0 respectively

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).

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.

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