Abstract
The Profile of Mood States (POMS) is one of the most widely applied scales for measuring mood. Considering the advantages of short scales and increased international research, the aim of the present study was to evaluate cross-culturally the psychometric properties of a short 16-item version of the POMS. Data were collected from 15,693 participants across 10 different countries worldwide. Initially, we identified the original versions of the POMS in various languages. Subsequently, we selected 16 items based on the previously validated short form (POMS-16) for analysis. Psychometric properties of the POMS were then evaluated in samples from each studied population for each language version. Confirmatory factor analysis was conducted to assess its invariance across age groups and gender, alongside reliability estimation. Most language versions of the POMS-16 showed a good fit with the four-factor model, except for the Chinese (traditional) and Turkish versions. Reliability was generally high, except for the Vigor subscale in a small subset of languages. Regarding measurement invariance, the majority of language versions were invariant across gender and age groups, except for the Farsi language version across gender, and the Chinese, Farsi, Finnish, and Turkish versions across age. These findings enhance the cross-cultural applicability of the POMS-16, contributing to its utility in diverse populations and thus enhancing the comparability of the results. In addition, we introduced the first versions of the POMS in Farsi, Finnish, and Icelandic.
Background
“Mood matters”, posited Lane and Terry (2000) – both, for basic and applied psychological research. Moods are mild and pervasive affective states (McNair et al., 1971) influenced by psychophysiological responses (Soylu, 2021) that significantly impact wellbeing, influencing behavioral patterns and perception (e.g., perceived health outcomes; Berger et al., 1998). Lane and Terry (2000) proposed a general definition of mood being “a set of feelings, ephemeral in nature, varying in intensity and duration, and usually involving more than one emotion” (Lane and Terry, 2000, p. 7). A pivotal factor in this description is that mood and emotion are understood as part of the same theoretical background, as making a definitive distinction between them remains a subject of debate (DeLancey, 2006, pp. 527–538; Lane et al., 2005). At a specific level, it is posited that mood encompasses an evaluative facet, namely the extent to which mood is perceived as pleasant, coupled with an arousal component, characterized by varying degrees of activity (Terry and Lane, 2000).
Mood impacts cognitive performance at a basic level (Schwarz and Clore, 2003) but is also relevant in collaborative settings, e.g., via mood contagion (Jordan et al., 2006; Neumann and Strack, 2000). Through these pathways, mood plays an important role in various contexts, such as the workplace (Morfeld et al., 2007; Selmi et al., 2023) and athletic performance (Aydi et al., 2022). In the clinical and psychotherapeutic context, the assessment of mood states is crucial for understanding and addressing mental health concerns (e.g., monitoring mood fluctuations diagnostics, treatment evaluation; Classen et al., 2001; Grulke et al., 2004; Hosaka et al., 2001).
A quick history of the Profile of Mood States (POMS) reflects the amount of effort put into measuring mood state (POMS; McNair et al., 1971). In its original form (McNair et al., 1971, 1992), it includes 65 items that are loaded on 7 different scales: depression, anxiety, fatigue, vigor, irritability, tension, and confusion. Initially, seven items constituted a Friendliness factor, which was excluded due to poor discriminant validity with the Vigor-Activity factor. However, few adaptations of the POMS retained this component (Andrade et al., 2010). The intensity of the mood is rated on a 5-point Likert scale ranging from “0 = not at all” to “4 = very strong.” Commonly used time frames reflecting mood over a specific period of time include: Today, Right Now, and This Week (e.g., “How did you feel today?” vs. “the last week including today; Gibson, 1997). The total score (total mood disturbance) is calculated by subtracting the (positive) value of the Vigor subscale from the sum of the remaining scales. However, different scoring procedures are described with regard to the calculation of scale values (Kieviet-Stijnen et al., 2008). It consistently achieved high internal consistency (α of 0.84–0.95; McNair et al., 1971, 1992) and its construct validity is supported by past research (e.g., Morris and Salmon, 1994; Watson and Clark, 1992).
Past studies examining its factor structure provided substantial evidence for most of the seven factors (Norcross et al., 1984), with the exception of the Confusion subscale (Bourgeois et al., 2012; Morfeld et al., 2007; Netz et al., 2005), which was instead regarded as a cognitive state (Lane et al., 2007). POMS is one of the most widely used questionnaires providing several advantages. First, it is a multidimensional self-report instrument that captures the transient and oscillating nature of mood states (McNair et al., 1971, 1992). Furthermore, it is a versatile tool that can be applied in a variety of settings extending from the psychotherapeutic and medical field (e.g., Baker et al., 2002; Braslis et al., 1995; Gross, 1991; López-Jiménez et al., 2021; Szaflarski et al., 2003; von Steinbüchel et al., 1994) to sport psychology (Leunes and Burger, 2000; Lochbaum et al., 2021). Moreover, it has been widely applied and validated in several languages (Chinese; Cheung, 1999; Cheung and Lam, 2005; German; Grulke et al., 2006; Italian; Mannarini et al., 2012; Peri et al., 2000; Portuguese, Spanish; Andrade et al., 2013; Fernández et al., 2000; Perczek et al., 2000, Turkish; Selvi et al., 2011). Due to increased international research and the need for outcome comparability, cross-cultural validation of a scale is paramount. In addition, considering that affective states may be influenced by sociological and cultural aspects, measuring mood states across cultures may provide important insights into universal aspects of mood in a variety of settings.
The German version of the POMS (Biehl and Landauer, 1975; Biehl et al., 1986) presented the first psychometrical analysis, with satisfactory psychometric results (a = 0.88–0.94). A short version with 35 items (Bullinger et al., 1990) indicated satisfying factorial validity and internal consistency (α = 0.90); however, the data were based on a student sample (Bullinger et al., 1990; Gross, 1991). A replication in a larger, population-representative sample demonstrated similarly satisfactory internal consistency (a = 0.89–0.95), although it still revealed a limited factorial structure (Albani et al., 2005). Due to the limitations in the factorial structure, Petrowski et al. (2021) aimed to empirically identify a shorter version of the POMS with improved factorial validity. A psychometrically optimal 16-item solution among all valid combinations of the full POMS was found with a four-factorial structure and very good psychometric properties (a = 0.86–0.91). This version is strictly invariant across age groups and shows strong and partial strict invariance across genders. It represents the shortest version of the POMS available, aside from the English POMS (Cella et al., 1987; 11-items). However, the latter only provides a total mood disturbance score without subscales or norms, emphasizing the uniqueness of the German POMS-16 and the developmental lack in other languages.
Ease of administration is an advantage when collecting data, especially when considering target populations in clinical contexts (e.g., patients with cancer and chronic pain) and in epidemiological research. The importance of using brief instruments with robust psychometric properties cannot be overstated. Therefore, preventing exhaustion, resistance, and boredom during the completion of the questionnaire is key. The purpose of the present study was thus to provide a brief measure of mood. To this end, we evaluated the psychometric properties of a short version of the POMS-16 in 11 languages. In this study, we examined whether the described four-factorial structure of the instrument could be replicated and explored additional characteristics of the scales, such as measurement invariances across age and gender.
Method
This study used a cross-sectional design to investigate mood, and its data were collected during the initial COVID lockdowns, between 29 March 2020 and 7 May 2020. The various language versions of the POMS were used as part of a separate project (Brand et al., 2020) by the International Research Group on COVID and Exercise (IRG). Existing versions of the POMS in various languages were used where available. In cases where no translation was available, the items were translated by bilingual experts in the field of study and subsequently independently checked for the quality of the translation. The translations were based on the English version of the POMS-16. Participants were informed about study procedures, data collection, and anonymization of personal data, and provided informed consent as required by German law, documented prior to commencement of the survey. Participants were recruited either through the research team’s personal networks or by responding to invitations shared via mailing lists and social media platforms, which provided a link to the online questionnaire.
Instruments
Profile of Mood Scales (POMS-16; Petrowski et al., 2021). In the study at hand, we implemented a recently validated short version (POMS-16) based on the original long-form POMS-65 (McNair et al., 1971, 1992). The items are grouped into four factors: dejection, vigor, fatigue, and anger. The intensity of the mood is rated on a 5-point Likert scale ranging from “0 = not at all” to “4 = very strong,” reflecting mood over a specific period of time (“How have you felt during the past week including today?”). The scores of each individual subscale range from 0 to 16.
Statistical analysis
All analyses were conducted in R, using the packages lavaan and semTools (Rosseel, 2012; Jorgensen et al., 2019). Specifically, we conducted confirmatory factor analysis (CFA) using robust full-information maximum likelihood estimation (Schafer and Graham, 2002; Yuan and Bentler, 2000). Across the entire dataset, 2.86% of response data was missing. Following the customary procedure, we analyzed model fit by means of the χ2-test and common descriptive fit measures: the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). Based on the typical recommendations, CFI and TLI should be equal to or greater than 0.90, and even better if it is equal to or greater than 0.95, while RMSEA and SRMR should be smaller than 0.08, and even better if it is equal or greater than 0.05 (Hu and Bentler, 1999; Schermelleh-Engel et al., 2003). We considered any given model fit as satisfactory if at least three out of the four indices fulfilled the criteria. Moreover, we report omega as a measure of internal consistency. Since the multiple factors in our model are only moderately correlated and no second-order or general construct can be assumed, we utilized McDonald’s (1999) basic formula. Finally, we tested the model for measurement invariance across age groups (two groups, split on the median) and participant gender. To this end, we used the common procedure described by Meredith (1993) of successively constraining factor loadings, item intercepts, and item residual variances. We utilized the common between-model cutoffs of 0.010 for CFI and 0.015 for RMSEA (Cheung and Rensvold, 2002; Chen, 2007).
Results
Sample characteristics
We analyzed the responses of participants speaking various languages (Chinese simplified, Chinese traditional, English, Farsi, Finnish, German, Icelandic, Italian, Portuguese, Spanish, and Turkish) around the world. The total sample comprised of N = 15,693 individuals. In general, more women than men participated in the study, with Iceland having, descriptively, the highest ratio of female participation (78.7%). The age average for the various samples ranged between 25 and 42 years. Details of the sample are provided in Table 1.
Table 1
| Language | n | Margin of error, % | Female (n, %) |
Age (M ± SD) |
|---|---|---|---|---|
| Chinese (simplified) | 922 | 3 | 482, 53.6% | 25.18 ± 9.04 |
| Chinese (traditional) | 1,172 | 3 | 621, 53.3% | 36.17 ± 15.38 |
| English | 7,120 | 1 | 4,285, 6.5% | 33.04 ± 13.14 |
| Farsi | 194 | 7 | 121, 63% | 34.21 ± 9.85 |
| Finnish | 220 | 7 | 137, 62.3% | 42.64 ± 11.31 |
| German | 2,599 | 2 | 1,629, 63.1% | 37.29 ± 14.47 |
| Icelandic | 423 | 5 | 333, 78.7% | 42.79 ± 12.76 |
| Italian | 1,324 | 3 | 668, 5.6% | 39.56 ± 16.42 |
| Portuguese | 553 | 4 | 353, 64.3% | 34.8 ± 11.58 |
| Spanish | 541 | 4 | 265, 49.3% | 31.74 ± 12.43 |
| Turkish | 625 | 4 | 380, 61.5% | 30.78 ± 21.23 |
Sample characteristics.
Factorial validity
We computed a CFA with the above-mentioned items in a correlated factors model with four latent constructs. The majority of the models exhibited a good fit, except for the Chinese (traditional) and Turkish (see Table 2). Similarly, the majority of different language versions showed good to very good reliability for the subscales—except for the subscale Vigor in English, Icelandic, Portuguese, and Spanish. All standardized factor loadings were equal to or greater than 0.744. At the behest of a reviewer, we added exploratory factor analyses to check the suitability of different numbers of factors in different language versions (see Supplementary material). While 1 out of the 11 language versions could potentially be reduced to 3 factors, all 10 other versions required at least 4 factors. Thus, in the interest of cross-cultural comparability and unity of the instrument, we decide in favor of a uniform four-factor solution.
Table 2
| Language | χ2 | df | p | CFI | TLI | RMSEA | Lower CI | Upper CI | SRMR | Ω | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Fatigue | Vigor | Anger | Dejection | ||||||||||
| Chinese (simplified) | 645.253 | 98 | <0.001 | 0.918 | 0.900 | 0.091 | 0.084 | 0.098 | 0.088 | 0.867 | 0.744 | 0.834 | 0.854 |
| Chinese (traditional) | 948.78 | 98 | <0.001 | 0.895 | 0.872 | 0.103 | 0.097 | 0.109 | 0.098 | 0.869 | 0.724 | 0.783 | 0.836 |
| English | 3197.312 | 98 | <0.001 | 0.921 | 0.903 | 0.073 | 0.071 | 0.076 | 0.065 | 0.861 | 0.619 | 0.803 | 0.789 |
| Farsi | 212.764 | 98 | <0.001 | 0.923 | 0.905 | 0.085 | 0.068 | 0.101 | 0.061 | 0.803 | 0.821 | 0.836 | 0.856 |
| Finnish | 177.126 | 98 | <0.001 | 0.951 | 0.940 | 0.065 | 0.049 | 0.081 | 0.053 | 0.900 | 0.818 | 0.858 | 0.817 |
| German | 1088.092 | 98 | <0.001 | 0.950 | 0.938 | 0.070 | 0.066 | 0.074 | 0.052 | 0.886 | 0.873 | 0.876 | 0.818 |
| Icelandic | 223.829 | 98 | <0.001 | 0.961 | 0.952 | 0.059 | 0.049 | 0.070 | 0.044 | 0.915 | 0.695 | 0.843 | 0.833 |
| Italian | 801.733 | 98 | <0.001 | 0.915 | 0.896 | 0.083 | 0.078 | 0.088 | 0.061 | 0.833 | 0.881 | 0.82 | 0.785 |
| Portuguese | 275.634 | 98 | <0.001 | 0.952 | 0.941 | 0.064 | 0.055 | 0.073 | 0.063 | 0.906 | 0.664 | 0.87 | 0.808 |
| Spanish | 341.966 | 98 | <0.001 | 0.921 | 0.904 | 0.075 | 0.067 | 0.084 | 0.088 | 0.859 | 0.579 | 0.872 | 0.755 |
| Turkish | 632.154 | 98 | <0.001 | 0.901 | 0.879 | 0.105 | 0.097 | 0.113 | 0.077 | 0.899 | 0.764 | 0.814 | 0.763 |
Model fit of each assessed language.
We reported omega values for reliability.
Measurement invariance
Finally, we tested each language version for measurement invariance across participant gender and age groups. For the age groups, we split each language sample at the median. We report the results of the step-wise test process in Tables 3, 4. The model in each different language is strictly invariant across gender and age groups, with some exceptions. Concerning gender, the language version of Farsi did not exhibit strict invariance—only strong invariance. The other reported languages showed evidence for strict gender invariance. Regarding age, the Chinese, Farsi, Finish, and Turkish versions provide evidence for strict invariance. The remaining language versions did not show strict invariance but strong invariance—with the exception of the Italian version.
Table 3
| Language | Model | χ2 | Δχ2 | df | Δdf | p | CFI | ΔCFI | RMSEA | ΔRMSEA |
|---|---|---|---|---|---|---|---|---|---|---|
| Chinese (simplified) | Configural | 775.724 | 196 | 0.917 | 0.092 | |||||
| Chinese (simplified) | Metric | 792.918 | 17.194 | 208 | 12 | 0.142 | 0.915 | 0.002 | 0.090 | 0.002 |
| Chinese (simplified) | Scalar | 842.285 | 49.366 | 220 | 12 | <0.001 | 0.911 | 0.004 | 0.090 | 0.000 |
| Chinese (simplified) | Strict | 881.231 | 38.947 | 236 | 16 | 0.001 | 0.906 | 0.005 | 0.089 | 0.001 |
| Chinese (traditional) | Configural | 1044.389 | 196 | 0.892 | 0.104 | |||||
| Chinese (traditional) | Metric | 1068.289 | 23.900 | 208 | 12 | 0.021 | 0.892 | 0.001 | 0.101 | 0.003 |
| Chinese (traditional) | Scalar | 1096.357 | 28.068 | 220 | 12 | 0.005 | 0.891 | 0.001 | 0.099 | 0.003 |
| Chinese (traditional) | Strict | 1091.611 | 4.747 | 236 | 16 | 0.997 | 0.892 | 0.001 | 0.095 | 0.004 |
| English | Configural | 3225.091 | 196 | 0.921 | 0.073 | |||||
| English | Metric | 3275.085 | 49.994 | 208 | 12 | <0.001 | 0.920 | 0.001 | 0.071 | 0.002 |
| English | Scalar | 3422.025 | 146.939 | 220 | 12 | <0.001 | 0.917 | 0.003 | 0.071 | 0.001 |
| English | Strict | 3508.287 | 86.262 | 236 | 16 | <0.001 | 0.915 | 0.003 | 0.069 | 0.001 |
| Farsi | Configural | 32.397 | 196 | 0.924 | 0.084 | |||||
| Farsi | Metric | 333.141 | 12.744 | 208 | 12 | 0.388 | 0.922 | 0.002 | 0.082 | 0.001 |
| Farsi | Scalar | 34.362 | 7.221 | 220 | 12 | 0.843 | 0.925 | 0.004 | 0.078 | 0.004 |
| Farsi | Strict | 373.031 | 32.669 | 236 | 16 | 0.008 | 0.914 | 0.011 | 0.081 | 0.003 |
| Finnish | Configural | 307.207 | 196 | 0.934 | 0.074 | |||||
| Finnish | Metric | 319.437 | 12.230 | 208 | 12 | 0.427 | 0.933 | 0.001 | 0.072 | 0.002 |
| Finnish | Scalar | 332.579 | 13.142 | 220 | 12 | 0.359 | 0.933 | 0.000 | 0.070 | 0.002 |
| Finnish | Strict | 333.966 | 1.387 | 236 | 16 | 1.000 | 0.939 | 0.006 | 0.065 | 0.006 |
| German | Configural | 1238.140 | 196 | 0.947 | 0.072 | |||||
| German | Metric | 1237.566 | 0.574 | 208 | 12 | 1.000 | 0.947 | 0.000 | 0.069 | 0.002 |
| German | Scalar | 130.445 | 62.878 | 220 | 12 | <0.001 | 0.945 | 0.002 | 0.069 | 0.001 |
| German | Strict | 1353.192 | 52.747 | 236 | 16 | <0.001 | 0.941 | 0.004 | 0.069 | 0.000 |
| Icelandic | Configural | 397.080 | 196 | 0.949 | 0.068 | |||||
| Icelandic | Metric | 40.556 | 3.476 | 208 | 12 | 0.991 | 0.948 | 0.001 | 0.066 | 0.002 |
| Icelandic | Scalar | 412.861 | 12.305 | 220 | 12 | 0.422 | 0.949 | 0.001 | 0.064 | 0.002 |
| Icelandic | Strict | 413.867 | 1.006 | 236 | 16 | 1.000 | 0.948 | 0.001 | 0.062 | 0.002 |
| Italian | Configural | 867.084 | 196 | 0.919 | 0.081 | |||||
| Italian | Metric | 892.221 | 25.137 | 208 | 12 | 0.014 | 0.918 | 0.002 | 0.079 | 0.001 |
| Italian | Scalar | 948.548 | 56.327 | 220 | 12 | <0.001 | 0.913 | 0.005 | 0.079 | 0.000 |
| Italian | Strict | 1011.600 | 63.052 | 236 | 16 | <0.001 | 0.906 | 0.007 | 0.079 | 0.000 |
| Portuguese | Configural | 357.187 | 196 | 0.958 | 0.059 | |||||
| Portuguese | Metric | 378.371 | 21.184 | 208 | 12 | 0.048 | 0.956 | 0.002 | 0.059 | 0.000 |
| Portuguese | Scalar | 401.396 | 23.026 | 220 | 12 | 0.028 | 0.954 | 0.003 | 0.059 | 0.000 |
| Portuguese | Strict | 425.607 | 24.211 | 236 | 16 | 0.085 | 0.950 | 0.003 | 0.059 | 0.000 |
| Spanish | Configural | 467.069 | 196 | 0.916 | 0.077 | |||||
| Spanish | Metric | 479.648 | 12.580 | 208 | 12 | 0.400 | 0.913 | 0.004 | 0.077 | 0.001 |
| Spanish | Scalar | 498.411 | 18.763 | 220 | 12 | 0.094 | 0.911 | 0.002 | 0.075 | 0.001 |
| Spanish | Strict | 533.878 | 35.467 | 236 | 16 | 0.003 | 0.904 | 0.008 | 0.076 | 0.000 |
| Turkish | Configural | 765.972 | 196 | 0.895 | 0.108 | |||||
| Turkish | Metric | 796.985 | 31.013 | 208 | 12 | 0.002 | 0.892 | 0.003 | 0.107 | 0.002 |
| Turkish | Scalar | 834.690 | 37.705 | 220 | 12 | <0.001 | 0.888 | 0.004 | 0.105 | 0.001 |
| Turkish | Strict | 856.856 | 22.166 | 236 | 16 | 0.138 | 0.886 | 0.002 | 0.103 | 0.003 |
Measurement invariance with regard to sex.
Table 4
| Language | Model | χ 2 | Δχ2 | df | Δdf | p | CFI | ΔCFI | RMSEA | ΔRMSEA |
|---|---|---|---|---|---|---|---|---|---|---|
| Chinese (simplified) | Configural | 782.449 | 196 | 0.917 | 0.091 | |||||
| Chinese (simplified) | Metric | 795.391 | 12.942 | 208 | 12 | 0.373 | 0.917 | 0.000 | 0.089 | 0.003 |
| Chinese (simplified) | Scalar | 822.581 | 27.191 | 220 | 12 | 0.007 | 0.916 | 0.001 | 0.087 | 0.002 |
| Chinese (simplified) | Strict | 828.243 | 5.662 | 236 | 16 | 0.991 | 0.915 | 0.001 | 0.084 | 0.003 |
| Chinese (traditional) | Configural | 1064.389 | 196 | 0.894 | 0.103 | |||||
| Chinese (traditional) | Metric | 1106.218 | 41.829 | 208 | 12 | <0.001 | 0.891 | 0.002 | 0.101 | 0.002 |
| Chinese (traditional) | Scalar | 1156.406 | 5.189 | 220 | 12 | <0.001 | 0.888 | 0.003 | 0.100 | 0.001 |
| Chinese (traditional) | Strict | 1186.872 | 3.466 | 236 | 16 | 0.016 | 0.884 | 0.004 | 0.098 | 0.002 |
| English | Configural | 3474.126 | 196 | 0.916 | 0.076 | |||||
| English | Metric | 3531.847 | 57.721 | 208 | 12 | <0.001 | 0.915 | 0.001 | 0.074 | 0.002 |
| English | Scalar | 3682.031 | 15.184 | 220 | 12 | <0.001 | 0.912 | 0.003 | 0.073 | 0.001 |
| English | Strict | 4292.368 | 61.337 | 236 | 16 | <0.001 | 0.895 | 0.017 | 0.077 | 0.004 |
| Farsi | Configural | 407.490 | 196 | 0.871 | 0.110 | |||||
| Farsi | Metric | 424.371 | 16.881 | 208 | 12 | 0.154 | 0.867 | 0.004 | 0.108 | 0.001 |
| Farsi | Scalar | 438.834 | 14.463 | 220 | 12 | 0.272 | 0.866 | 0.001 | 0.106 | 0.003 |
| Farsi | Strict | 448.674 | 9.840 | 236 | 16 | 0.875 | 0.868 | 0.002 | 0.101 | 0.004 |
| Finnish | Configural | 366.456 | 196 | 0.912 | 0.089 | |||||
| Finnish | Metric | 375.606 | 9.150 | 208 | 12 | 0.690 | 0.913 | 0.001 | 0.086 | 0.003 |
| Finnish | Scalar | 391.541 | 15.935 | 220 | 12 | 0.194 | 0.911 | 0.001 | 0.084 | 0.002 |
| Finnish | Strict | 405.706 | 14.165 | 236 | 16 | 0.586 | 0.911 | 0.001 | 0.081 | 0.003 |
| German | Configural | 1164.390 | 196 | 0.952 | 0.069 | |||||
| German | Metric | 1192.162 | 27.772 | 208 | 12 | 0.006 | 0.951 | 0.001 | 0.067 | 0.001 |
| German | Scalar | 1407.062 | 214.900 | 220 | 12 | <0.001 | 0.941 | 0.010 | 0.071 | 0.004 |
| German | Strict | 1788.009 | 38.947 | 236 | 16 | <0.001 | 0.921 | 0.020 | 0.080 | 0.009 |
| Icelandic | Configural | 361.055 | 196 | 0.954 | 0.064 | |||||
| Icelandic | Metric | 393.531 | 32.476 | 208 | 12 | 0.001 | 0.949 | 0.006 | 0.065 | 0.002 |
| Icelandic | Scalar | 422.732 | 29.201 | 220 | 12 | 0.004 | 0.944 | 0.005 | 0.066 | 0.001 |
| Icelandic | Strict | 476.488 | 53.756 | 236 | 16 | <0.001 | 0.926 | 0.018 | 0.074 | 0.007 |
| Italian | Configural | 87.956 | 196 | 0.919 | 0.081 | |||||
| Italian | Metric | 928.266 | 57.310 | 208 | 12 | <0.001 | 0.914 | 0.005 | 0.081 | 0.000 |
| Italian | Scalar | 1105.393 | 177.127 | 220 | 12 | <0.001 | 0.895 | 0.019 | 0.087 | 0.006 |
| Italian | Strict | 1255.493 | 15.101 | 236 | 16 | <0.001 | 0.878 | 0.017 | 0.090 | 0.004 |
| Portuguese | Configural | 416.917 | 196 | 0.943 | 0.069 | |||||
| Portuguese | Metric | 43.341 | 13.424 | 208 | 12 | 0.339 | 0.943 | 0.000 | 0.067 | 0.002 |
| Portuguese | Scalar | 452.105 | 21.764 | 220 | 12 | 0.040 | 0.940 | 0.002 | 0.066 | 0.001 |
| Portuguese | Strict | 51.810 | 58.705 | 236 | 16 | <0.001 | 0.928 | 0.013 | 0.070 | 0.004 |
| Spanish | Configural | 504.322 | 196 | 0.905 | 0.083 | |||||
| Spanish | Metric | 524.940 | 2.618 | 208 | 12 | 0.056 | 0.903 | 0.002 | 0.081 | 0.002 |
| Spanish | Scalar | 563.295 | 38.355 | 220 | 12 | <0.001 | 0.895 | 0.008 | 0.082 | 0.001 |
| Spanish | Strict | 689.575 | 126.280 | 236 | 16 | <0.001 | 0.860 | 0.036 | 0.092 | 0.010 |
| Turkish | Configural | 733.018 | 196 | 0.904 | 0.103 | |||||
| Turkish | Metric | 745.663 | 12.645 | 208 | 12 | 0.395 | 0.904 | 0.000 | 0.100 | 0.003 |
| Turkish | Scalar | 788.416 | 42.753 | 220 | 12 | <0.001 | 0.899 | 0.005 | 0.100 | 0.000 |
| Turkish | Strict | 844.341 | 55.925 | 236 | 16 | <0.001 | 0.890 | 0.009 | 0.101 | 0.001 |
Measurement invariance with regard to age.
Discussion
The Profile of Mood States questionnaire is a widely utilized instrument in psychological research. Its applications range from basic research on cognitive issues to applied fields such as work psychology, athletics, and clinical settings. The study at hand aimed to assess the psychometric properties of the newly developed POMS-16 across 10 different languages. Specifically, we aimed to investigate whether the established four-factor structure of the instrument could be replicated in the languages previously described, while also examining measurement invariance across age and gender. Overall, the results of the factor analysis indicated a satisfactory fit for the majority of languages, with the exception of Chinese (traditional) and Turkish versions. However, even these versions with slightly worse fit may prove useful depending on their intended use case. Additionally, the majority of language versions demonstrated strong evidence of reliability across all subscales, although exceptions were observed for the Vigor subscale in the English, Icelandic, Portuguese, and Spanish versions. Regarding measurement invariance, the model for each language displayed evidence of strict invariance across gender and age groups, although some exceptions were noted.
Factorial structure
In detail, compared to the other language versions, the Chinese (traditional) and Turkish versions did not reveal a satisfactory fit. To date, the shortest Chinese version (Chen et al., 2002) comprises 30 items and was validated by showing excellent reliability and a one-factor structure, which makes it not comparable to our findings of a four-factor structure. In comparison to our scale, Chen et al. (2002) investigated a Taiwanese-speaking elderly community (i.e., aged 65 years and above). Similarly, there is no short version of the Turkish language version (Selvi et al., 2011). The latter replicated the original six-factor solution containing 58 items. Therefore, there is a need for further studies in order to verify our initial results of the short Turkish version. The data of the following language versions English, Farsi, Finnish, German, Icelandic, Italian, Portuguese, and Spanish provided evidence of a satisfactory fit model. It is worth noting that some studies have used an English version for Finish athletes (Heikura et al., 2023; Huttunen et al., 2004) or a modified version of the POMS, as applied by Azizi et al. (2021) in Iran during COVID-19. However, there are no official translations nor validity studies for the assessment of POMS in Farsi, Finish, or Icelandic. Consequently, the present instrument provides a valid short measure for the evaluation of mood states in these languages.
Furthermore, our revealed factor structure differs from the results provided by Cella et al. (1987), which represents the shortest English version available. In contrast to our studied population, it was validated in a cancer patient population, illustrating a one-factor model without the subscale Vigor. The German version in this study validates past results by Petrowski et al. (2021) showing a four-factorial structure and similar psychometric properties. The Italian short version of the POMS (Mannarini et al., 2012) shows a two-factor structure with 13 items, which was also evaluated in a patient sample with cancer. The authors renamed the scale to “Negative and Positive Mood State Short Form,” leaving out a differentiation of broader constructs, as depicted in the German version (Petrowski et al., 2021). Furthermore, our findings also differ from those provided in the Portuguese version of POMS (Pereira et al., 2023). Compared to our sample, the authors evaluated a short version based on student population, exhibiting a three-factor structure (i.e., depression, hostility, and vigor) encompassing 12 items. Finally, a short Spanish version (Andrade et al., 2010) demonstrated a five-factor structure with 30 items, validated in an athlete sample. Although shorter than the original, our newly validated Spanish version appears more suitable for purposes that require frequent and rapid self-monitoring.
Measurement invariance
To the best of our knowledge, the analysis of the present study provides the first evidence of measurement invariance in the reported languages with regard to gender and age. However, measurement invariance of the short German version of the POMS-16 has been previously assessed (Petrowski et al., 2021). The current results replicate past findings revealing gender invariance and strong evidence for age invariance. With the exception of Farsi, all of the other reported languages exhibited evidence for strict gender invariance, although Farsi was still strongly invariant. The Chinese, Farsi, Finish, and Turkish versions were strictly invariant across ages. The remaining versions did not show strict invariance for age, except for the Italian version, which supported strong invariance for age. In practice, strong invariance is considered sufficient to allow for valid comparisons between groups (Gregorich, 2006; Schmalbach and Zenger, 2019). In sum, the findings of this study have implications for both research and clinical practice. Overall, the POMS-16 questionnaire demonstrates satisfactory fit and strong reliability across most languages, indicating its validity for assessing mood states in diverse cultural contexts. However, exceptions in the Vigor subscale for certain language versions suggest the need for cautious interpretation and potential adaptation of the instrument. Despite this, measurement invariance across gender and age groups was generally supported, enhancing the utility of the POMS-16 for comparative research and clinical assessments. Additionally, the POMS in diverse language versions expands its accessibility and applicability, facilitating cross-cultural research and improving assessment accuracy in clinical settings. Future research should consider using the POMS for comparisons between the different cultures and countries examined in the present research.
Limitations
Data collection was carried out during the initial COVID lockdowns in the spring of 2020. Accordingly, it should be noted that this was quite a unique time, which was likely to have an influence on respondents’ moods. However, it remains unclear whether the evaluation process itself, via the questionnaire, was affected by this. Despite this, the overall psychometric results are comparable to those of previous research, without considering this special circumstance.
Conclusion
The present study aimed to assess the psychometric properties of the POMS-16 questionnaire across 10 languages, examining its four-factor structure and measurement invariance across age and gender. The results indicate a satisfactory fit for the majority of languages, except the Chinese (traditional) and Turkish versions. The majority of language versions showed strong reliability across subscales, with exceptions in the Vigor subscale for English, Icelandic, Portuguese, and Spanish versions. Measurement invariance was generally supported across gender and age groups, although some exceptions were noted. Notably, we provided the first version of the POMS in Farsi, Finnish, and Icelandic. These findings enhance the cross-cultural applicability of the POMS-16, contributing to its utility in diverse populations and thus enhancing the comparability of the results.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical approval was not required for the studies involving humans because the study included harmless questions regarding exercise and mood, with no risk to participants. In accordance with German law, it did not require ethical approval. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
IS: Supervision, Validation, Writing – original draft, Writing – review & editing. BS: Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. AA: Writing – original draft, Writing – review & editing. RB: Conceptualization, Data curation, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing. Y-KC: Writing – original draft, Writing – review & editing. MÇ: Writing – original draft, Writing – review & editing. HE: Writing – original draft, Writing – review & editing. JF: Writing – original draft, Writing – review & editing. ZH: Writing – original draft, Writing – review & editing. HK: Writing – original draft, Writing – review & editing. LM: Writing – original draft, Writing – review & editing. SN: Writing – original draft, Writing – review & editing. CP: Writing – original draft, Writing – review & editing. DR: Writing – original draft, Writing – review & editing. DM: Writing – original draft, Writing – review & editing. ST: Writing – original draft, Writing – review & editing. EB: Writing – review & editing, Data curation. KP: Project administration, Validation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Acknowledgments
We are grateful to Noora Ronkainen and Arto Pesola for the translation to Finnish and their support in the recruitment of participants in Finland.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1498717/full#supplementary-material
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Summary
Keywords
mood, affect, survey research, psychometric evaluation, confirmatory factor analysis
Citation
Schmalbach I, Schmalbach B, Aghababa A, Brand R, Chang Y-K, Çiftçi MC, Elsangedy H, Fernández Gavira J, Huang Z, Kristjánsdóttir H, Mallia L, Nosrat S, Pesce C, Rafnsson D, Medina Rebollo D, Timme S, Brähler E and Petrowski K (2025) Cross-cultural validation of the profile of mood scale: evaluation of the psychometric properties of short screening versions. Front. Psychol. 16:1498717. doi: 10.3389/fpsyg.2025.1498717
Received
19 September 2024
Accepted
24 January 2025
Published
17 March 2025
Volume
16 - 2025
Edited by
Ian van der Linde, Anglia Ruskin University, United Kingdom
Reviewed by
Marco Tommasi, University of Studies G. d’Annunzio Chieti and Pescara, Italy
Eric E. Pierson, Ball State University, United States
Updates
Copyright
© 2025 Schmalbach, Schmalbach, Aghababa, Brand, Chang, Çiftçi, Elsangedy, Fernández Gavira, Huang, Kristjánsdóttir, Mallia, Nosrat, Pesce, Rafnsson, Medina Rebollo, Timme, Brähler and Petrowski.
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*Correspondence: Bjarne Schmalbach, schmalbb@uni-mainz.de
†ORCID: Muhammet Cihat Çiftçi, orcid.org/0000-0002-6892-8564
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.