The independent variables, dependent variables, and demographic control variables underwent a Pearson coefficient correlation to examine their relationships. Table 4.1 displays the means, standard deviations, and correlation values of these variables. The results of the correlation analysis show that with the exception of gender and the prior cultural exposure sub-dimension of language study, all of the independent variables and control variables are significantly related to the respondents’ ICS scores.
48 Table 4.1
Correlation Results
Variable Mean SD 1 2 3 4 5 6 7 8 9 10
1 ICS Total Score 3.857 .590 -
2 Background
Settinga .686 .466 .236** -
3 Prior Cultural
Exposure 2.064 .605 .528*** .243** - 4 Cultural
Experiences 2.393 .837 .573*** .208* .912*** - 5 Cross Narrative
Experience 1.421 .717 .294** .108 .641*** .357*** -
6 Language Study 2.047 .649 .163 .251** .669*** .425*** .363*** -
7 Genderb .769 .423 .087 -.118 .026 .051 .040 -.082 -
8 Intended Work
Settingc .868 .340 .316*** .367*** .242** .213* .185* .154 -.098 -
9 Age 30.983 10.022 .336*** .149 .154 .263** -.040 -.059 .052 .031 -
10 Education 1.802 .737 .263** .132 .334*** .312*** .201* .217* -.041 .094 .330*** -
aCoding: 0 = rural, 1 = non-rural
bCoding: 0 = male, 1 = female
cCoding: 0 = rural, 1 = non-rural
* p < .05 **p < .01 ***p < .001
49
Hypothesis Testing
Hypotheses H1, H3, and H4 use categorical variables to measure the independent variables.
Because of this, these hypotheses were tested using a T-test to compare means between groups.
Hypotheses H2, H2-1, H2-2, and H2-3 use continuous variables and were tested through hierarchical regression by applying the following formula where the values b1, b2, and b3 are the partial regression coefficients and the intercept b0 is the regression constant.
( ) ( ) ( )
The main level hypothesis H2was tested separately from its sub-hypotheses. The results of the hypothesis testing for H2 and forH2-1, H2-2, and H2-3are displayed in tables 4.4 and 4.5 respectively.
This study sought to investigate the connection, if any, between a nursing student’s background setting and their ICS scores. Therefore to test for this relationship the following hypothesis was stated:
H1: Students from an urban or metropolitan background have higher levels of ICS than students from rural areas.
The T-test conducted for H1 compared non-rural and rural background settings means against ICS levels. There was a significant difference in the ICS means between non-rural (M=3.951, SD=0.534) and rural (M=3.651, SD=0.659) responses: t(119)=-2.655, p<.01. These results are displayed in Table 4.2 and show that students from non-rural background settings are indeed different in their ICS levels from students of rural background. Specifically, the results suggest that nursing students from urban and metropolitan areas have higher levels of ICS.
Therefore it is suggested that background setting does have some effect on ICS levels thereby supporting H1.
The next categorically based hypothesis of this study looks at the effects of gender on ICS scores. The following hypothesis was stated to test for this relationship:
50
H3: Female students have a higher ICS levels than their male counterparts.
The T-test for H3 compared gender means against ICS. There was not a significant difference in the ICS means between male (M= 3.763, SD= .654) and female (M= 3.884, SD= .570) responses: t(119)= -.954, p>.05. These results indicate that gender is not significant in predicting ICS scores. Table 4.2 displays the results of this t-test. This finding does not support the hypothesis that females will score higher in ICS scales than males.
The final T-test compared the means of the intended work setting variable to test relationship, if any, between a nursing student’s intended work setting and their ICS scores. To test this connection the following hypothesis was stated:
H4: Students intending to work in urban areas or large cities have higher levels of ICS than students seeking employment in rural areas.
There was a significant difference in the ICS means between non-rural (M= 3.929, SD=
0.526) and rural (M= 3.380, SD= .766) responses: t(119)= -2.770, p<.05. These results are displayed in Table 4.2. The results of this test indicate that the difference between students intending to work in non-rural areas over rural areas is significant. Simply put, students intending to work in urban and metropolitan areas will score higher on ICS scales thereby support H4.
51 Table 4.2.
T-test Results for Independent Variables' Effects on ICS
Variable Category Means t df p
The second hypothesis for this study uses a continuous variable to measure prior cultural exposure. This hypothesis was tested using the previously stated hierarchical regression formula.
In the first model education and age are controlled for and the independent variables are included in the second model. Because background setting, gender, and intended work setting are categorical variables they were coded with dummy variables to perform this test. Table 4.3 shows the regression results for prior cultural exposure. When predicting ICS scores it was found that prior cultural exposure (β = 0.424, p < .001) was a significant predictor. This indicates that students with prior cross cultural experience will score higher on ICS scales thus supporting H2. It should be noted that the regression results also indicate that intended work setting is a significant predictor of ICS scores (β = 0.202, p < .05) thus further supporting the t-test findings for H4.
52 Table 4.3.
Results of Hierarchical Regression Analysis on Predictors of ICS
Variable Standardized Coefficients
cIntended Work Setting 0.202*
F 9.517*** 12.212***
This study also sought to test new scales for assessing prior cultural exposure and to test them on a dimensional level for predictive qualities against ICS scores. Hypotheses H2-1, H2-2, and H2-3 represent these scales. Table 4.4 presents the results for these hypotheses. As expected the results show that there is a very strong and significant relationship between cultural immersion experience and ICS scores (β=0.461, p<.001). Cross-narrative experience and language study did not show strong predictive relationships with ICS scores: (β=0.127, p>.05) and (β=-.114, p>.05) respectively. It should be noted that in table 4.2 cross narrative experience is correlated with ICS scores with a high significance score (r=.294, p<.01). This correlation was
53
explored further by testing cross narrative experience as a moderator but the results were not significant.
Table 4.4.
Results of Hierarchical Regression Analysis on Prior Exposure Dimensional Level Predictors of ICS
Variable Standardized Coefficients
Model 1 Model 2
Education 0.171 0.035
Age 0.280** 0.183**
aBackground Setting 0.063
Prior Exposure - Immersion Experience 0.461***
Prior Exposure - Narrative Experience 0.127
Prior Exposure - Language Study -0.114
bGender 0.067
cIntended Work Setting 0.187**
F 9.517*** 10.712***
∆F 9.517*** 9.706***
R2 0.139 0.433
∆R2 0.139 0.294
Adjusted R2 0.1242 0.393
aCoding: 0 = rural, 1 = non-rural
bCoding: 0 = male, 1 = female
cCoding: 0 = rural, 1 = non-rural
*p < .05
**p < .01
***p < .001
54
This study involved seven hypotheses. The results of the hypotheses testing show that four hypotheses were supported by the findings. Table 4.5 shows the results of the hypothesis testing.
Table 4.5.
Results of Hypotheses Testing
Hypothesis Results
H1: Students from an urban or metropolitan background have higher levels
of ICS than students from rural areas. Accepted
H2: Prior cultural exposure has a positive influence on students’ levels of
ICS. Accepted
H2-1: Cultural immersion experience has a positive influence on
students’ levels of ICS. Accepted
H2-2: Cross-narrative experience has a positive influence on students’
levels of ICS. Rejected
H2-3: Language study has a positive influence on students’ levels of ICS. Rejected
H3: Female students have a higher ICS levels than their male counterparts. Rejected H4: Students intending to work in urban areas or large cities have
higher levels of ICS than students seeking employment in rural areas.
Accepted
Post Hoc Analysis of New ICS Dimensions
The published ICS scale used in this study did not retain its original factor structure but instead reduced to three dimensions. Because of this structure change a post hoc analysis was conducted on the new ICS dimensions and the independent variables to explore their relationships. Table 4.6 shows the results of the independent t-tests for the dichotomized independent variables and the new ICS dimensions.
55 Table 4.6.
T-test Results for Independent Variables' Effects on New ICS Dimensions
Respect for Cultural Differences
Note. Standard Deviations appear in parentheses next to the means (continued)
56
Note. Standard Deviations appear in parentheses next to the means
The results of Table 4.6 do not reveal any significant or unexpected relationships between the dichotomized independent variables and the new ICS dimensions. The results showed that students from non-rural backgrounds would have higher scores across all dimensions than students from rural backgrounds. Likewise, students intending to work in non-rural areas also had higher scores in all dimensions than their counterparts. Gender did not show any significant effects on scores for any dimension.
Just as was done for the hypothesis test, hierarchical regression was used to explore the relationship between the continuous variable of prior cultural exposure and the new ICS dimensions. Table 4.7 displays the results of the regression. The strongest predictor for all of the new ICS dimensions was prior exposure. Prior exposure showed to have the strongest effect on scores within the interaction presence dimension (β=.424, p<.001). It should be noted that intended work setting also showed some predictive power in this dimension as well (β=.251, p<.01). No other significant or unexpected relationships arose from the regression results.
57 Table 4.7.
Results of Hierarchical Regression Analysis on Predictors of ICS
Variable
Respect for Cultural Differences
Interaction Surety Interaction Presence
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Education 0.113 -0.005 .258** .116 .135 .024*
Age .228* .191* .256** .236** .290** .266**
aBackground Setting 0.05 .006 .016
bPrior Exposure .352*** .398*** .424***
cGender .190* -.013 .015
Intended Work Setting 0.149 .152 .251**
F 5.269** 7.525*** 12.560*** 10.966*** 8.661*** 12.685***
∆F 5.269** 8.025*** 12.560*** 8.560*** 8.661*** 12.944***
R2 0.082 0.284 .176 .366 .128 .400
∆R2 0.082 0.202 .176 .190 .128 .272
Adjusted R2 0.066 0.246 .162 .333 .113 .369
aCoding: 0 = rural, 1 = non-rural
bCoding: 0 = Male, 1 = Female
cCoding: 0 = rural, 1 = non-rural
*p < .05
**p < .01
***p < .001
58
59