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Table 4.29
NLP Descriptives for Different Levels of Expert Teaching Style

Expert
Statistic
Std. Error
NLP
Low
Mean
132.7857
3.40520

95% Confidence Interval for Mean
Lower Bound
125.4292

Upper Bound
140.1422

5% Trimmed Mean
132.9286

Median
132.0000

Variance
162.335

Std. Deviation
12.74108

Minimum
110.00

Maximum
153.00

Range
43.00

Interquartile Range
14.00

Skewness
-.224
.597

Kurtosis
.317
1.154

Moderate
Mean
144.7345
1.02644

95% Confidence Interval for Mean
Lower Bound
142.7008

Upper Bound
146.7683

5% Trimmed Mean
144.4567

Median
143.0000

Variance
119.054

Std. Deviation
10.91118

Minimum
120.00

Maximum
210.00

Range
90.00

Interquartile Range
12.00

Skewness
1.753
.227

Kurtosis
10.632
.451
a. NLP is constant when Expert = High. It has been omitted.

Table 4.30
NLP Descriptives for Different Levels of Formal Authority Teaching Style

Formal authority
Statistic
Std. Error
NLP
Low
Mean
141.0313
1.00421

95% Confidence Interval for Mean
Lower Bound
139.0376

Upper Bound
143.0249

5% Trimmed Mean
141.3727

Median
142.0000

Variance
96.810

Std. Deviation
9.83918

Minimum
110.00

Maximum
163.00

Range
53.00

Interquartile Range
12.00

Skewness
-.582
.246

Kurtosis
1.317
.488

Moderate
Mean
150.8788
2.34147

95% Confidence Interval for Mean
Lower Bound
146.1094

Upper Bound
155.6482

5% Trimmed Mean
149.5051

Median
152.0000

Variance
180.922

Std. Deviation
13.45074

Minimum
135.00

Maximum
210.00

Range
75.00

Interquartile Range
14.00

Skewness
2.608
.409

Kurtosis
11.211
.798

Table 4.31
NLP Descriptives for Different Levels of Personal Model Teaching Style

Personal model
Statistic
Std. Error
NLP
Low
Mean
142.1635
.96592

95% Confidence Interval for Mean
Lower Bound
140.2478

Upper Bound
144.0791

5% Trimmed Mean
142.6068

Median
143.0000

Variance
97.031

Std. Deviation
9.85045

Minimum
110.00

Maximum
163.00

Range
53.00

Interquartile Range
12.50

Skewness
-.739
.237

Kurtosis
1.368
.469

Moderate
Mean
149.3200
3.25101

95% Confidence Interval for Mean
Lower Bound
142.6102

Upper Bound
156.0298

5% Trimmed Mean
147.4333

Median
148.0000

Variance
264.227

Std. Deviation
16.25505

Minimum
132.00

Maximum
210.00

Range
78.00

Interquartile Range
19.50

Skewness
2.184
.464

Kurtosis
7.358
.902

Table 4.32
NLP Descriptives for Different Levels of Facilitator Teaching Style

Facilitator
Statistic
Std. Error
NLP
Low
Mean
139.3750
1.69548

95% Confidence Interval for Mean
Lower Bound
135.9641

Upper Bound
142.7859

5% Trimmed Mean
139.6019

Median
142.0000

Variance
137.984

Std. Deviation
11.74666

Minimum
110.00

Maximum
163.00

Range
53.00

Interquartile Range
13.75

Skewness
-.365
.343

Kurtosis
.652
.674

Moderate
Mean
146.0247
1.21398

95% Confidence Interval for Mean
Lower Bound
143.6088

Upper Bound
148.4406

5% Trimmed Mean
145.4108

Median
145.0000

Variance
119.374

Std. Deviation
10.92586

Minimum
125.00

Maximum
210.00

Range
85.00

Interquartile Range
14.00

Skewness
2.434
.267

Kurtosis
13.657
.529

Table 4.33
NLP Descriptives for Different Levels of Delegator Teaching Style

Delegator
Statistic
Std. Error
NLP
Low
Mean
130.0000
2.99537

95% Confidence Interval for Mean
Lower Bound
123.0927

Upper Bound
136.9073

5% Trimmed Mean
129.8333

Median
130.0000

Variance
80.750

Std. Deviation
8.98610

Minimum
120.00

Maximum
143.00

Range
23.00

Interquartile Range
17.50

Skewness
.312
.717

Kurtosis
-.991
1.400

Moderate
Mean
144.5667
1.02304

95% Confidence Interval for Mean
Lower Bound
142.5410

Upper Bound
146.5924

5% Trimmed Mean
144.3981

Median
143.5000

Variance
125.592

Std. Deviation
11.20679

Minimum
110.00

Maximum
210.00

Range
100.00

Interquartile Range
14.00

Skewness
1.275
.221

Kurtosis
9.667
.438

4.2.3.3. Tests of Normality
Since the teaching styles are categorized into low, moderate, and high levels, each teaching style is considered as a nominal variable. Moreover, as the NLP is also on an interval scale, the choice of statistic to measure the relationship between one nominal variable and one interval variable is eta. However, since the frequencies of some of the styles levels are very low, it was decided to choose non-parametric Kruskal Wallis and Mann Whitney tests to compare the levels of each style in terms of NLP scores. T
he reason for choosing non-parametric tests was that the test of normality results in Tables 4.34 to 4.38 indicated non-normality of the data (p .05).

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Table 4.34
Tests of Normality Regarding Expert Style

Expert
Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
NLP
Low
.240
14
.028
.890
14
.080

Moderate
.101
113
.007
.872
113
.000
a. Lilliefors Significance Correction
b. NLP is constant when Expert = High. It has been omitted.

Table 4.35
Tests of Normality Regarding Formal Authority Style

Formal authority
Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
NLP
Low
.087
96
.068
.964
96
.009

Moderate
.203
33
.001
.752
33
.000
a. Lilliefors Significance Correction

Table 4.36
Tests of Normality Regarding Personal Model Style

Personal model
Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
NLP
Low
.090
104
.039
.958
104
.002

Moderate
.160
25
.098
.787
25
.000
a. Lilliefors Significance Correction

Table 4.37
Tests of Normality Regarding Facilitator Style

Facilitator
Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
NLP
Low
.109
48
.200*
.960
48
.105

Moderate
.126
81
.003
.823
81
.000
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction

Table 4.38
Tests of Normality Regarding Delegator Style

Delegator
Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
NLP
Low
.200
9
.200*
.860
9
.095

Moderate
.109
120
.001
.882
120
.000
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction

4.2.3.4. Final Results
Tables 4.39 to 4.43 present the results on the comparison of total NLP scores across the categories of teaching styles. Evidently, the categories of all teaching styles except the Personal Model in terms of NLP are significantly different from one another. In other words, except for the Personal Model there is a significant relationship between different teachers’ styles and NLP. A closer look at the descriptive statistics of these teaching styles reveals that the moderate category of the above teaching styles are of higher NLP in comparison to their low categories. This indicates that there is a positive relationship between teachers’ Expert, Formal Authority, Facilitator, and Delegator styles and NLP.
Table 4.39
Comparing NLP across Categories of Expert

Table 4.40
Comparing NLP across Categories of Formal Authority

Table 4.41
Comparing NLP across Categories of Personal Model

Table 4.42
Comparing NLP across Categories of Facilitator

Table 4.43
Comparing NLP across Categories of Delegator

4.2.4. Testing the Third Null Hypothesis
H03: There is no significant relationship between teachers’ autonomy and NLP (Neuro-linguistic programming).

4.2.4.1. Assumption of Linearity

In order to test above null hypothesis, correlational measures needed to be employed. Pearson product moment correlation and Spearman rho were two options; however, to choose between these two measures, some assumptions needed to be checked in advance. The first of these the linearity of the relationship between NLP and autonomy, which was done by drawing the scatter graph (Figures 4.1 to 4.3). As the figures display, it seems that the two variables’ data are approximately aligned along a straight line; however, the lines are not very diagonal. Therefore, despite the linearity of the relationship, a low correlation coefficient is expected between the two variables.

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Figure 4.1. General Autonomy Scatter Plot

Figure 4.2. Curriculum Autonomy Scatter Plot

Figure 4.3. Total Autonomy Scatter Plot

4.2.4.2. Assumption of Normality

The next assumption is to do with the normality of the data, which was investigated employing Kolmogorov-Smirnov and Shapiro-Wilk tests of normality, whose results in Table 4.44 indicate that the data were not normally distributed (p .05).

Table 4.44
Tests of Normality
Tests of Normality

Kolmogorov-Smirnova
Shapiro-Wilk

Statistic
df
Sig.
Statistic
df
Sig.
General Autonomy
.115
129
.000
.971
129
.007
Curriculum Autonomy
.123
129
.000
.961
129
.001
Total Autonomy
.100
129
.003
.979
129
.042
NLP
.100
129
.003
.905
129
.000
a. Lilliefors Significance Correction

4.2.4.3. Final Results
With regard to the fact that data were not normally distributed, the choice of statistic became Spearman rho, whose results in Table 4.45 show that there is almost no significant relationship between the two variables. In fact, NLP is only significantly and positively correlated with General autonomy with small to medium effect size (p 05). In other words, the null hypothesis is mainly supported; that is to say, except for General autonomy, there is no significant relationship between teachers’ Total and Curriculum autonomy and NLP (Neuro-Linguistic Programming).

Table 4.45
Correlations among Curriculum, General and Total Autonomy and NLP

NLP
General Autonomy
Curriculum Autonomy
Total Autonomy
Spearman’s rho
NLP
Correlation Coefficient
1.000
.205*
-.028
.103

Sig. (2-tailed)
.
.020
.757
.246

N
129
129
129
129

General Autonomy
Correlation Coefficient
.205*
1.000
.245**
.807**

Sig. (2-tailed)
.020
.
.005
.000

N
129
129
129
129

Curriculum Autonomy
Correlation Coefficient
-.028
.245**
1.000
.728**

Sig. (2-tailed)
.757
.005
.
.000

N
129
129
129
129

Total Autonomy
Correlation Coefficient
.103
.807**
.728**
1.000

Sig. (2-tailed)
.246
.000
.000
.

N
129
129
129
129
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

4.2.4. Testing the Fourth Null Hypothesis
H04: There is no significant difference between EFL teachers’ teaching styles and NLP in predicting autonomy?

In order to test this hypothesis, multiple regression analysis was employed three times for the three Total, General, and Curriculum autonomy scores. The variables whose predictive powers are supposed to be examined are the 5 teaching styles and NLP. Employing multiple regression requires checking several assumptions which are initially checked in the following.

4.2.4.1. Assumption of Multicollinearity

The first assumption is to do with multicollinearity, which is investigated by considering the correlation among the independent variables. Tables 4.46 to 4.48 indicate the great majority of the correlations amon
g


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