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Habibi, Mojtaba, Khawaja, Nigar G., Moradi, Shahram, Dehghani,Mohsen, & Fadaei, Zahra(2014)University Students Depression Inventory : measurement model and psy-chometric properties.Australian Journal of Psychology, 66(3), pp. 149-157.

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https://doi.org/10.1111/ajpy.12037

University Students Depression Inventory

1

University Students Depression Inventory: Measurement model and Psychometric

Properties

Mojtaba Habibi a

Nigar G. Khawaja b

Shahram Moradi c

Mohsen Dehghani a

Zahra Fadaei a

Family Research Institute, Shahid Beheshti University, G. C., Iran a

School of Psychology and Counseling, Queensland University of Technology, Australia b

Linnaeus Centre HEAD, Department of Behavioral Sciences and Learning, Linköping

University, Linköping, Swedenc

Number of text pages: 28 pages

Number of tables: 4 tables

* Proofs and Correspondence:

Mojtaba Habibi

Shahid Beheshti University, Tehran-Iran.

P.O. Box: 1983963113

Zip Code: 19395 - 4716

Tel: +98-21- 22 43 18 14

Fax: +98-21- 29 90 23 68

Email: [email protected] (Mojtaba Habibi)

mailto:[email protected]


2

Abstract

Depression is a serious condition that impacts the academic success and emotional

well-being of the university students globally. Keeping in view the debilitating nature of this

condition, the present study examined the stability of the factor structure and psychometric

properties of the University Student Depression Inventory (USDI; Khawaja and Bryden,

2006). There is a need to translate and validate the scale for Persian speaking students, who

live in Iran, its neighboring countries and in many other Western countries. The scale was

translated into the Persian language and was used as part of a battery consisting of the scales

measuring suicide, depression, stress, happiness and academic achievement. The battery was

administered to 359 undergraduate students, and an additional 150 students who had been

referred to the mental health center of the University of Tehran as clinical

sample. Confirmatory factor analysis upheld the original three-factor structure. The results

exhibited internal consistency, test-retest reliability, convergent, and divergent validity, and

discriminant validity. There were gender differences and male had higher mean scores on

Lethargy, Cognitive\emotion, and Academic motivation subscales than female students.

Findings supported the Persian version of the USDI for cross-cultural use as a valid and

reliable measure in the diagnosis of depression.

Key words: University students' depression, confirmatory factor analysis.


3

Introduction

University life is associated with many changes and challenges, which may lead to the

experiences of stress and depression (Bayatiet al., 2009; Verger et al., 2010). Compared to

the general population, previous studies have described particularly high levels of depression

among college students (Adlaf et al., 2001) due to new and unfamiliar circumstances,

separation from family members, adjustment issues, lack of interest in their selected

discipline, learning challenges, and academic stressors (Besharat et al., 2006; Friedlander.,

2007). Depression is considered a dangerous problem among college students (Bayram and

Bilgel, 2008), as depressed students report more academic difficulties than their peers (Vaez

and Laflamme, 2008; Yeh et al., 2007).

Khawaja and Bryden (2006) developed the University Student Depression Inventory

(USDI), which consists of 30 items that measure students’ levels of depression based on a 5-

point Likert scale. The items fall into three sub-scales: lethargy (L), cognitive/emotional

(CE), and academic motivation (AM). This three-factor model accounts for 78.86% of the

variance of the USDI. The L scale (nine items) focuses on exhaustion, both mental

(concentration difficulties) and physical (e.g., ‘I am more tired than I used to be’). The CE

scale (14 items) targets the cognitive and emotional factors of depression, such as suicidal

ideation, worthlessness, sadness, and emotional emptiness (e.g., ‘I feel worthless’). The AM

scale (seven items) assesses motivation related to academic work (e.g., ‘I have no desire to

attend lectures’). The USDI score ranges from 30 to 150, with higher scores indicating higher

levels of depression. The reliability and validity of this instrument are well reported (Khawaja

and Bryden, 2006).

Research suggests that a considerable percentage of Iranian students exhibit various

levels of depression or are at risk of becoming depressed, with an overall depression rate of

8.5-44% (Amini and Farhadi, 1999; Bagheri-Yazdi et al., 1995; Ebrahimi and Keyghobadi,


4

2004; Rahimi and Kamran-Pour, 2006). Although data emerging from the West indicate that

depression is more prevalent among female than male students (Khawaja and Duncanson,

2008; Wells et al., 2006), the results are mixed in Iran, where research has shown male

students to be more vulnerable than women. Although depression is reported by female

students in Iran (Bayani et al., 2008), the rate is not as high as among males. However, some

researchers have found only non-significant differences based on gender (Ghassemzadeh et

al., 2005; Jahani et al., 2008). The results of these studies are based on different assessment

tools (Bayati et al., 2009; Ghassemzadeh et al., 2005; Hojat., 1986a, 1986b). The striking

directional similarity between male/female ratios for depression among Iranian students

requires explanation with proper tools. The question of whether this gender-based

discrepancy in the prevalence of depression is caused by real differences in incidence or

mislabeling remains open (Mellsop and Smith, 2007).

The literature review indicates that in spite of the global nature of depression, the bulk

of the previous research has examined depression among western student populations. While

the original USDI has been investigated and used in Australia (Khawaja and Bryden, 2006),

no published psychometric studies of the USDI in non-Western cultures are known to the

authors. The Persian edition is expected to find widespread use in various settings in Iran and

other Persian-speaking countries and among practitioners working with Persian-speaking

clients in Western countries. This preliminary study may encourage researchers to further

examine the psychometric properties of the test in other clinical samples. Although the

English version of the USDI has been determined psychometrically sound, to date no Persian-

language translation has been described. Further, no study has tested the construct validity of

the USDI in a sample drawn from Asian and Middle Eastern student populations. The present

study aimed to translate the USDI into the Persian language and validate it for use with

Iranian students. It was expected that the instrument’s factor structure and psychometric


5

properties would be upheld with the Iranian student population. Further, it was expected that

the Persian version would reveal a possible gender difference. The study also aimed to

develop a cut of points for the Iranian student population.

Method

Participants

The study participants were grouped into two samples: general students (Normative

sample) and students who had been diagnosed with depression (Clinical sample). Although

400 undergraduate students volunteered to participate in the Normative sample, 41 (10%)

students were excluded because they did not meet the inclusion criteria. The participants

comprised 359 undergraduate students (206 male and 151 females; the data were missing for

2 participants) from Tehran University. Their average age was 21.26 years (SD=2.60; range

17 to 33). Two hundred and fifty-seven were single, 32 were married, one was divorced, and

69 participants did not specify their marital status. One hundred and eighty-seven lived in

dormitories, 72 had families, and 100 lived at a rental accommodation. Of the full sample, 30

male and 30 female students took the inventory, with a 4-week interval to investigate the test-

retest reliability.

The Clinical sample consisted of 150 students who had sought help as outpatients at the

university health care center. Seventy of the participants were males, and eighty were

females. Of the sample, 79 had been diagnosed with Major Depression based on DSM-IV

interviews by psychiatrists, and 71 were non-depressed. The average age of the sample was

21.46 years (SD=2.40; range 17 to 29). One hundred and twenty-five participants were single,

22 were married, three did not specify their marital status, 76 lived in dormitories, 60 lived

with their families, and 14 resided in rental accommodations.


6

Measures

University Students Depression Inventory (USDI): The USDI is a 30-item self-report

questionnaire rated on a 5-point scale (1-5). A study by Khawaja and Bryden (2006) loaded

the factor analysis of the scale onto three sub-scales: L, CM, and AM. This scale has sound

psychometric properties. Its Cronbach’s alpha is α = .95, and the internal consistencies of the

sub-scales of L, CM, and AM are .89, .92, and .84, respectively. The correlation coefficient

for its test-retest reliability with a one-week interval has been reported to be .86. The scale

was found to have good convergent validity with a strong positive relationship with the

Depression, Anxiety, and Stress Scale (DASS) (Khawaja and Bryden, 2006).

Suicide Ideation Scale (SIS): The SIS was made up of 38 items, ranging from 1 to 3

points on the Likert scale. It was developed in the Persian language to address the student

population (Mohammadifar et al., 2006). An exploratory factor analysis revealed five sub-

scales, labeled as feelings of guilt, hopelessness, withdrawal, inertia, and depression. The SIS

has presented a good internal consistency for the total score and sub-scales ranging from .72

to .93; the 2-week test-retest reliability for the total and sub-scales ranged from .81 to .89.

The convergent validity of the total score of SIS with the Beck hopelessness scale (Beck et

al., 1974) was satisfactory (r= -.31, p < .001).

Beck Depression Inventory Second Edition: Persian version (BDI-II): The BDI-II

consists of 21 self-report items rated on a 4-point scale (0-3). It is used to measure depression

and comprises general depression and hopelessness, emotional distress, negative attitude, and

psychosomatic disorder factors. The BDI-II’s internal consistency and one-week test-retest

reliability among younger adult outpatients were .92, and .93, respectively (Beck., 1996). The

psychometric properties (i.e., validity and reliability) of the BDI-II have been confirmed


7

among Iranian samples (Rajabi., 2001). The Persian version of the BDI-II is used in the

current study. The depression scale of this version was determined to have good internal

reliability, with a Cronbach`s alpha of .85.

Oxford Happiness Inventory (OHI): The OHI (Argyle., 1989) comprises 29 items rated

on a 4-point scale (0-3) to reflect levels of happiness. Using Cronbach’s alpha and

considering a sample of Persian students (n =727), the OHI’s internal consistency was .92

(Alipour and Nor-bala, 1999).

Student-Life Stress Inventory (SSI): The SSI (Gadzella, 1994) assessed university

students’ life stressors and reactions to stressors, categorized into five types of stressors

(frustrations, conflicts, pressures, changes, and self-imposed) and four types of reaction to

stressors (physiological, emotional, behavioral, and cognitive appraisal). The scale comprised

51 items measured on a 5-point Likert scale, ranging from 1 (no, never) to 5 (most of the

time). Investigations of psychometric properties supported an internal consistency ranging

from .63 to .92 and the instrument’s factor structure (Gadzella and Baloglu, 2001). A Persian

version of SSI (Shokri et al., 2008) was used in the current study.

Procedures

Team members who were fluent in the Persian and English languages, including

linguistics experts and psychologists (in addition to the principal investigator), translated the

USDI into Persian using the guidelines for the cross-cultural adaptation of instruments

(Guillemin., 1993; Villagran and Lucke, 2005). Any differences were judged and resolved

based on the consensus reached by the mental health professionals on the research team.

After the back translation, the differences were resolved by agreement, which resulted in the


8

final version of the instrument. Finally, the primary version of USDI was administered in a

pilot study of 30 students to assess the readability and clarity of the items in the Persian-

language version of the USDI. Apart from a few minor adjustments to the wording and

layout, the Persian version was similar to the original USDI.

The ethical approval of the university’s research committee was obtained for the data

collection. The study was advertised on the university campus, and students were invited to

participate. It should be noted that the researchers informed the students that their

participation was voluntary and that they could discontinue at any time. The students were

also notified that their participation was anonymous and their data would remain confidential.

Of the total sample, sixty students completed the USDI twice with a four-week interval for

test-retest reliability purposes.

Results

Preliminary Analyses

The data set was cleaned and screened. The assumption of normality was checked, and

a slight skew was evident in the sub-scales but not in the total USDI score. The decision was

made not to transform the data because the data set was large and the transformation would

not improve the results (Tabachnick and Fidell, 2013). The decision whether to remove or

retain the outliers was made by comparing the original mean with the 5% trimmed mean

(Pallant, 2010; Tabachnick and Fidell, 2013).

Using Cronbach’s alpha, the internal consistency of the measures used with the

Normative group in the study was examined. The internal consistency was overall

satisfactory. The Cronbach's alphas for SIS, BDI, OHI, and SSI were .88, .85, .89, and 93,

respectively.


9

Design

Confirmatory factor analysis was selected to examine the USDI’s stability. This method

offers a variety of statistical tests and indices designed to assess the "goodness-of-fit" of the

identified models (Maccallum., 1996). In the present study, the goodness-of-fit was evaluated

using the following statistics: the goodness-of-fit index (GFI >.90), the adjusted goodness-of-

fit index (AGFI > .90), the non-normal fit index (NNFI > .90), the comparative fit index (CFI

> .90), the root mean square residual (RMSR < .10), the normal chi-square (3 > χ2/df < 2) and

the root mean square error of approximation (RMSEA) and its 90% confidence interval < .08

(Bentler and Bonett, 1980; Loehlin, 2004; Miles and Shevlin, 2007). Multiple indices were

used because they provide different information about the model’s fit (i.e., absolute fit, fit

adjusting for model parsimony, fit relative to a null model). Used together, these indices

provide a more conservative and reliable evaluation of the solution (Maruyama, 1997). Due

to multivariate skewness in the data, the fit indexes (except SRMR) of all the models were

corrected with the Satorra-Bentler scaled difference chi-square test statistic (Bentler, 1995;

Hu., 1992).

The fitted models were nested; in these instances, the comparative fit was evaluated by

χ2 difference tests (∆ χ2) and the interpretability of the solutions. The concurrent validity was

investigated by examining the correlations between the USDI scores and OHI, SIS, BDI-II,

ASAA, and SSI. To evaluate the test-retest reliability of the USDI, Spearman correlation

coefficients were calculated at two points of time over four weeks for the total scale and three

sub-scales. Cronbach`s alpha and mean inter-item correlation coefficients were calculated for

the total USDI and its sub-scales (Hammond, 2000). To explore the relationship between the

USDI and the remaining measures, the Spearman r correlation was used to address the

skewness of the scores. Given the number of correlations, the p values were set at .012 to

control for the experiment wise error. The Bonferroni adjustment was used: an initial α of .05


10

was divided by the number of measures, or .05/4 (Howell, 2009). The depressed cases were

compared with the non-depressed students in the Clinical sample to examine the discriminant

analysis. Logistic regression analyses were conducted to evaluate the unique contributions of

the USDI and subscale scores to predict the risk factors and differentiate the group of

students with a diagnosis of depression from those who did not meet the diagnostic criteria.

Logistic regression allows for the prediction of group membership when the predictors are

continuous, discrete, or combination of the two. Therefore, it is an alternative to both

discriminant and logistic analyses. Logistic regression allows an evaluation of the odds (or

probability) of membership in one of the groups based on the combination of values of the

predictor variables (Tabachnick and Fidell, 2013). Finally, to develop cut-off points, the

receiver operating characteristic (ROC) curve analysis method was used (Beck and Shultz,

1986).

Confirmatory Factor Analyses

LISREL version 8.72 (Jöreskog and Sörbom, 2005) was applied to the current data to

examine the fitness of three Models. Model 1 describes a one-factor model in which all 30

items were made to load on a single factor of general depression symptoms. Model 2 presents

a three-factor orthogonal model, and Model 3 examines a three-factor oblique model, as

reported by Khawaja and Bryden for the EFA procedure (2006). The oblique model was used

because we expected the factors to be theoretically correlated. For all the Models, the

variance of each factor was set to a 1.0. Z score for the univariate skewness values ranging

from –.37 (Item 21, “My mood affects my ability to carry out assigned tasks”) to 6.58 (Item

7, “I have thought about killing myself”; Table 1). Thus, we used the weighted list square

(WLS) due to its lower sensitivity to normality (Bentler and Bonett, 1980).


11

Table 1 presents the results of the fit estimates for the one-factor and three-factor

models. The one-factor model and the three-factor orthogonal model did not meet the

previously specified fit criteria. The chi-squared test was significant for all the models, but

that is to be expected for models with large degrees of freedom and relatively large sample

sizes (Bentler, 1995). An examination of the remaining fit indices suggested that the Satorra-

Bentler scaled difference chi-square test statistic (Satorra and Bentler, 2001) test among the

nested models and, importantly, the best fitting model was significantly better than the three

correlated factor models (∆χ2 = 229.35; P<.001). In a comparison of the nested models, the

x2-difference test (Jöreskog and Sörbom, 1993) showed that the three-factor oblique model

provided a better fit, although it was not satisfactory [S-B χ2/df = 2.58; CFI= .97; NNFI= .97;

and RMSEA = .066 ([CI] 90% = .061, .071]. The correlation between the L and CE latent

variables was .74 (p<.001); between L and AM it was .46(p< .001), and between CE and AM

it was .70 (p< .001). The factor loadings for the three-factor oblique model and the R2 values

for the L, CE, and AM items show adequate loadings on the related factors ranging from .24

to .83, .38 to .90, and .53 to .94, respectively (Table 1). In addition, the three-factor oblique

model provided a better fit for the present sample (all p’s <.001).

-------------------------

Insert table 1 about here

--------------------------

Consistency and test-retest reliability

Table 2 presents the mean, standard deviation, internal consistency coefficients, and

mean inter-item correlation of the USDI based on sample type (Normative/Clinical). Table 3

depicts the test-retest reliability of the USDI according to the Spearman correlation

coefficients between the total and subscale scores at times 1 and 2. The range of correlation


12

coefficients extends from .69 to .86, which was moderately higher. In the present study, the

means of inter-item correlation were .44, .45, .49, and .36 for L, CE, AM, and USDI,

respectively. In the inter-item correlation range between .2 and .4 and the corrected item-total

correlation, all the items performed adequately (range of .37 to .75). The Cronbach’s alpha

coefficients for the USDI total scores in this case were .94 and .83 in the Normative and

Clinical groups, respectively.

-------------------------


--------------------------

-------------------------


---------------------------

Convergent, divergent and discriminant validity

Table 3 presents the Spearman correlation coefficients between USDI and BDI-II, SIS,

OHI, ASAA, and SSI. The results showed that the USDI has a strong relationship with the

scales described above, indicating a good convergent validity. A logistic regression analysis

was conducted to differentiate the group of students with a diagnosis of depression (79 out of

the 150 members of the Clinical group) from the non-depressed students in that group in the

Clinical sample. To avoid violating the assumption of multicollinearity, the total USDI score

and the subscale scores were analyzed separately. The traditional estimates were examined,

including the standardized coefficients, the odd ratios (OR) with 95% CIs, and the Wald ratio

of each equation to evaluate the contribution of the USDI and its sub-scales using the

Backward Stepwise (likelihood ratio) method. However, only the estimates that offered a

95% accuracy estimate with a lower bound of 1 were identified as useful measures of risk


13

factors (Kleinbaum et al., 1982) for depression-related behaviors. The L [estimate= .07, Wald

ratio =4.34, p< .05; OR=1.08, 95% CI=1.01, 1.16], CE [estimate= .11, Wald ratio = 24.63, p

< .001; OR= 1.12, 95% CI = 1.07, 1.18] and AM [estimate= .12, Wald ratio = 15.71, p <

.001; OR= 1.13, 95% CI = 1.06, 1.20] sub-scales were revealed to be significant and useful

measures for differentiating groups. All the sub-scales together presented an overall

classification accuracy estimation of 84.7%. An analysis was then followed using the total

USDI scores [estimate= .11, Wald ratio =37.26, p< .001; OR=1.11, 95% CI=1.07, 1.15],

which supported the fact that the subscales significantly are capable of differentiating the

groups with an overall classification accuracy estimate of 81.3%.

Gender differences

Table 2 presents the mean and standard deviations of the USDI and its sub-scales for

males and females separately in both the Clinical and the Normative groups. In the Normative

group, the male students scored significantly higher than the females on their total USDI

scores [t (355) =3.52, p< .001]. The male students in the Clinical group also scored slightly

higher than the females on their total USDI scores; however, the difference did not reach a

significant level [t (148) = .21, p=.83, ns]. In addition, a MANOVA was conducted to

investigate the gender-based difference between males and females on the three USDI sub-

scales (as dependent variables) with gender used as an independent variable in the analysis.

The Box’s M assumption of the homogeneity of variance-covariance matrices was violated in

the Normative group, [F Normative (6, 720162.8) =2.44, p<.05], and [F Clinical (6, 48408.35)

=1.00, p= .42]. However, Box’s M is considered a notoriously sensitive test, while

MANOVA is robust to violations of its homogeneity of variance when the sample sizes are

large (Tabachnick and Fidell, 2013). Gender also had a significant effect on the USDI sub-

scales in the Normative sample: Hotelling`s Trace F Normative (3, 353) =4.82, p< .01, partial


14

Eta squared = .04; and Hotelling`s Trace F Clinical (3, 146) =.28, p= .84, ns, partial Eta

squared=.01. This effect was observed univariately on the USDI sub-scales. In the Normative

sample, the males scored significantly higher than the females on the L, CE, and AM sub-

scales: [F (1, 355) =10.54, p< .01], [F (1, 355) =7.82, p<.01, and [F (1, 355) =8.85, p< .01],

respectively. In the Clinical sample, there was no significant difference between males and

females on the L, CE, and AM sub-scales: [F (1, 148) = .12, p= .73, ns], [F (1, 148) = .08, p=

.78, ns], and [F (1, 148) = .20, p= .66, ns], respectively.

Cut-off scores

A ROC curve, which plots sensitivity versus specificity for every possible cut-off point,

was obtained. Youden’s index was used to evaluate the optimal cut-off point

(sensitivity+specificity-1.00) (Viinamaki et al., 2004). Sensitivity and specificity indices were

calculated for all the possible USDI cut-off points. The ROC curve was calculated to estimate

the instrument’s discriminant capability. USDI raw scores were analyzed to classify both at-

risk and non-risk groups. The best USDI cut-off point for females is 91 with a sensitivity of

85.37% and a specificity of 84.62%, indicating that 15.38% of the non-risk group and

85.37% of the at-risk group exceeded the cut-off of 91. The area under the curve was .89

[(95% CI) = .79 to .95, p<.001]. The best USDI cut-off point for males is 85 with a sensitivity

of 92.11% and a specificity of 78.12%, indicating that 21.88% of the non-risk group and

92.11% of the at-risk group scored beyond the cut-off of 85. In this case, the area under the

curve was .91 [(95% CI) = .82 to .97, p<.001]. These cut-offs were applied because they were

revealed to be the most optimal combination of the points in the sensitivity and specificity

indices (Perkins and Schisterman, 2006). Comparisons of the area under curve (AUC) in both

sexes showed that no significant difference in the AUC is associated with gender (d= .03,

S.E. = .05, Z=.54, P= .59, ns).


15

Normative data for the USDI

Given the positive skewness, the means and SDs from the Normative sample are not

useful when interpreting an individual’s USDI score. Therefore, Table 4 was produced after

transforming the raw USDI scores into percentiles. The tabulation method in Table 4 was

adopted to translate these raw scores to percentiles for all three sub-scales and the total scale

within the same table. The table shows that in a few cases, a given raw score can correspond

to more than one percentile due to the granularity of the raw scores (e.g., for the CE scale, a

raw score of 42 spans the 79th to 82th percentiles).

Discussion

The main objective of the present study was to examine the factor structure,

psychometric properties, and clinical utility of the USDI. In particular, this study focused on

fulfilling five objectives: (1) to evaluate the factor structure of a Persian version of the USDI;

(2) to examine the associations between the USDI and the SIS, BDI, OHS, ASAA, and SSI to

test its convergent and discriminant validity; (3) to evaluate possible gender differences in the

the mean USDI scores of Iranian university students; (4) to prepare a cut-off point USDI

score to discriminate depressed from non-depressed cases; and (5) to provide normative data

for the Persian USDI in the form of tables that allow raw scores to be transformed into

percentiles.

The findings resulted in the 30-item Persian instrument referred to as the USDI,

indicating that the content of the translated items was identical to that of the original USDI.

The USDI’s three original factors were upheld in the Persian version. The results indicated

that neither a one-dimensional modal nor an orthogonal three-factor model could meet the fit

indices. However, the fit indices of the oblique three-factor modified model were satisfactory.

The oblique three-factor structure of the USDI emerged as the best fit for the data, which is


16

consistent with the findings of Khawaja and Bryden (2006). The findings of this study

corroborate the three original dimensions. It appears that even in the Iranian population,

manifestations of L and CE and a decrease in the AM emerged as the main features of

depression among university students.

The USDI has been identified as an internally consistent scale. The Cronbach’s alpha

coefficients in the Normative and Clinical groups were above .7, and the item-total

correlations exceeded the minimum acceptable value of .30 (Nunnally and Bernstein, 1994).

In the present study, all the items performed adequately (within a range of .37 to .75).

Therefore, the results supported the internal consistency of the overall scale and its sub-

scales, and the items within each subscale were thematically linked.

Consistent with Khawaja and Bryden’s outcome (2006), the validity of the USDI was

robust. The concurrent validity showed that that USDI was negatively correlated with OHI

and ASAA (Struthers., 2000). This indicates that the USDI, which measures depressive

symptoms in the student population, differs from scales measuring student-life happiness,

suicidal ideation, and stress. Therefore, its divergent validity is supported. Nevertheless, the

instrument is significantly positively correlated with SIS, BDI-II, and SSI, which provides

strong evidence for its convergent validity. As expected, all three USDI sub-scales were also

significantly correlated with the OHI, SIS, BDI, ASAA, and SSI scores in the same directions

as the total scores. The USDI’s discriminative validity was also established, and the scale was

able to differentiate the students who had been diagnosed with depression from those who did

not meet the diagnostic criteria. This finding indicated that the USDI was able to measure the

severity of depression-related behaviors in the sample of university students. The USDI sub-


17

scales were found to be useful measures to differentiate between significantly depressed and

non-depressed individuals.

The study’s results showed that the male students had significantly higher scores on all

three dimensions of the USDI than the female students in the Normative group. It is possible

that these differences may be related to cultural issues (Goldberg., 1998; Sen and Mari,

1986). Cultural expectations regarding gender roles in the society have profound effects on

mental health in general and depression in particular (Kokanovic et al., 2008; Okello and

Ekblad, 2006). For example, finding a limited number of job opportunities after graduation

will have more severe effects on male student than female students (Bayati et al., 2009). Prior

studies in Iran have shown that university education is a more stressful event for males than

for females (Dehshiri et al., 2008; Khodayari-Fard et al., 2004), which accords with evidence

that stressful life events are associated with depression and that depressive events are more

common in the Iranian male university student (Khodayari-Fard et al., 2004; Rezai et al.,

2007). Another reason may be related to male and female students’ different styles and use of

coping skills when confronted with distress (Courtenay, 2000; Pillay and Ngcobo, 2010)

and/or their different help-seeking behaviors (Beck et al., 1996; Galdas et al., 2005; Khawaja

and Bryden, 2006; Martin Jr et al., 1999; Martin et al., 1999; McCreanor and Nairn, 2002;

Mellsop and Smith, 2007; Tapsell and Mellsop, 2007; Vredenburg et al., 1988b). Due to the

non-significant difference between the males and females in the Clinical group, we assume

that the males and females in the normal group probably had different reactions in their

confrontations with stressful events, resulting in gender differences in the USDI-PV scores.

However, in the Clinical group, the males and females exhibited very similar reactions when

facing stressful events; therefore, the differences in the Clinical group were not gender-based.


18

Clinical Utility

For use in clinical application, the cut-off points and Normative group data were

prepared for the Iranian student population. Skewness, whether positive or negative, is not

necessarily an indication of a psychometric problem; rather, it reflects the underlying nature

of the construct being measured. In fact, clinical measures of anxiety or depression are often

expected to be positively skewed in the general population, with most people reporting

relatively few symptoms of these disorders (Pallant, 2010). Despite the development of a

variety of valid measures for depression (e.g., BDI, Center for Epidemiologic Studies

Depression Scale (CESS), and DASS with clinical and research applications), it appears that

most of the items in the scales described above were developed for clinical settings and are,

therefore, not specifically suited to the student population (Beck et al., 1996). Studies argue

that students’ symptoms of depression differ somewhat from those of the general population

(Cox et al., 2001), and cognitive problems are more frequent in students (Cox et al., 1999). It

has been reported that physiological items, such as problems with sleep or eating, are not

reliable indicators of depression among college students (Kitamura et al., 2004). Thus, the

development of reliable and validated measurements for this population is necessary.

Expressions of sadness and depression are strongly related to cultural and even linguistic

issues (Wierzbicka, 1999;Falicov, 2003; Tsai and Chentsova-Dutton, 2002). Furthermore,

there is a need to adapt and translate the scale into languages appropriate to various cultural

groups with their own cut points and Normative data.

Limitations and Future Directions: This study presents a validated version of the USDI

for use with Iranian students. However, there are a few limitations that cannot go

unmentioned. First, only self-reported data were included, which may not be in compliance

with structured interviews toward the diagnosis of depression. Second, although a sufficient


19

sample of depressed students who were clinical outpatients at mental health centers and a

large Normative student sample were studied, all the students came from a large,

metropolitan university in Tehran. As a result, the ethnic diversity of the sample was limited,

and generalizations of these results to students of different ethnic backgrounds in other

Iranian universities should be made with caution. Third, it is possible that the observed

differences between males and females are spurious and confounded by other variables

related to personality and/or socioeconomic factors. The way in which gender-based

behaviors develop in western and eastern cultures could impact patterns of depression that

should be investigated in future research.

Conclusion

Despite the limitations described above, the psychometric properties of the Persian

USDI are primarily consistent with those reported for the English-language version of the test

among Australian university college students. Exploring cultural differences in the

development and manifestation of depression at the clinical level in Iran is not yet robust; it

requires more extensive and comparative studies in various clinical settings and populations.

If future research provides psychometric confirmation for the USDI in Clinical samples, this

instrument will prove itself to be a useful measure for comparative trans-cultural studies.

Future research must also assess the utility of the USDI as a screening and outcome measure

in Iranian and other Persian-speaking clinical populations when it is administered with BDI-II

and other diagnostic methods. This process will develop USDI into a screening tool for

depression in students, given its sensitivity, specificity and utility in clinical settings. The

USDI appears to be a promising tool for use in screening for depression among university

students. The scale is robust with a stable factor structure. Its psychometric properties are

comparable to other widely used measures of depression, although it is too early to draw


20

conclusions about the USDI’s clinical utility. However, the existing evidence recommends

this tool for research purposes and potentially for use in identifying students at risk of

depression.

Acknowledgment

We would like to offer our appreciation to the clinical staff and academics at the University

of Tehran and its mental health center for their support and assistance in data collection.

Special thanks go to M.A. Besharat, B. Ghobari, A. Asghari, D. Bourzabadi, and Y. Pour-

Mohammad, who assisted in the translation and back translation of the USDI between the

English and Persian languages.

Declaration of interest: The authors report no conflicts of interest. The authors alone are

responsible for the content and authorship of this paper.

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29

Table 1

Parameter estimates and goodness-of-fit indexes for CFA of the USDI

Items P.E.O P.E S.E R2 Z T.V r α

Factor 1: L 1. I am more tired than

.74 .81 .05 .66 .96 15.79 .62 .86 4. I do not have the

.72 .77 .05 .59 .50 14.13 .62 .86 9. My energy is low .67 .91 .05 .83 1.66 19.13 .71 .86 13. I find it hard to

.61 .89 .05 .79 .98 18.16 .70 .86 16. I don't feel rested

.56 .85 .05 .72 2.21* 15.78 .68 .86 18. Challenges I

.49 .76 .05 .58 1.76 14.39 .62 .86 21. My mood affects

.46 .49 .06 .24 -.37 7.62 .37 .89 24. Daily tasks take me

.44 .73 .05 .53 1.47 14.95 .63 .86 28. My study is

.44 .87 .05 .76 .36 16.46 .63 .86 Factor 2: CE

2. I wonder whether

.85 .77 .05 .59 3.41** 15.71 .61 .92 5. I feel worthless .76 .91 .05 .83 3.80** 17.18 .72 .91 7. I have thought about

.74 .66 .06 .44 6.58** 11.55 .59 .92 10. No one cares about

.70 .79 .05 .62 2.91** 16.83 .66 .91 11. I feel emotionally

.58 .86 .05 .74 1.82 18.77 .68 .91 14. I feel sad .57 .87 .04 .76 .67 19.54 .73 .91 15. I worry I will not

.57 .72 .05 .52 1.68 13.42 .59 .92 17. The activities I

.55 .72 .05 .52 1.08 14.55 .62 .92 19. I feel like I cannot

.51 .62 .05 .38 2.44 11.09 .58 .92 20. I spend more time

.51 .87 .05 .76 .84 16.71 .64 .91 22. I feel disappointed

.49 .95 .04 .90 1.51 21.56 .75 .91 25. I feel withdrawn

.49 .78 .05 .61 2.97** 16.42 .68 .91 26. I do not cope well .46 .71 .04 .50 1.68 16.55 .68 .91 29. I think most people

.44 .57 .06 .32 1.56 10.01 .47 .92 Factor 3: AM

3. I do not have any

.68 .73 .07 .53 .85 10.84 .52 .87 6. I don't attend

.67 .89 .06 .79 1.32 15.51 .64 .85 8. I don't feel motivated

.58 .91 .05 .83 1.93 17.35 .67 .85 12. Going to university

.52 .92 .06 .85 4.06** 14.54 .62 .85 23. I have trouble

.50 .93 .05 .86 .69 19.08 .69 .84 27. I do not find study

.49 .97 .06 .94 2.98* 17.31 .67 .85 30. I have trouble

.44 .87 .05 .76 1.24 17.11 .72 .84

MODEL NNFI SRMR RMSEA CFI AGFI GFI S-B χ2 df χ2

∆ S-B χ2

M1 .90 .13 .12 (.115-

.90 .58 .63 2486.90 406 6.12 ***1221.65

M2 .96 .25 .077(.072-

.96 .74 .77 1265.25 405 3.12 229.35***

M3 .97 .062 .066(.061-

.97 .78 .80 1035.90 402 2.58 Notes- All parameter estimates were significant at p<.05. P.E.O=Parameter Estimation for

items of original data by Khawaja and Bryden (2006), P.E =Parameter Estimation for items

in the present study for three-factor oblique model, R2= Coefficient of determination of


30

parameter estimation for items, S.E = Standard Error, Z = Z score for tests of univariate

normality for linear transformed skewness of items, and T.V = T value for parameter

estimation, r = Corrected item-to-total correlation, α= Cronbach's Alpha if item is deleted,

M1=one-factor model, M2=three-factor orthogonal model, and M3= three-factor oblique

model.

Table 2

Means, standard deviations, internal consistency coefficients, and mean inter-item

correlations of USDI

L CE AM USDI

(n=359)sNormative sample Sample T M F T M F T M F T M F

Mean 23.32 4.392 21.84 31.71 33.13 29.80 16.93 17.82 15.72 71.80 75.34 67.37

S.D. 7.41 7.63 6.85 11.22 10.63 11.74 6.65 6.80 6.25 21.47 21.26 20.94

α .88 .88 .87 .92 .90 .94 .87 .85 .89 .94 .94 .95

Mr. .44 .44 .42 .45 .38 .53 .49 .46 .53 .36 .33 .37

(n=150)sClinical sample Sample T M F T M F T M F T M F

Mean 26.92 26.73 27.08 39.59 40.43 38.90 20.24 20.69 19.56 87.33 88.49 86.35

S.D. 7.98 7.83 168. 13.80 12.01 15.16 8.72 8.91 8.58 23.82 20.94 26.10

α .91 .92 .91 .96 .94 .96 .95 .94 .95 .95 .93 .96

Mr. .54 .55 .53 .61 .53 .67 .71 .71 .73 .38 .31 .44

Notes- T= Total sample, M=Male, F=Female, α = Cronbach`s alpha, Mr. = mean inter-item

correlation


31


32

Table 3

Spearman correlations of USDI and subscale scores with BDI, SIS, OHI, ASAA, and SSI

based on Normative sample and test-retest correlation of USDI and sub-scales

Measures SIS OHI BDI ASAA SSI r s.

L **.66 **.57- **.72 -.24** .63** .86**

CE **.66 **.59- **.79 -.17** .68** .72**

AM **.47 **.60- **.62 -.15* .51** .69**

USDI **.65 **.65- **.75 -.20** .69** .80**

Notes- All correlations are significant at P< .001 (two-tailed), SIS=suicide ideation scale,

OHS = Oxford happiness scale, BDI=Beck's depression inventory, ASAA = Average score of

academic achievement, SSI = Student-life stress inventory, L = Lethargy, CE =

Cognitive\emotion, AM = Academic motivation, USDI = Persian version of the university

student depression inventory, and r s. = Spearman correlation coefficient for test-retest

reliability.


33

Table 4

Raw scores of the USDI and subscale scores converted to percentiles

ercentileP L CE AM USDI percentile

5 13 16 8 39 5

10 14 18 10 46 10

15 16 20 11 49 15

20 17 22 12 52 20

25 18 23 12 54 25

30 19 24 13 57 30

35 20 26 14 61 35

40 21 27 14 63 40

45 22 28 15 67 45

50 22 30 16 69 50

55 23 32 17 73 55

60 24 34 17 77 60

65 26 36 18 82 65

70 27 38 19 85 70

75 28 40 20 89 75

76 28 40 20.6 89 76

77 28 40 21 90 77

78 28.8 41 21 90 78

79 29 42 21 91 79


34

80 29 42 22 92 80

81 29 42 22 92.6 81

82 30 42 22 94 82

83 30 42.8 23 94.8 83

84 30.4 43 23 96 84

85 31 43 23 97 85

86 31 43.6 23.6 98 86

87 32 44 24 98 87

88 32.8 45.8 24 98.8 88

89 33 46.4 25 100 89

90 34 47 26 100 90

91 34 47.6 28 102 91

92 35 48 28 103.2 92

93 35 49.8 28.8 105 93

94 36.4 50.4 29 106 94

95 37 52 33 109 95

96 38.6 54.6 34.6 110.6 96

97 39.4 56 35 113 97

98 43.6 59.8 35 116.8 98

99 44 62 35 123.4 99

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