Individual characteristics of the participants can be seen in Table 2:
Table 2. Individual characteristics of the participants
Variables
|
Items
|
Frequency (percentage) of student )n=142)
|
Frequency (percentage) of staff
(n=123)
|
Age
|
18-24year
|
7(4.9%)
|
0(0%)
|
25-34 year
|
62(43.7%)
|
19(15.4%)
|
35-44 year
|
63(44.4%)
|
61(49.6%)
|
45-54 year
|
10(7.0%)
|
36(29.3%)
|
55-64 year
|
0(.0%)
|
7(5.7%)
|
Sex
|
Male
|
58(40.8%)
|
80(66.7%)
|
Female
|
84(59.2%)
|
40(33.3%)
|
Missing
|
0(0%)
|
3(0.02%)
|
Experience_history
|
<=1 year
|
113(79.6%)
|
28(22.8%)
|
>1 year
|
29(.20%)
|
95(0.67%)
|
Degree
|
BA
|
29(20.4%)
|
57(48.3%)
|
MA
|
39(27.5%)
|
61(51.7%)
|
Professional doctorate
|
45(31.7%)
|
0(.0%)
|
PhD
|
29(20.4%)
|
0(.0%)
|
Missing
|
0(0%)
|
5(0.4%)
|
Score
|
0
|
6(4.2%)
|
0(0%)
|
20
|
37(26.1%)
|
18(14.6%)
|
40
|
39(27.5%)
|
43(35.0%)
|
60
|
34(23.9%)
|
32(26.0%)
|
80
|
22(15.5%)
|
23(18.7%)
|
100
|
4(2.8%)
|
7(5.7%)
|
As shown the amount of knowledge in the student group at age 25 – 44 year and the age range 35-44 year in the group of staff have had the highest score.
Also, the level of knowledge in the student group of female and male in the group of staff was higher. The work experience in the group of staff was also higher. Most of the staff have masters and most Ph.D. students. In both groups, they won the most points at 40 points.
Table 3: Mean and standard deviation of participants’ hours of scientific and non-scientific studies across fields of study in two groups of students and staff.
Non-scientific-hours studying
|
Scientific-hours studying
|
Participant
|
Mean ± SD
|
Mean ± SD
|
3.13±0.92
|
3.79±0.59
|
Student(n=142)
|
2.70±0.82
|
3.13±0.89
|
Staff(n=123)
|
0.001
|
<0.001
|
p-value
|
As can be seen, there was a significant correlation between the mean hours of scientific study and the mean hours of the non - scientific study of students and staff.
Investigating the relationship of knowledge score with each variable was studied individually (simple analysis) and once analyzed in the GLM modeling (multiple analyses). These results are as follows:
Table 4. Comparison of the mean scores of knowledge of staff and students in terms of age group, gender, background, degree, field, number of hours of scientific and non-scientific study
|
scores of knowledge of participant
|
|
Variables
|
Items
|
Staff (n=123)
|
Students (n=143)
|
|
|
|
ni
|
Mean
|
SD
|
ni
|
Mean
|
SD
|
p-value1
|
Age
|
18-24 year
|
0
|
|
|
7
|
2.2857
|
1.38013
|
-
|
25-34 year
|
19
|
2.4737
|
1.07333
|
62
|
2.0806
|
1.20516
|
0.206
|
35-44 year
|
61
|
2.5902
|
1.16013
|
63
|
2.4286
|
1.20100
|
0.448
|
45-54 year
|
36
|
2.8889
|
1.08963
|
10
|
2.7000
|
1.15950
|
0.635
|
55-64 year
|
|
2.5714
|
.97590
|
|
|
|
--
|
p-value
|
.511
|
.284
|
Sex
|
|
p-value3
|
Male
|
80
|
2.6000
|
1.06260
|
58
|
2.2069
|
1.16617
|
0.041
|
Female
|
40
|
2.7750
|
1.25038
|
84
|
2.3452
|
1.24662
|
0.075
|
p-value4
|
.425
|
.506
|
experience_history
|
|
Pvalue5
|
<=1year
|
28
|
2.4643
|
.99934
|
113
|
2.1947
|
1.17912
|
0.267
|
>1 year
|
95
|
2.716
|
1.145
|
29
|
2.667
|
1.278
|
0.809
|
p-value6
|
.172
|
.140
|
degree
|
|
Pvalue7
|
BA
|
57
|
2.5965
|
1.09967
|
29
|
2.1034
|
1.26335
|
0.065
|
MA
|
61
|
2.7213
|
1.15659
|
39
|
2.0256
|
1.08790
|
0.003
|
Professional doctorate
|
0
|
|
|
45
|
2.6000
|
1.21356
|
|
PhD
|
0
|
|
|
29
|
2.3448
|
1.26140
|
|
p-value8
|
550
|
.135
|
scientific_hours_studying
|
|
Pvalue9
|
<1
|
18
|
|
|
11
|
|
|
0.976
|
1-3
|
35
|
2.7143
|
.04520
|
12
|
2.3333
|
1.30268
|
0.312
|
3-5
|
20
|
2.7000
|
1.41793
|
3
|
2.3333
|
1.52753
|
0.683
|
>5
|
49
|
2.6735
|
1.14360
|
46
|
2.2586
|
1.22383
|
0.044
|
Pvalue10
|
.987
|
.976
|
Non-scientific_hours_studying
|
|
Pvalue11
|
<1
|
44
|
2.568
|
1.246
|
33
|
2.879
|
1.166
|
0.270
|
1-3
|
46
|
2.6957
|
1.19014
|
35
|
2.1714
|
1.07062
|
0.044
|
3-5
|
22
|
2.7273
|
1.03196
|
15
|
2.0000
|
1.25357
|
0.062
|
>5
|
20
|
2.1500
|
.74516
|
48
|
2.2083
|
1.25407
|
0.813
|
Pvalue12
|
0.128
|
0.839
|
|
|
|
|
|
|
|
|
|
|
|
Note that:
- To compare the mean scores of knowledge in the analysis of student data and staff data in each age group, the independent sample was used.
- One-way ANOVA test was used to compare the mean scores of knowledge of big data analysis of each group in different age groups.
- To compare the mean score of big data analysis of students and staff in any gender of the independent test sample was used.
- To compare the mean score of big data analysis of each of the groups in two different genders of the independent test sample was used.
- To compare the mean score in big data analysis of students and staff in each category of work experience of the independent test sample was used.
- The independent tests were used to compare the mean scores of knowledge in the analysis of student data and staff data at each degree.
- To compare the mean score in big data analysis of each of the groups in different sections. Of one-way ANOVA was used.
- To compare the mean score in big data analysis of students and staff in each row from the scientific study hours of the independent test sample was used.
- To compare the mean score of big data analysis of each of the groups in the scientific study hours of one-way ANOVA was used
- To compare the mean score in big data analysis of students and staff in each row of non-scientific study of the independent test sample was used.
- To compare the average score of big data analysis of each of the groups in non-scientific study groups hours of one-way ANOVA was used.
As seen in Table 4 a significant difference between the average score of staff and students was not seen in any of the levels of age group. Also, there was no significant difference between the mean scores of staff's knowledge at different levels of age group. Also for students although the effect of the age group on the comparison of the average score of knowledge of both groups of staff and students was adjusted with stratification. However, there was no significant difference between the mean scores of knowledge in these two groups.The higher average level of knowledge of the male staff is significant male students.The average of the knowledge score in staff of the master's degree was also observed compared to the master's students. Significant differences in staff knowledge score with more than 5 hours of scientific study in comparison with the knowledge of the students with more than 5 hours of scientific study are remarkable.
General Linear Model results
Table 5. Investigating the relationship between the variables group (staff, students), gender, age, work experience (less than one year and more than one year) degree, the number of scientific and non-scientific study hours daily with a score of big data analysis.
Source
|
Type III Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.*
|
|
86.461a
|
51
|
1.695
|
1.280
|
.143
|
|
24.911
|
1
|
24.911
|
18.809
|
.000
|
scientific_hours_studying
|
.520
|
1
|
.520
|
.393
|
.532
|
non_scientific_hours_studying
|
.015
|
1
|
.015
|
.012
|
.915
|
group
|
3.635
|
1
|
3.635
|
2.745
|
.100
|
age
|
.407
|
2
|
.203
|
.153
|
.858
|
sex
|
1.095
|
1
|
1.095
|
.827
|
.365
|
experience_history
|
3.600
|
3
|
1.200
|
.906
|
.441
|
degree
|
7.598
|
3
|
2.533
|
1.912
|
.132
|
group * experience_history
|
.072
|
1
|
.072
|
.054
|
.817
|
group * degree
|
.892
|
1
|
.892
|
.673
|
.414
|
age * sex
|
.338
|
2
|
.169
|
.128
|
.880
|
age * experience_history
|
5.093
|
4
|
1.273
|
.961
|
.432
|
age * degree
|
11.008
|
5
|
2.202
|
1.662
|
.150
|
sex * experience_history
|
8.579
|
2
|
4.289
|
3.239
|
.043
|
sex * degree
|
9.245
|
3
|
3.082
|
2.327
|
.079
|
experience_history * degree
|
2.248
|
4
|
.562
|
.424
|
.791
|
group * age * sex
|
.000
|
0
|
.
|
.
|
.
|
group * age * experience_history
|
.000
|
0
|
.
|
.
|
.
|
To investigate the effect of different variables such as age and gender, etc. on the level of knowledge of individuals about the big data the General Linear Model was used.
Table 5 shows that none of the variables studied individually did not have any effect on the points obtained in the field of knowledge, however, the interaction between gender and work experience is an influential factor.
That no significant for related main effects could not be presented interpretation for them.
Assessment of the rating according to the age group in the two groups is shown in the diagrams below.
Figure 1. Evaluated scores by age group in two groups.
According to the figure1 average score in the age group of 35 to 44 years old among men students less than other age groups, although there was no significant difference seems to have not shown the statistical tests.
The average score of knowledge in other age groups of women is not seen as much difference in students and staff.
Evaluate of knowledge scores in terms of the level of work experience in two groups.
Figure 2. evaluate of knowledge in terms of the level of work experience in two groups
According to Figure 2, the average score in people with work experience of one to three is less than the other age groups
Although statistical tests did not show any significant difference.
The average score of knowledge in other groups of work experience was not seen much difference in students and staff.
Evaluate of knowledge scores in terms of gender and level of experience in the two groups.
Figure 3. evaluate of knowledge in terms of gender and level of experience in the two groups.
According to left figure 3,the average score in people who have had work experiences of between one and three and were men was less than the rest,it seems that although statistical tests did not show significant differences.The average scores of knowledge in other groups of history and gender were not seen much difference in students and staff.
According to right figure 3, the average score in people who have a history of between 3 and 5 And have been female. The average score of knowledge in other groups of history and gender is not seen much difference in students and staff.
evaluate of knowledge by gender and age group in two groups.
Figure 4. evaluate of knowledge by gender and age group in two groups
According to left figure 4,the average score in people who have been ages 34-25 And female have been less than others.
Statistical tests did not show a significant difference, though.The average score of knowledge in other age groups and gender is not seen much difference in students and staff.
According to right figure 4, the average score for people aged between 54 and 35 was less than the rest.
It seems that although statistical tests do not show significant differences. The average score of knowledge in other age groups and gender is not seen much difference in students and staff.