| Literature DB >> 22530007 |
Raymond C K Chan1, Yan-fang Shi, Man-kin Lai, Yu-na Wang, Ya Wang, Ann M Kring.
Abstract
BACKGROUND: The Temporal Experience of Pleasure Scale (TEPS) is a measure specifically designed to capture the anticipatory and consummatory facets of pleasure. However, few studies have examined the structure of the measure in non-Western samples. The current study aimed to evaluate the factor structure and psychometric properties of the TEPS in a Chinese sample.Entities:
Mesh:
Year: 2012 PMID: 22530007 PMCID: PMC3329425 DOI: 10.1371/journal.pone.0035352
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Factor loadings of the 4-factor solution for the translated 19-item TEPS in the calibration sample.
| Factors | |||||
| Items | I | II | III | IV | |
| TEPS1 | When I hear about a new movie starring my favorite actor, I can't wait to see it.(Anticipatory) | 0.64 | |||
| TEPS5# | I love it when people play with my hair.(Consummatory) | 0.36 | |||
| TEPS8 | When I think of something tasty, like a chocolate chip cookie, I have to have one.(Anticipatory) | 0.60 | |||
| TEPS10 | I get so excited the night before a major holiday I can hardly sleep.(Anticipatory) | 0.59 | |||
| TEPS11# | When I'm on my way to an amusement park, I can hardly wait to ride the roller coasters.(Anticipatory) | 0.67 | |||
| TEPS2 | I enjoy taking a deep breath of fresh air when I walk outside.(Consummatory) | 0.75 | |||
| TEPS3 | The smell of freshly cut grass is enjoyable to me. (Consummatory) | 0.75 | |||
| TEPS7 | A hot cup of coffee or tea on a cold morning is very satisfying to me.(Consummatory) | 0.49 | |||
| TEPS9 | I appreciate the beauty of a fresh snowfall.(Consummatory) | 0.57 | |||
| TEPS14 | I love the sound of rain on the windows when I'm lying in my warm bed.(Consummatory) | 0.57 | |||
| TEPS19 | I love it when a baby snuggles into my arms.(Added item, to replace item 5, Consummatory) | 0.28b | 0.36 | ||
| TEPS4 | I look forward to a lot of things in my life.(Anticipatory) | 0.67 | |||
| TEPS6 | Looking forward to a pleasurable experience is in itself pleasurable.(Anticipatory) | 0.68 | |||
| TEPS18 | When something exciting is coming up in my life, I really look forward to it.(Anticipatory) | 0.74 | |||
| TEPS20 | On the way to my first date with my beloved, I can hardly wait to see him/her. (Added item, to replace item 11, Anticipatory) | 0.57 | |||
| TEPS12 | I really enjoy the feeling of a good yawn.(Consummatory) | 0.58 | |||
| TEPS15 | When I think about eating my favorite food, I can almost taste how good it is.(Anticipatory) | 0.64 | |||
| TEPS16 | When ordering something off the menu, I imagine how good it will taste.(Anticipatory) | 0.64 | |||
| TEPS17 | The sound of crackling wood in the fireplace is very relaxing.(Consummatory) | 0.61 | |||
Notes:
Factor I - Abstract Anticipatory; II - Contextual Anticipatory; III - Abstract Consummatory; IV - Contextual Consummatory
indicates item part of the original TEPS;
indicates item added to be more in line with Chinese culture.
Correlation matrix of the 20 items of the Chinese translated version of TEPS in the 1156 calibration sample of Chinese participants.
| Items | a. | b. | c. | d. | e. | f. | g. | h. | i. | j. | k. | |
| a. | TEPS1 | 1.00 | ||||||||||
| b. | TEPS5 | 0.15 | 1.00 | |||||||||
| c. | TEPS8 | 0.22 | 0.20 | 1.00 | ||||||||
| d. | TEPS10 | 0.22 | 0.18 | 0.27 | 1.00 | |||||||
| e. | TEPS11 | 0.26 | 0.20 | 0.30 | 0.35 | 1.00 | ||||||
| f. | TEPS2 | 0.12 | 0.09 | 0.08 | 0.12 | 0.13 | 1.00 | |||||
| g. | TEPS3 | 0.11 | 0.19 | 0.16 | 0.17 | 0.15 | 0.52 | 1.00 | ||||
| h. | TEPS7 | 0.17 | 0.15 | 0.26 | 0.20 | 0.21 | 0.28 | 0.31 | 1.00 | |||
| i. | TEPS9 | 0.13 | 0.15 | 0.21 | 0.20 | 0.22 | 0.35 | 0.34 | 0.34 | 1.00 | ||
| j. | TEPS14 | 0.04 | 0.12 | 0.08 | 0.13 | 0.12 | 0.32 | 0.33 | 0.27 | 0.27 | 1.00 | |
| k. | TEPS19 | 0.13 | 0.23 | 0.11 | 0.22 | 0.14 | 0.22 | 0.26 | 0.28 | 0.23 | 0.21 | 1.00 |
| l. | TEPS4 | 0.16 | 0.17 | 0.16 | 0.18 | 0.12 | 0.33 | 0.29 | 0.26 | 0.27 | 0.19 | 0.17 |
| m. | TEPS6 | 0.09 | 0.14 | 0.19 | 0.17 | 0.16 | 0.30 | 0.28 | 0.33 | 0.28 | 0.14 | 0.25 |
| n. | TEPS18 | 0.13 | 0.12 | 0.19 | 0.22 | 0.19 | 0.22 | 0.19 | 0.20 | 0.26 | 0.23 | 0.24 |
| o. | TEPS20 | 0.17 | 0.11 | 0.19 | 0.21 | 0.20 | 0.17 | 0.13 | 0.20 | 0.18 | 0.14 | 0.24 |
| p. | TEPS12 | 0.08 | 0.17 | 0.15 | 0.15 | 0.25 | 0.11 | 0.18 | 0.16 | 0.15 | 0.13 | 0.17 |
| q. | TEPS13 | 0.07 | 0.05 | 0.02 | 0.02 | 0.04 | −0.05 | −0.04 | −0.02 | 0.02 | −0.15 | −0.02 |
| r. | TEPS15 | 0.19 | 0.17 | 0.29 | 0.21 | 0.23 | 0.22 | 0.24 | 0.21 | 0.21 | 0.21 | 0.25 |
| s. | TEPS16 | 0.16 | 0.15 | 0.21 | 0.28 | 0.23 | 0.21 | 0.25 | 0.29 | 0.20 | 0.18 | 0.23 |
| t. | TEPS17 | 0.09 | 0.21 | 0.18 | 0.13 | 0.17 | 0.21 | 0.25 | 0.27 | 0.20 | 0.30 | 0.24 |
p<0.01,
p<0.05.
Evaluative measures of model fit by the three models, and the Bollen-Stine bootstrap estimates for the 4-factor model in the 1119 validation sample of Chinese participants.
| Confirmatory factor analysis | BS bootstrapping 4-factor model | ||||
| Good-of-fit indices | 2-factor model | 4-factor model | 2nd-order factor model | Mean | 95% CI |
| SB scaled χ2 | 1065.276 | 452.2996 | 482.7125 | 176.2968 | (142.94, 219.95) |
| df | 151 | 146 | 147 | 146 | - |
|
| <0.001 | <0.001 | <0.001 | 0.1466 | (0.0001, 0.5562) |
| GFI | 0.8916 | 0.9512 | 0.9482 | 0.9838 | (0.9799, 0.9868) |
| AGFI | 0.8636 | 0.9366 | 0.933 | 0.9789 | (0.9739, 0.9828) |
| SRMR | 0.06124 | 0.04184 | 0.04511 | 0.0228 | (0.0201, 0.0257) |
| NFI | 0.897 | 0.9563 | 0.9533 | 0.9583 | (0.9475, 0.9672) |
| NNFI | 0.8982 | 0.9647 | 0.9616 | 0.9912 | (0.9790, 1.0008) |
| CFI | 0.9101 | 0.9699 | 0.967 | 0.9924 | (0.9821, 1.0000) |
| RMSEA | 0.07359 | 0.04332 | 0.0452 | 0.0124 | (0.0000, 0.0213) |
|
| <0.001 | 0.9924 | 0.9602 | - | - |
Notes:
SB scaled χ2 - Santorra-Bentler scaled χ2; df - degree of freedom; p - p value; GFI - Goodness-of-fit index; AGFI - Adjusted GFI; SRMR - Standard Root Mean Square Residuals; NFI - Normed Fit Index; NNFI - Non-normed Fit Index; CFI - Comparative Fit Index; RMSEA - Root Mean Square Error of Approximation.
Kring et al. (2004).
Based on 250 bootstrap sample of size 250.
95% Confidence Interval.
Model χ2 instead of SB scaled χ2.
127 of 250 bootstrap samples with p>0.05.
Figure 1Four-factor model of the Chinese translated TEPS in the validation sample – Factor loadings and correlations between factors estimated by the Satorra-Bentler (SB) method, and their means (which is the same as the estimates by the SB method at 2 decimal places) and 95% confidence intervals (in brackets) estimated by the Bollen-Stine bootstrapping method.