version 23.

We note through the table within the outputs of the study model without the planning axis reached a value of, 910 = TLI and reached a value of, 910 = CFI, which are acceptable values for indicators limited between0.90 and 0.95 within the comparative or incremental Comparative fit matching indicators indices/fit indices incremental (Table 23).

Model	RMSEA	LO 90	HI 90	PCLOSE
Default model	,040	,037	,042	1000
Independence model	,132	,130	,135	,000

Table 23.

RMSEA.

Confirmatory factor analysis Amos version 23.

Through the table: within the outputs of the study model without the planning axis a value of ,040 = RMSEA, which is a good indicator among the indicators of correcting the lack of economy or economic indicators correction indices parsimony (Table 24).

Model	AIC	BCC	BIC	CAIC
Default model	2280,401	2305,959	2742,010	2852,010
Saturated model	2550,000	2846,241	7900,466	9175,466
Independence model	11,862,350	11,873,967	12,072,172	12,122,172

Table 24.

AIC.

Confirmatory factor analysis Amos version 23.

Through a table within the outputs of the study model without a planning axis the economic indicators AICO BCC and BIC of the study model without planning reached lower values compared to the saturated model is a good indicator of the economy, these indicators are among the indicators of correcting the lack of economy or economic indicators correction indices parsimony (Table 25).

Model	ECVI	LO 90	HI 90	MECVI
Default model	4654	4404	4920	4706
Saturated model	5204	5204	5204	5809
Independence model	24,209	23,507	24,924	24,233

Table 25.

ECVI.

Confirmatory factor analysis Amos version 23.

Through a table within: the outputs of the study model without a planning axis.

The ECVI economic index of the study model without planning reached a lower value compared to the saturated model and the model the Independent is a good indicator of the economy, these indicators are among the indicators of correcting the lack of economy or economic indicators correction indices parsimony.

SRMR value was = 0.05, which is a good indicator for the economy among the indicators for correcting the lack of economy or economic indicators correction indices parsimony.

Table represent conformity indicators in favor of the study model after the exclusion of planning and after adjustment (Table 26).

Conformity Indicators	The standard	Value	Judgment
CMINx2	Not signified	2060,40	Signified p = 0.00
D f	Degree of freedom	1165
N c	1–3; Good 3–5; Acceptable	1,76	Good
SRMR	0–0.05; Good 0.08–0.05; Acceptable	0.05	Good
GFI	0.9–0.95; Acceptable	,86	Not suitable
TLI	0.95–1; Good	0.91	Acceptable
CFI	0.95–1; Good	0.92	Acceptable
RMSEA	0–0.05; Good 0.05–0.08; Acceptable	0.04	Good

Table 26.

ECVI.

Confirmatory factor analysis Amos version 23.

Through the results of the conformity indicators shown in the table, it appears that they are appropriate, and this is confirmed by these results obtained by confirmatory factor analysis, where the model showed a good match for the data of the study sample.

The same results as we got earlier: according to which the number of Bootstrap samples in which the matching indicators worked or were good at their level is 9999 out of 10,000 samples, while one sample in which the model matching of the data was inappropriate.

Testing the null hypothesis: through these results, the difference is statistically D p =, 000, which is less than 0.05 (the level of significance), and therefore we reject the null hypothesis, and therefore there are significant differences in favor of the quality and validity of the model, as it showed quality in matching the data at the overwhelming majority.

Through the results we have reached through the confirmatory factor analysis of the proposed model in estimating the modeling of free parameters, as well as testing the quality of the model’s data matching, it is evident that it is suitable for measuring life skills, waiting for the results of the comparison between the two models.

4.1.1.2.2 Make a comparison between the two models

The initial study model that includes all the factors used in factor analysis a c p, the study model without a factor or planning axis (Table 27).

Conformity indicators	The standard	Model (1) study without factor or planning axis		Model (2) study without factor or planning axis
		Value	Judgment	value	judgment
CMIN χ2	Not signified	2499.04	P = 0.00 signified	2060.4	P = 0.00 signified
D f	Degree of freedom	1528		1165
AIC	Used to compare models: the model with the smallest value is the best	2749		2280	Matching the second form to the sample data is better than matching the first form
BCC		2782		2305
BIC		3273		2742
ECVI		5.61		4.65

Table 27.

The study model without a factor or planning axis.

Confirmatory factor analysis Amos version 23.

By comparing the first and second models using the economic conformity indicators that are used to compare the models, we conclude that the second model without the planning axis is the most appropriate to measure life skills because these conformity indicators are at its best, where they have the smallest value, and this confirms the results we obtained in the factor analysis, where the planning axis is not saturated with the rest of the factors within the factor that combines them.

Through all the above, we can point out that the model using confirmatory factor analysis came to confirm what we get in exploratory factor analysis.

Therefore, we can adopt the model in measuring life skills among university students. As follows:

The ability of the model to estimate its parameters, or the designation of the model Identification Model (Table 28).

Factors

Thinking and problem solving

Have sound critical thinking

I have the ability to identify problems specific to my field of specialization

The ability to propose appropriate solutions to each problem.

I have the ability to organize thoughts in a logical way

I have the ability to find alternatives to the problem

I have the ability to think independently.

I have the ability to analyze.

I have the ability to research and experiment.

I can relate educational situations to similar life situations.

I can sense the problem.

I have the ability to accurately identify the problem.

I can collect information about the subject and its parent.

Density skills and patriotism and Identity Respect national symbols.

I defend and protect my homeland.

I work hard to serve my country.

I enjoy the love of the Fatherland.

I am proud to belong to my homeland.

Apply general rules and regulations

I respect the National Law.

I act with the credibility of a national trend.

Psychological skills and self-awareness Psychological and self-awareness

I can control the situations that confront me.

I am often proud of what I do.

I have high self-confidence.

I have the ability to adjust my feelings .

I have the ability to detect other people’s feelings.

I predict the expected situations.

I have the ability of self-reliance.

Language control skills Language control

I understand the meanings of the English language,

I have the ability to discuss in a sound language and present research that is prepared by me in front of the professor and colleagues.

I can translate the basic terms of the specialty from Arabic to French.

I have the ability to express in proper Arabic.

I can intervene in discussions in proper Arabic.

I acquire the skill of translation into multiple languages.

Scientific and technological scientific and technological skills. I can use the computer skillfully.

I keep abreast of modern scientific and technical developments.

I have the ability to use modern technological means.

I adhere to the basics of scientific research and its ethics.

I use different sources to obtain information and knowledge in order to serve the cognitive outcome.

I acquire a diverse knowledge culture.

Gain the skill of using the internet.

Communication and communication //communication skills It’s better to listen to others.

Improve verbal communication.

I express my thoughts clearly.

I use the appropriate vocabulary when talking to others.

Listen attentively to the words of others.

Social skills and working with the group Social and working with the group

I treat others on the basis of tolerance.

I accept and respect another point of view.

I live with other people’s problems.

I have the ability to put the interest of the group over the interest of the individual.

I have the ability to build bonds of trust with others.

Table 28.

Parameters (Questions), or the designation of the model Identification Model.

5. Conclusion

The construction of metrics is one of the important things that have become used today in scientific research and through which credible results are obtained.

The construction of the life skills scale has passed through two important stages, the first stage was represented by the two exploratory factors and the second stage was represented by the confirmatory factor analysis, through which we reached the construction of two models using economic conformity indicators that are used to compare the models, where we concluded that the second model without the planning axis is the most appropriate to measure life skills because these conformity indicators in its efficiency where it has the smallest value.

From the foregoing, we can point out that the model using confirmatory factor analysis came to confirm what we get in exploratory factor analysis. Therefore, we can adopt the model in measuring the life skills of university students.

Abbreviations

SRMR	Standardized Root Mean squared Residual
DF	Degree of freedom
S.E	Standard Error human serum
CR	Critical Ratio
NPAR	Number of Parameters for each model
RMS	Root mean square residual
GFI	Goodness of Fit Index
AGFI	Adjusted Good ness of Fit Index