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Chae, Jeon, and Kim: A Structural Model for Premenstrual Coping in University Students: Based on Biopsychosocial Model
Original Article

Journal of Korean Academy of Nursing 2017; 47(2): 257-266.

Published online: 28 April 2017

DOI: https://doi.org/10.4040/jkan.2017.47.2.257

A Structural Model for Premenstrual Coping in University Students: Based on Biopsychosocial Model

Myung-Ock Chae, Hae Ok Jeon, Ahrin Kim

Department of Nursing, College of Health Sciences, Cheongju University, Cheongju, Korea.

Address reprint requests to: Kim, Ahrin. Department of Nursing, College of Health Sciences, Cheongju University, 298 Daeseong-ro, Cheongwon-gu, Cheongju 28503, Korea. Tel: +82-43-229-7932, Fax: +82-43-229-8969, arkim@cju.ac.kr

Received 21 September 2016       Revised 3 March 2017       Accepted 6 March 2017

© 2017 Korean Society of Nursing Science

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0/) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

Abstract

Purpose

The aims of this study were to construct a hypothetical structural model which explains premenstrual coping in university students and to test the fitness with collected data.

Methods

Participants were 206 unmarried women university students from 3 universities in A and B cities. Data were collected from March 29 until April 30, 2016 using self-report structured questionnaires and were analyzed using IBM SPSS 23.0 and AMOS 18.0.

Results

Physiological factor was identified as a significant predictor of premenstrual syndrome (t=6.45, p<.001). This model explained 22.1% of the variance in premenstrual syndrome. Psychological factors (t=-2.49, p=.013) and premenstrual syndrome (t=8.17, p<.001) were identified as significant predictors of premenstrual coping. Also this model explained 30.9% of the variance in premenstrual coping in university students. A physiological factors directly influenced premenstrual syndrome (β=.41, p=.012). Premenstrual syndrome (β=.55, p=.005) and physiological factor (β=.23, p=.015) had significant total effects on premenstrual coping. Physiological factor did not have a direct influence on premenstrual coping, but indirectly affected it (β=.22, p=.007). Psychological factors did not have an indirect or total effect on premenstrual coping, but directly affected it (β=-.17, p=.036).

Conclusion

These findings suggest that strategies to control physiological factors such as menstrual pain should be helpful to improve premenstrual syndrome symptoms. When developing a program to improve premenstrual coping ability and quality of menstrual related health, it is important to consider psychological factors including perceived stress and menstrual attitude and premenstrual syndrome.

Keywords: Female, Premenstrual syndrome, Structural models, Students, Young adult

Figures and Tables

Figure 1

Conceptual framework.

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Figure 2

Path diagrams for the hypothetical model.

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Table 1

Descriptive Statistics of Observed Variables and Correlations among the Observed Variables (N=206)

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Variables M±SD Range Skewness Kurtosis r (p)
1 2 3 4 5 6 7
1. Menstrual pain 5.85±2.99 0~10 −0.79 −0.53 1
2. Perceived stress 3.02±0.46 1.80~4.50 0.40 0.72 .04 (.622) 1
3. Attitudes toward menstruation 3.61±0.77 1.81~5.62 0.04 −0.33 .04 (.601) .28 (<.001) 1
4. Size of social network 4.14±2.10 0.67~9.00 0.71 −0.40 −.10 (.176) −.18 (.008) −.02 (.811) 1
5. Satisfaction with social network 4.84±1.09 1.00~6.00 −1.30 1.58 .05 (.438) −.19 (.007) −.17 (.013) .26 (<.001) 1
6. Premenstrual syndrome 27.73±10.66 10.00~55.00 0.20 −0.79 .42 (<.001) .19 (.006) .09 (.220) −.15 (.034) −.05 (.462) 1
7. Premenstrual coping 94.66±21.52 36.00~138.00 −0.78 0.37 .24 (<.001) −.04 (.538) −.02 (.804) −.10 (.140) <.01 (.951) .53 (<.001) 1
Table 2

Model Fit Indices for the Hypothetical Model (N=206)

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Fit indices Absolute fit indices Incremental fit indices
χ2 df p CMIN/df SRMR GFI NFI IFI TLI CFI
Hypothetical model 8.73 8 .366 1.09 .04 .99 .95 .99 .99 .99
Acceptable criteria >.01 ≤2.0 ≤.08 ≥.90 ≥.90 ≥.90 ≥.90 ≥.90

Df=degree of freedom; SRMR=standardized root mean square residual; GFI=goodness fit Index; NFI=normed fit indrx; IFI=incremental fit index; TLI=Turker-Lewis index; CFI=comparative fit index.

Table 3

Standardized Direct, Indirect, and Total Effects of the Hypothetical Model (N=206)

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Endogenous variables Exogenous variables β t (p) SMC Standardized direct effect Standardized indirect effect Standardized total effect
Premenstrual syndrome Physiological factor .41 6.45 (<.001) .22 .41 (.012) .41 (.012)
Psychological factors .13 1.78 (.076) .13 (.076) .13 (.076)
Social factors −.14 −1.17 (.244) −.14 (.193) −.14 (.193)
Premenstrual coping Physiological factor .01 0.21 (.832) .31 .01 (.932) .22 (.007) .23 (.015)
Psychological factors −.17 −2.49 (.013) −.17 (.036) .07 (.072) −.10 (.318)
Social factors −.06 −0.56 (.575) −.06 (.648) −.07 (.220) −.13 (.331)
Premenstrual syndrome .55 8.17 (<.001) .55 (.005) .55 (.005)

β=Standardized regression weight; SMC=Squared multiple correlation (R2).

Notes

CONFLICTS OF INTEREST The authors declared no conflict of interest.

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