The Responses to Compliments - Research Paper Example

Data Analysis Student’s name: Instructor’s Name: Class Name and Code: University: Date of Submission: Data Analysis This chapter explores the results of the study, which examines the responses of 64 Saudi female lecturers and 62 Saudi female students to compliments. Descriptive statistics are used to elicit differences between the older generation lecturers and younger generation students. Moreover, charts will be extensively used to compare the two groups of participants. The results address each research question as presented in the previous chapter. Results for Research Question 1: Major Compliment Responses Types. The first research question explores the major compliment types among Saudi females. Comparison between the Saudi female lecturers and female students is done in six scenarios. In these six scenarios, we examine the major compliment responses type and whether the responses differ between lectures and students. Table 1 below shows the frequency of each responses type for both Saudi female lecturers and Saudi female students. Table 1: Lecturers vs. Student Responses to a Compliment on their Possession. Compliment Responses Type Profession Total Lecturer Student Compliment on a possession 1. Appreciation Token: "e.g thanks or a nod" Count 18a 8b 26 % within Profession 28.1% 12.9% 20.6% 2. Compliment Acceptance: "e.g I like it too" Count 18a 27a 45 % within Profession 28.1% 43.5% 35.7% 3. Return: "e.g Yours looks nice too" Count 24a 21a 45 % within Profession 37.5% 33.9% 35.7% 4. Scale down: "e.g It's not that expensive" Count 1a 2a 3 % within Profession 1.6% 3.2% 2.4% 5. Question: "e.g Are you sure?" Count 3a 3a 6 % within Profession 4.7% 4.8% 4.8% 6. Disagreement: "e.g I don't like it" Count 0a 1a 1 % within Profession 0.0% 1.6% 0.8% Total Count 64 62 126 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 7.150a 5 .210 Likelihood Ratio 7.654 5 .176 N of Valid Cases 126 a. 6 cells (50.0%) have expected count less than 5. The minimum expected count is .49. As table 1 shows, among the lecturers the most frequent compliment response is “Return” with a frequency of 24 responses which constitutes 37.5 per cent. Appreciation token and Compliment Acceptance compliment responses have the same frequency of 28.1 per cent, Question compliment response has the least frequency at 4.7 per cent. For students, the most frequent compliment response is Compliment Acceptance, with a frequency of 43.5 per cent. The second major response type is Return with a frequency of 33.9 per cent. Appreciation Token, Scale down, Question and Disagreement have frequencies of 12.9 per cent, 3.2 per cent, 4.8 per cent and 1.6 per cent respectively. The bar chart below shows the compliment response on Saudi female lecturers and students when complimented on their written work. Figure 1: Lecturers vs. Student Responses to a Compliment on their Language Ability (Written). Table 2 below shows the frequency of each responses type for both Saudi female lecturers and Saudi female students. Table 2: Lecturers vs. Student Responses to a Compliment on their Language Ability (Written). Compliment Responses Type Profession Total Lecturer Student Compliment on your language ability 1. Appreciation Token: "e.g thanks or a nod" Count 12a 9a 21 % within Profession 19.4% 15.0% 17.2% 2. Compliment Acceptance: "e.g I like it too" Count 28a 15b 43 % within Profession 45.2% 25.0% 35.2% 3. Return: "e.g Yours looks nice too" Count 14a 24b 38 % within Profession 22.6% 40.0% 31.1% 4. Scale down: "e.g It's not that expensive" Count 1a 1a 2 % within Profession 1.6% 1.7% 1.6% 5. Question: "e.g Are you sure?" Count 7a 11a 18 % within Profession 11.3% 18.3% 14.8% Total Count 62 60 122 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 7.849a 4 .097 Likelihood Ratio 7.949 4 .093 N of Valid Cases 122 a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is .98. Table 2 shows that for the lecturers the major response to language compliment is Compliment Acceptance which has a frequency of 45.2 per cent. Return and Appreciation Token has almost the same frequency, however, Return has a higher frequency of 22.6 per cent compared to 19.4 per cent for Appreciation Token. Scale down and Question compliment response has 1.6 per cent and 11.3 per cent respectively. Students prefer to use Return compliment response compared to lecturers who majorly use Compliment Acceptance. From table 2, Return compliment responses has 40 per cent for students. Another 25 per cent of students uses Compliment Acceptance response, while 15 per cent of students uses Appreciation Token, when responding to compliment on language ability. 1.7 per cent and 18.3 per cent of students uses Scale down and Question compliment responses type respectively. The bar chart below shows the compliment response on Saudi female lecturers and students when complimented on their speech. Figure 2: Lecturers vs. Student Responses to a Compliment on their Language Ability (Spoken). Table 3 below shows the frequency of each responses type for both Saudi female lecturers and Saudi female students. Table 3: Lecturers vs. Student Responses to a Compliment on their Language Ability (Spoken). Compliment Responses Type Profession Total Lecturer Student Compliment on your language ability 1. Appreciation Token: "e.g thanks or a nod" Count 12a 12a 24 % within Profession 18.8% 20.0% 19.4% 2. Compliment Acceptance: "e.g I like it too" Count 14a 6a 20 % within Profession 21.9% 10.0% 16.1% 3. Return: "e.g Yours looks nice too" Count 14a 32b 46 % within Profession 21.9% 53.3% 37.1% 4. Scale down: "e.g It's not that expensive" Count 5a 1a 6 % within Profession 7.8% 1.7% 4.8% 5. Question: "e.g Are you sure?" Count 18a 9a 27 % within Profession 28.1% 15.0% 21.8% 6. Qualification: "e.g It's okay but Alan's is nice" Count 1a 0a 1 % within Profession 1.6% 0.0% 0.8% Total Count 64 60 124 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 16.799a 5 .005 Likelihood Ratio 17.753 5 .003 N of Valid Cases 124 a. 4 cells (33.3%) have expected count less than 5. The minimum expected count is .48. The major compliment responses type for speech vary widely between Saudi female lecturers and Saudi female students. The lecturers most frequently responds with a Question while student responds with a Return. For the lecturers, 28.1 per cent responded with a Question, while 21.9 per cent responded with a Compliment Acceptance. Also, 21.9 per cent of the lecturers responds with Return. 18.8 per cent of the female lecturers used an Appreciation Token compared to 20 per cent of female students. The most frequent compliment response type for student is Return with 53.3 per cent of student using this type. More lecturers use Question response type than students, where only 15 per cent of the students in the sample used this type of compliment response. The bar chart below shows the compliment response on Saudi female lecturers and students when complimented on their hair style. The bar graph shows that the most frequent compliment response is Appreciation Token for both the students and the lecturer. However, lecturers are most likely to use the response than students. Figure 3: Lecturers vs. Student Responses to a Compliment on their Appearance (Hair style). Table 4 below shows the frequency of each responses type for both Saudi female lecturers and Saudi female students. Table 4 below shows that 82.8 per cent of the lecturers used Appreciation Token compared to 77.4 per cent of the students. Thus, Appreciation Token is the most frequent compliment response for both groups. Another frequent compliment response is Question, with 7.8 per cent of the lecturers responded with a question to a compliment on their hair style compared to 9.7 per cent of the students. Table 4: Lecturers vs. Student Responses to a Compliment on their Appearance (Hair style). Compliment Responses Type Profession Total Lecturer Student Compliment on you appearance 1. Appreciation Token: "e.g thanks or a nod" Count 53a 48a 101 % within Profession 82.8% 77.4% 80.2% 2. Compliment Acceptance: "e.g I like it too" Count 1a 2a 3 % within Profession 1.6% 3.2% 2.4% 3. Praise Upgrade: "e.g It's really worth trying, isn't it?" Count 0a 1a 1 % within Profession 0.0% 1.6% 0.8% 4. Return: "e.g Yours looks nice too" Count 4a 5a 9 % within Profession 6.2% 8.1% 7.1% 5. Question: "e.g Are you sure?" Count 5a 6a 11 % within Profession 7.8% 9.7% 8.7% 6. No acknowledgement Count 1a 0a 1 % within Profession 1.6% 0.0% 0.8% Total Count 64 62 126 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 2.752a 5 .738 Likelihood Ratio 3.531 5 .619 N of Valid Cases 126 a. 8 cells (66.7%) have expected count less than 5. The minimum expected count is .49. Table 5 examine the compliment response, from a compliment on a dress. Table 5: Lecturers vs. Student Responses to a Compliment on their Appearance (Dress). Compliment Responses Type Profession Total Lecturer Student Compliment on you appearance 1. Appreciation Token: "e.g thanks or a nod" Count 16a 12a 28 % within Profession 25.4% 19.4% 22.4% 2. Compliment Acceptance: "e.g I like it too" Count 19a 14a 33 % within Profession 30.2% 22.6% 26.4% 3. Praise Upgrade: "e.g It's really worth trying, isn't it?" Count 0a 1a 1 % within Profession 0.0% 1.6% 0.8% 4. Return: "e.g Yours looks nice too" Count 0a 6b 6 % within Profession 0.0% 9.7% 4.8% 5. Scale down: "e.g It's not that expensive" Count 27a 29a 56 % within Profession 42.9% 46.8% 44.8% 6. No acknowledgement Count 1a 0a 1 % within Profession 1.6% 0.0% 0.8% Total Count 63 62 125 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 9.393a 5 .094 Likelihood Ratio 12.488 5 .029 N of Valid Cases 125 a. 6 cells (50.0%) have expected count less than 5. The minimum expected count is .50. Table 5 shows a remarkable difference on the most frequent compliment response. When a compliment is made on hair style, both lecturers and student frequently responded with an Appreciation Token but when a compliment is made on their dress, most students and lecturers respond with a Scale Down type of compliment response. When a compliment was made on the dress of a lecturer, 42.9 per cent responded with a Scale Down, while 46.8 per cent responded the same. The second most frequent responses among students was Compliment Acceptance, with 22.6 per cent, while 30.2 per cent of the lecturers responded the same. The third major compliment response was Appreciation Token, with 25.4 per cent of the lecturers using it, and 19.4 per cent of students. Figure 4 depicts the responses graphically. As can be seen, for both lecturers and students, Scale Down is most frequent, followed by Compliment Acceptance, and Appreciation Token. Figure 4: Lecturers vs. Student Responses to a Compliment on their Appearance (Dress). Table 6: Lecturers vs. Student Responses to a Compliment on their Character. Compliment Responses Type Profession Total Lecturer Student Compliment on the character. 1. Appreciation Token: "e.g thanks or a nod" Count 201a 31b 51 % within Profession 31.2% 50.0% 40.5% 2. Scale down: "e.g It's not that expensive" Count 44a 30b 74 % within Profession 68.8% 48.4% 58.7% 3. No acknowledgement Count 0a 1a 1 % within Profession 0.0% 1.6% 0.8% Total Count 64 62 126 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests2 Value df Asymp. Sig. (2-sided) Pearson Chi-Square 5.991a 2 .050 Likelihood Ratio 6.411 2 .041 N of Valid Cases 126 a. 2 cells (33.3%) have expected count less than 5. The minimum expected count is .49. There is a remarkable difference in the compliment responses from lecturers and students, when a person compliment them about their character. For lecturers, the major compliment responses type is Scale Down, with a frequency of 68.8 per cent. In contrast, for student most frequently response type is Appreciation Token, with 50 per cent. For the lecturers, the second most frequent compliment response is Appreciation Token, with 31.2 per cent. On the hand, the second most frequent compliment response is Scale Down for the students, with 48.4 per cent. Only 0.8 per cent of the students indicated that they will not acknowledge a compliment about their character. Figure 5: Lecturers vs. Student Responses to a Compliment on their Character Figure 5 represent compliment on the character of lecturers and students. The figure shows the major compliment response by lectures is Scale Down, while for students is Appreciation Token. In conclusion, the Saudi females use different compliment response type. When responding to compliment on their possession, the Saudi female uses mostly a Question or Compliment Acceptance. Both the students and lecturer mostly responds with an Appreciation Token for a compliment on hair style. Furthermore, Scale Down is most popular in both group for compliment on dress. Other common compliment response type includes Question and Return. Results for Research Question 2: Differences in Politeness Strategy Between the two Generation. The second research questions examine if there is any significant difference in the politeness strategies that the two generations of the Saudi females use to respond to compliments from six scenarios. A comparison will be made in each of the six scenarios and the chi-square test will be used to make conclusion. Scenario 1: Lecturers vs. Student Responses to a Compliment on their Possession. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 7.150a 5 .210 Likelihood Ratio 7.654 5 .176 N of Valid Cases 126 a. 6 cells (50.0%) have expected count less than 5. The minimum expected count is .49. The chi-square statistics is 7.150, with a p-value of 0.21. Hence, the chi-square statistics is not significant at 5% level of significance. Thus, there is significant differences in the politeness strategies between the two generations when they respond to a compliment on their possession. Scenario 2: Lecturers vs. Student Responses to a Compliment on their Language Ability (Written). Value df Asymp. Sig. (2-sided) Pearson Chi-Square 7.849a 4 .097 Likelihood Ratio 7.949 4 .093 N of Valid Cases 122 a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is .98. The chi-square statistics is 7.849, with a p-value of 0.97, which more than 0.05. Hence, the chi-square is not statistically significant at 5% level of significance. Hence, there is significant differences in the politeness strategies between the two generations when they respond to a compliment on their language ability(written). Scenario 3: Lecturers vs. Student Responses to a Compliment on their Language Ability (Spoken). Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 16.799a 5 .005 Likelihood Ratio 17.753 5 .003 N of Valid Cases 124 a. 4 cells (33.3%) have expected count less than 5. The minimum expected count is .48. The chi-square statistics is 16.799 with a p-value of 0.005. The p-value is less than 5 per cent level of significant and hence, the chi-square statistics is significant at 5 %. There is no significant difference on the politeness strategies used by the two generation of the Saudi females, when the responding to language ability(spoken). Scenario 4: Lecturers vs. Student Responses to a Compliment on their Appearance (Hair style). Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 2.752a 5 .738 Likelihood Ratio 3.531 5 .619 N of Valid Cases 126 a. 8 cells (66.7%) have expected count less than 5. The minimum expected count is .49. The chi-square statistics is 2.752 with a p-value of 0.738, which is more than 5 per cent. Thus, the chi-square statistics is not statistically different from zero. There is difference in the politeness strategies between the two generations of the Saudi females. Scenario 5: Lecturers vs. Student Responses to a Compliment on their Appearance (Dress). Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 9.393a 5 .094 Likelihood Ratio 12.488 5 .029 N of Valid Cases 125 a. 6 cells (50.0%) have expected count less than 5. The minimum expected count is .50. For compliment on Saudi female dress, the chi-square statistics is 9.393, with a p-value of 0.94. The p-value is more than 0.05 per cent and hence, the statistic is not significant at 5 per cent. Hence, there is significant difference in the politeness strategies of the two generations of the Saudi females. Scenario 6: Lecturers vs. Student Responses to a Compliment on their Character. Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 5.991a 2 .050 Likelihood Ratio 6.411 2 .041 N of Valid Cases 126 a. 2 cells (33.3%) have expected count less than 5. The minimum expected count is .49. The chi-square statistic is 5.991, with a p-value of 0.05. The p-value is equal to 5 per cent and hence, the chi-square is significant at 5 per cent level. Hence, there is no significant difference in the politeness strategies adopted by the two generation of the Saudi females. In conclusion, only two scenarios had no differences in the politeness strategies used by the two generation of Saudi females. The first one is when a compliment is made on their spoken language ability, and the second is when a compliment is made on their character. On the other four scenarios, there is statistically significant difference on the compliment response type used by the two groups. In general, there seems to be significant difference in the compliment response type used by the young Saudi female and older Saudi female. Results for Research Question 3: Pragmatic Transfer. The third research question examines whether there is evidence of pragmatic transfer in the compliment response patterns used among Saudi female students and lecturers in each of the six scenarios. One-way ANOVA was used to test whether there is pragmatic transfer from the younger generation of the Saudi females to the old generation of Saudi females. Table 7: One Way ANOVA Table. ANOVA3 Sum of Squares df Mean Square F Sig. Compliment on a possession Between Groups 1.418 1 1.418 .242 .624 Within Groups 727.884 124 5.870 Total 729.302 125 Compliment on you language ability Between Groups 41.426 1 41.426 6.395 .013 Within Groups 777.369 120 6.478 Total 818.795 121 Compliment on you language ability Between Groups .021 1 .021 .003 .958 Within Groups 900.818 122 7.384 Total 900.839 123 Compliment on you appearance Between Groups .409 1 .409 .070 .791 Within Groups 721.631 124 5.820 Total 722.040 125 Compliment on you appearance Between Groups 8.341 1 8.341 1.088 .299 Within Groups 943.291 123 7.669 Total 951.632 124 Compliment on the character. Between Groups 35.417 1 35.417 4.019 .047 Within Groups 1092.742 124 8.812 Total 1128.159 125 Table 7 reports the One-way ANOVA results, for lecturers and students. A compliment on a possession, has F (1,124) =.242 p=.624. The F statistics is not significant at 5 per cent. Thus, the means of the compliment on a possession for students and lecturers are not statistically different. The mean of students compliment responses and the lecturers compliment responses are not statistically different. The students and lecturers seems to be using similar patterns of compliment responses. Hence, there is evidence of pragmatic transfer. Compliment responses from comment on a language ability (written), has F (1,120) =6.395, p=0.13. The F statistics is not significant at 5 per cent, an indication that there is no significant difference in compliment response types between the two group. Hence, there is pragmatic transfer from the younger generation to the old generation. The two generations are using similar phrases in responding to compliment on their language ability. Compliment responses on language ability(spoken) (F (1,122) =0.003, p=.958), compliment on appearance(Hairstyle) (F (1,124) =0.70, p=.791), and compliment on appearance(Dress) (F (1,123) =1.088, p=.299) are all not statistically significant at 5 per cent. Hence, in each of these scenarios, there is evidence of pragmatic transfer from the new generation to the old generation. The only evidence of no pragmatic transfer was found for the compliment on the character. The F (1,124) =4.019, p=0.47, p-value is less than 5 per cent. Thus, the F statistics is significant at 5 per cent, indication that means of the two groups is statistically different. The two generation of the Saudi females use different compliment responses for compliment on their character. In conclusion, generally there is pragmatic transfer between the two generation of Saudi females. The two generation seems to be using similar patterns of compliment response except in one category. There is pragmatic transfer when the two generations respond to compliment on possession, language ability, dress and hair style. However, there is no pragmatic transfer for compliment on character. Read More

Table 1: Lecturers vs. Student Responses to a Compliment on their Possession. Compliment Responses Type Profession Total Lecturer Student Compliment on a possession 1. Appreciation Token: "e.g thanks or a nod" Count 18a 8b 26 % within Profession 28.1% 12.9% 20.6% 2. Compliment Acceptance: "e.g I like it too" Count 18a 27a 45 % within Profession 28.1% 43.5% 35.7% 3. Return: "e.g Yours looks nice too" Count 24a 21a 45 % within Profession 37.5% 33.9% 35.7% 4.

Scale down: "e.g It's not that expensive" Count 1a 2a 3 % within Profession 1.6% 3.2% 2.4% 5. Question: "e.g Are you sure?" Count 3a 3a 6 % within Profession 4.7% 4.8% 4.8% 6. Disagreement: "e.g I don't like it" Count 0a 1a 1 % within Profession 0.0% 1.6% 0.8% Total Count 64 62 126 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 7.150a 5 .210 Likelihood Ratio 7.654 5 .176 N of Valid Cases 126 a.

6 cells (50.0%) have expected count less than 5. The minimum expected count is .49. As table 1 shows, among the lecturers the most frequent compliment response is “Return” with a frequency of 24 responses which constitutes 37.5 per cent. Appreciation token and Compliment Acceptance compliment responses have the same frequency of 28.1 per cent, Question compliment response has the least frequency at 4.7 per cent. For students, the most frequent compliment response is Compliment Acceptance, with a frequency of 43.

5 per cent. The second major response type is Return with a frequency of 33.9 per cent. Appreciation Token, Scale down, Question and Disagreement have frequencies of 12.9 per cent, 3.2 per cent, 4.8 per cent and 1.6 per cent respectively. The bar chart below shows the compliment response on Saudi female lecturers and students when complimented on their written work. Figure 1: Lecturers vs. Student Responses to a Compliment on their Language Ability (Written). Table 2 below shows the frequency of each responses type for both Saudi female lecturers and Saudi female students.

Table 2: Lecturers vs. Student Responses to a Compliment on their Language Ability (Written). Compliment Responses Type Profession Total Lecturer Student Compliment on your language ability 1. Appreciation Token: "e.g thanks or a nod" Count 12a 9a 21 % within Profession 19.4% 15.0% 17.2% 2. Compliment Acceptance: "e.g I like it too" Count 28a 15b 43 % within Profession 45.2% 25.0% 35.2% 3. Return: "e.g Yours looks nice too" Count 14a 24b 38 % within Profession 22.6% 40.0% 31.1% 4. Scale down: "e.

g It's not that expensive" Count 1a 1a 2 % within Profession 1.6% 1.7% 1.6% 5. Question: "e.g Are you sure?" Count 7a 11a 18 % within Profession 11.3% 18.3% 14.8% Total Count 62 60 122 % within Profession 100.0% 100.0% 100.0% Each subscript letter denotes a subset of Profession categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square 7.849a 4 .097 Likelihood Ratio 7.949 4 .093 N of Valid Cases 122 a.

2 cells (20.0%) have expected count less than 5. The minimum expected count is .98. Table 2 shows that for the lecturers the major response to language compliment is Compliment Acceptance which has a frequency of 45.2 per cent. Return and Appreciation Token has almost the same frequency, however, Return has a higher frequency of 22.6 per cent compared to 19.4 per cent for Appreciation Token. Scale down and Question compliment response has 1.6 per cent and 11.3 per cent respectively. Students prefer to use Return compliment response compared to lecturers who majorly use Compliment Acceptance.

From table 2, Return compliment responses has 40 per cent for students. Another 25 per cent of students uses Compliment Acceptance response, while 15 per cent of students uses Appreciation Token, when responding to compliment on language ability. 1.

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