Anxiety and depression as roots of mental illness disorders

Anxiety and depression are believed to be the two primary roots of mental illness disorders, and often co-occur, which led to explain the common characteristics of anxiety and depression. The estimated annual expenditure in Australia is believed to be around twenty billion for the treatment of patients suffering from physiological diseases. Recently, Cummings, Caporino & Kendall (2014) researched with youths and children, explaining the relating features of apprehension and sadness. These conditions were individually portrayed by symptoms of distress and irritability; but confidence and happiness, physiologically, had a positive impact on these hyper-arousals. Specifically, MacNamara, Kotov & Hajcak (2016) projected the unique effect of negative emotion on physiological hyper-arousals. Lovi-bond & Lovi-bond (1995) explained the psychometrics of DASS-21 tool, distinguishing between anxiety and depression. The symptoms of agitation and tension (generalized anxiety) with physical arousal were differentiated utilizing the DAS scale. Factor investigation revealed that DASS components were consistent and reliable, which were explicitly clustered into three sub-levels, DASS-D (Depression), DASS-A (Anxiety), and DASS-S (Stress). The DASS-D scale measures symptoms associated with worthlessness or sadness. The Anxiety scale included symptoms of fear, panic attacks, and physical arousal. Finally, the Stress scale included articles such as irritability, overreaction, and tension.

Psychologists have conceptualized diverse benefits of optimism associated with an upbeat mood and effective problem solving. Again, depression is foreshadowed by pessimism; whereas failure and mortality characterizes depression. Mechanism of the evolutionary human brain is the eventuality of a selective process, contributing direct or indirect accomplishments. The complexity of evolved psychological human thought process and functions can be answered provisionally by psychologists. This article offers grounded reflection in conceptual and empirical elucidation with the explicit acknowledgment of interim nature. The article initiates by examining the ingredients of distress and depression and then offers suggestions on how to cluster the reactions of the respondents. Earlier, confirmatory factor analysis (CFA) has been used by the researchers to evaluate the structure of the DAS scale in non-clinical samples (Henry & Crawford, 2005). Trauer, Dodd, Callaly, Campbell, & Berk, (2007) examined the triple-factor structure paradigm, where DASS-21 was utilized for a large sample (N = 786) of psychiatric patients and compared alternative models using Mental Health Questionnaire (MHQ-14) and Health of the Nation Outcome Scales (HoNOS). Marianna (2010) utilized a professionally translated Vietnamese version DASS-21tool for a feminine clinical subjects from Ha Nam Province in northern Vietnam (N = 221). The subjects were detected with anxiety disorders, where the factor analysis results were found to be loaded in a single factor. High internal consistency (= 0.7 to 0.88) was noted in the one-factor structure. However, the model fit was not idyllic but sensitivity (79.1%) and specificity (77.0%) were very high for anxiety and depression, but no differentiation was observed with solitary anxiety or depression. A psychometric assessment of a non-clinical and normative sample using DASS-21 tool was performed on the large sample (N = 503), where the item-scale divergence was not fulfilled and the principal component analysis (PCA) extracted a single factor explaining mere 47% of the variance of the sample. The three-factor structure was later established by CFA (Sinclair et al., 2012).

DASS-21 tool to assess depression, anxiety, and stress

Regardless of numerous researches ascertaining the efficacy of DASS tools, DASS-21 has been less exercised in both non-clinical and clinical populations. The target of the scholar was to review the DASS-21 with exploratory factor analysis, analyzing the structure of DASS-21. Some studies used small samples (Gold et al., 2010), and raised doubts regarding the internal consistency and stability of the results. These methods were augmented by systematic investigative scaling assumptions of divergence and convergence of the DASS-21 item-scale, using the multi-trait analysis approach (Zhang, Verkuyten & Weesie, 2018). Assumptions behind the scaling were estimated along the norms by Cronbach (1951), Cohen, West & Aiken (2014), Nunnally & Bernstein (2008). The scholar hypothesized convergence of factors and statistically significant correlations with their hypothesized scale. This study evaluated DASS-21 for its psychometric properties, using a clinical sample of Queensland people, to aid comparative explanation with well-being subscales, such as MHC-SF, Kessler10, and flourishing scales. Internal consistency was hypothesized for DASS-21 sub-scales and item-scale convergence was assumed.

The participants (subjects) were chosen by a professional organization named Polimetrix Inc. Clinically suffering patients in North Queensland were identified for this purpose by Sample Matching. This proprietary sampling method assured that samples were unbiased and similar with some definite characteristics. Polimetrix used an internal database of the Australian people, earlier participated in research studies. Incentives were not offered to the subjects in this study.

Psychometric properties of the outpatients (n = 171; Mean age = 31.09 years; 75.4% female), diagnosed with sleep disturbance, panic disorder with or without stress, or major sadness disorder were collected with the DASS-21 tool. A faction of clinical volunteers served as an examiner group. All the participants signed the consent form and were between 18 years and 62 years. Exclusion criteria were used for patients diagnosed with drug – dependence, or psychotic disorder.

Table 1: Detailed Description of the Participants

The scholar exercised the data collection with DASS-21 tool that assessed depression, stress, and anxiety. The subjects comprehended these constructs and recorded their answers. The DASS tool contains a 4-point answer in accordance with a Likert-scale, which ranged from zero to three. For scale-level convergence assessment, the BASIS 24 item scale, the 10 item Kessler scale, and the MHC-SF well-being scale were also exercised on the participants.

The DASS-21 scale (M = 35.76; SD = 12.71) summary measures for the depression (Mean = 12.04; SD = 5.11), Anxiety (Mean = 10.54; SD = 4.21), and Stress (Mean = 13.19; SD=4.71) scales were nearly homogeneous to previous studies using clinical samples (Teo et al., 2018). The scholar and the clinicians interpreted the DASS-21 scale, utilizing scores to produce reference scores. The descriptive of other psychometric scales, such as MHC-SF (M = 57.25; SD = 14.38), Kessler10 (M = 21.76; SD = 7.62), Flourishing (M = 44.09; SD = 9.66), BASIS24 (M = 44.30; SD = 16.99), SWLS (M = 20.99; SD = 7.01), and LOTR (M = 15.28; SD = 4.41) were also measured for construct validity.

Examinations the assumptions of item convergence scaling were estimated by the approach of Sinclair et al. (2010). Pearson’s corrected correlations for item-scale were estimated to assess convergence. Normally, item-scale convergence supports an item with a correlation of 0.40 or above with its hypothesized scale (Reio, 2010). The item-scale factor loading also revealed the extraction in the pattern matrix, which eventually describes the convergence of components. Table 2 summarizes the convergence of all item scaling assumptions in factor loading. Overlapping of anxiety items in DASS – 21 were evident, whereas the depression components loaded smoothly. The stress factor failed to include all the components in the converged matrix, which were scattered in the anxiety item scale. Two of the three components had loading weight less than 0.4 and were insignificant in the current factor loading investigation.

Exploratory factor analysis for psychometric properties evaluation

Table 2: Factor Loading for Item Scale Convergence

Note: Extraction Method used was Principal Axis Factoring. Rotation converged in 6 iterations.

The discriminant validity of the items of the factors was estimated from the comparative analysis of average factor extraction and inter-factor correlation. All the variances extracted between the factors were greater than the respective R-square values, the exception was observed for the inter-relation between anxiety and stress. Hence, discriminant validity was established, except the anxiety-stress relation.  

Table 3: Discriminant Validity Check from Factor Loading in Pattern Matrix

Table 4 represents the range of item-scale correlation matrix; the hypothetical assessment of the item-scale convergence for stress and depression was found to be factual. The items of depression and stress factors correlated with 0.40 or greater values. The anxiety component (“I was aware of dryness of my mouth”) failed the assumption with a correlation of 0.22 to 0.33 with its hypothesized scale. The minimum correlations among the items in anxiety factor yielded a low level of item-scale correlations with their intended scale.

Table 4: Item-Scale Inter-Correlation for Convergence

Internal consistency or reliability for the study was estimated using Cronbach’s coefficient. Alphas greater than 0.70 (table 5) for each factor suggested internally consistent and reliable analysis. Table 5 represent these statistics (alpha = 0.93, 0.86, and 0.89 for the DASS-21), which were analogous to previous literature. Therefore, the reliability for the current research work was in tandem with earlier works (Antony, Bieling, Cox, Enns, & Swinson, 1998; Cameron et al., 2007; Pavot & Diener, 2009).

Table 5: Internal Consistency (Reliability) of Different Scales

Table 6 represents the internal consistency with table 5 presenting the reliability statistic of scale-level correlations for components of DASS-21 scale. The correlations between other synchronized measures were also established. The correlations, based on scale-level in the current analysis (DASS-21) were fairly larger than other scales. In some cases the factors approached their reliability values. Instance of the inter-correlations between stress and depression (R = 0.71), and anxiety and depression (R = 0.68) were larger compared to the study in BASIS24 personal and BASIS24 depression (R = 0.77) and BASIS depression and self-harm (R = .46), respectively. The correlation between BASIS psychotic and BASIS alcohol (R = 0.68) was comparable with DASS Anxiety and Stress (r = .68).  The results of inter-item correlation and reliability established the construct validity of the study judged against various previous pieces of literature.

Table 6: Construct Validity of the Study with Pearson’s Correlation

Utilizing the SPSS platform, exploratory factor analysis was used for structural analysis of DASS. A principal axis factoring extracted the factors and established the results of Scree test and Eigen values. All the criteria recommended a three-factor model with Eigen values as 10.72, 1.88, and 1.04, accounting for 59.53% of the variance. An Oblique rotation was applied on the initial factor analysis solution by Kaiser Normalization. The prior knowledge of the correlation between the three scales enforced a factor solution supported by a non-orthogonal rotation.  

The factor loading has been demonstrated in Table 2, where components with 0.30 or larger loadings were clustered to load on a single and particular factor. The last five factors loaded to the DASS-S tool; with two items of the stress had a composite structure, which eventually loaded on the anxiety factor. The first seven factors loaded in the DASS-D scale, and no other components had composite loading property. The final five factors loaded on to the DASS-A tool, but one of the items loaded on the depression factor. Overall compatibility with other studies, especially components with composite loadings has been correspondingly pointed out.

Comparison with well-being subscales

The DASS tool for assessing outcomes in psychiatric patients has become universal, especially for anxiety, depression, and life stress conditions. Since last decade, competence of DASS-42 (the complete scale) has been observed in both non-clinical and clinical samples for evaluation of the psychometric analysis (Costa, Marôco, Pinto?Gouveia, Ferreira, & Castilho, 2016). Nevertheless, research on DASS-21 is hitherto in its prelude stages and auxiliary investigation is required to elaborate the psychometric efficacy of DASS tool.

The scholar wanted to narrow down on several loop holes in the present study with DASS-21 item-level tests using a representative sample from Australia. In accordance with hypothesized concepts, internal consistency and item-scale convergence were achieved. However, discriminant validity demonstrated greater overlapping between the constructs.

Pearson’s inter-scale correlations were also better than other studies, and occasionally the internal consistency of different other scales were approached. Overlapping variance in the constructs was found, and the outline of results was consistent with the results of Sinclair et al. (2012). However, EFA results for stress and anxiety in DASS were somewhat not uniform, only the depression scale entirely met the standards for individual loading regarding internal consistency analysis. These outcomes of the study were somewhat in line with Henry and Crawford’s (2005) bi-factorial model. Future research was required for further clarity for the structural instrument and clear factor definition.

References

Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological assessment, 10(2), 176.

Cameron, I. M., Cunningham, L., Crawford, J. R., Eagles, J. M., Eisen, S. V., Lawton, K., …& Hamilton, R. J. (2007). Psychometric properties of the BASIS-24©(Behaviour and Symptom Identification Scale–Revised) mental health outcome measure. International Journal of Psychiatry in Clinical Practice, 11(1), 36-43.

Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation analysis for the behavioral sciences.Psychology Press.

Costa, J., Marôco, J., Pinto?Gouveia, J., Ferreira, C., & Castilho, P. (2016).Validation of the psychometric properties of the Self?Compassion Scale.Testing the factorial validity and factorial invariance of the measure among borderline personality disorder, anxiety disorder, eating disorder and general populations. Clinical Psychology & Psychotherapy, 23(5), 460-468.

Cummings, C. M., Caporino, N. E., & Kendall, P. C. (2014). Comorbidity of anxiety and depression in children and adolescents: 20 years after. Psychological bulletin, 140(3), 816.

Gold, E., Smith, A., Hopper, I., Herne, D., Tansey, G., & Hulland, C. (2010). Mindfulness-based stress reduction (MBSR) for primary school teachers. Journal of child and family studies, 19(2), 184-189.

Henry, J. D., & Crawford, J. R. (2005). The short?form version of the Depression Anxiety Stress Scales (DASS?21): Construct validity and normative data in a large non?clinical sample. British journal of clinical psychology, 44(2), 227-239.

MacNamara, A., Kotov, R., &Hajcak, G. (2016). Diagnostic and symptom-based predictors of emotional processing in generalized anxiety disorder and major depressive disorder: An event-related potential study. Cognitive therapy and research, 40(3), 275-289.

Ng, F., Trauer, T., Dodd, S., Callaly, T., Campbell, S., &Berk, M. (2007). The validity of the 21-item version of the Depression Anxiety Stress Scales as a routine clinical outcome measure. ActaNeuropsychiatrica, 19(5), 304-310.

Nunnally, J. C., & Bernstein, I. H. (2008).Psychometric theory.

Pavot, W., & Diener, E. (2009). Review of the satisfaction with life scale. In Assessing well-being (pp. 101-117). Springer, Dordrecht.

ReioJr, T. G. (2010). The threat of common method variance bias to theory building. Human Resource Development Review, 9(4), 405-411.

Sinclair, S. J., Blais, M. A., Gansler, D. A., Sandberg, E., Bistis, K., & LoCicero, A. (2010). Psychometric properties of the Rosenberg Self-Esteem Scale: Overall and across demographic groups living within the United States. Evaluation & the health professions, 33(1), 56-80.

Sinclair, S. J., Siefert, C. J., Slavin-Mulford, J. M., Stein, M. B., Renna, M., & Blais, M. A. (2012).Psychometric evaluation and normative data for the depression, anxiety, and stress scales-21 (DASS-21) in a nonclinical sample of US adults. Evaluation & the Health Professions, 35(3), 259-279.

Teo, Y. C., Yusuf, A. H., Lim, W. P. A., Ghazali, N. B., Rahman, H. A., Lin, N., &Koh, D. (2018). Validation of DASS-21 among nursing and midwifery students in Brunei. Journal of Public Health, 1-5.

Zhang, S., Verkuyten, M., & Weesie, J. (2018). Dual identity and psychological adjustment: A study among immigrant-origin members. Journal of Research in Personality, 74, 66-77.