Dalena Dillman Taylor, Saundra M. Tabet, Megan A. Whitbeck, Ryan G. Carlson, Sejal Barden, Nicole Silverio
Individuals living in poverty have higher rates of mental health disorders compared to those not living in poverty. Measures are available to assess adults’ levels of psychological distress; however, there is limited support for instruments to be used with a diverse population. The purpose of our study was to examine the factor structure of Outcome Questionnaire-45.2 scores with an economically vulnerable sample of adults (N = 615), contributing to the evidence of validity of the measure’s scores in diverse mental health settings. Implications for professional counselors are considered, including clinical usage of the brief Outcome Questionnaire-16 and key critical items.
Keywords: poverty, psychological distress, factor structure, Outcome Questionnaire-45.2, validity
In the United States, it is estimated that 34 million adults live in poverty (i.e., income less than $12,880 per year), and poverty is a significant factor contributing to poor mental and physical health outcomes (Hodgkinson et al., 2017). Poverty, or economic vulnerability, refers to the extent to which individuals have difficulty living with their current level of income, increasing the risk for adverse social and economic consequences (Semega et al., 2021). Economically vulnerable adults often experience greater social inequality, lower educational attainment, less economic mobility (Stanford Center on Poverty and Inequality, 2015), and difficulty securing full-time employment (Dakin & Wampler, 2008), which leads to increased distress (Lam et al., 2019). Lower income levels are also associated with several mental health conditions (e.g., anxiety, depression, suicide attempts; Santiago et al., 2011). Further, Lam and colleagues (2019) found strong negative associations between income, socioeconomic status, and psychological distress.
To effectively support their clients, counselors must understand the unique context and financial stressors related to living in poverty. Incorporating poverty-sensitive measures into assessment and evaluation practices is essential to providing culturally responsive care that considers the systemic and environmental barriers of poverty (Clark et al., 2020). Implementing culturally responsive assessments ensures that counselors use outcome measures that are attuned to poverty-related experiences (Clark et al., 2020). Such measures can help counselors identify and prioritize treatment planning approaches and acknowledge the reality that economic disadvantages create for clients (Foss-Kelly et al., 2017). However, the availability of poverty-sensitive assessments is limited.
Measuring Psychological Distress in Adults Living in Poverty
Because of the risk of mental health issues related to economic vulnerability, assessments with evidence of validity and reliability that measure psychological distress relative to income are warranted. Professional counselors can individualize their therapeutic approach to meet the needs of this population with the assistance of accurate assessments of related mental health conditions. Naher and colleagues (2020) noted the need for individual-level data as well as interventions specifically targeted to adults living in poverty. Although outcome assessments exist to measure psychological distress or severity of mental illness symptoms (e.g., Beck Depression Inventory [BDI], Beck et al., 1961; Generalized Anxiety Disorder Screener [GAD-7], Löwe et al., 2008; Patient Health Questionnaire-9 [PHQ-9], Kroenke et al., 2001), there is a lack of measures with evidence of validity and reliability with economically vulnerable adult populations. Therefore, our investigation examined the factor structure of the Outcome Questionnaire-45.2 (OQ-45.2; Lambert et al., 2004) with an economically vulnerable adult population, increasing the applicability of the measure in mental health settings.
Outcome Questionnaire-45.2
The OQ-45.2 (Lambert et al., 2004) is one of the most widely used outcome measures of psychological distress in applied mental health settings (Hatfield & Ogles, 2004). The OQ-45.2 assists professional counselors with monitoring client progress and can be administered multiple times throughout treatment, as it is sensitive to changes over time (Lambert et al., 1996). The OQ-45.2 has been implemented in outcome-based research with diverse populations such as university counseling center clients (Tabet et al., 2019), low-income couples (Carlson et al., 2017), and ethnic minority groups (Lambert et al., 2006). Lambert et al. (1996) reported strong test-retest reliability (r = .84) and internal consistency (α = .93) for the OQ-45.2, based on a sample of undergraduate students (n = 157) and a sample of individuals receiving Employee Assistance Program services (n = 289). However, researchers have yet to investigate the psychometric properties of the OQ-45.2 with an economically disadvantaged, diverse population.
Given the utility of the OQ-45.2 as a client-reported feedback measure, clinicians can use the OQ-45.2 in a variety of ways to evaluate client progress, including measuring changes in individual distress across the course of counseling and before and after specific treatment interventions, as well as to glean a baseline level of distress at the start of counseling (Lambert, 2017). For example, one study used the OQ-45.2 as a primary outcome measure for anxiety symptoms in clients engaging in cognitive behavioral therapy (Levy et al., 2020). The OQ-45.2 was administered at the beginning of each weekly counseling session and change scores were calculated between each session, which helped clinicians understand that about half of their sample reported clinically significant reductions in symptoms in just nine sessions (Levy et al., 2020). This example demonstrates how the OQ-45.2 can be implemented to monitor treatment outcomes and improve the duration and efficiency of counseling. A clinician can also use salient items as part of the intake clinical interview to encourage clients to elaborate on the specific symptoms they are experiencing, and how they may be impacting their functioning, across a variety of clinical settings (Espiridion et al., 2021; Lambert, 2017; Levy et al., 2020).
Factor Structure of OQ-45.2
Researchers contested the factor structure proposed by Lambert et al. (2004), suggesting the need for further validation of the three-factor oblique measurement model and exploration of other possible factor structures (e.g., Kim et al., 2010; Mueller et al., 1998; Rice et al., 2014; Tabet et al., 2019). Mueller and colleagues (1998) examined three models: (a) a one-factor model, (b) a two-factor oblique model, and (c) a three-factor oblique model, none of which fit the data well. In addition, the factors in the three-factor model were highly correlated, ranging from .83 to .91, asserting that the subscales may not be statistically indistinguishable and the OQ-45.2 might be a unidimensional measure of global distress.
Kim and colleagues (2010) also explored three models to assess adequate fit of the data: (a) a one-factor model, (b) a three-factor model, and (c) a revised 22-item four-factor model. Indicating weak support for the OQ-45.2’s factorial validity across all models, researchers cautioned against widespread utilization in mental health and research settings, encouraging further psychometric exploration and validation of the OQ-45.2 (Kim et al., 2010).
Rice and colleagues (2014) found evidence to support a two-factor OQ-45.2 model that included (a) overall maladjustment and (b) substance use. Results indicated relatively good fit (comparative fit index [CFI] = .990, root-mean-square error of approximation [RMSEA] = .068) for a two-factor measure with 11 items, which demonstrated better model fit than the original three-factor model
(CFI = .840, RMSEA = .086 [90% confidence interval {CI} = .085, .087]). Overall, multiple researchers have demonstrated poor fit for the original factor structure of the OQ-45.2 (Kim et al., 2010; Mueller et al., 1998; Rice et al., 2014; Tabet et al., 2019), supporting the need for further validation for using the OQ-45.2 with samples of adults living in poverty.
This study’s primary aim is to examine the factor structure of the OQ-45.2 with an economically vulnerable sample to enhance the generalizability of the OQ-45.2 in mental health settings. Therefore, the following research questions guided our study:
RQ1. What is the factor structure of OQ-45.2 scores with a sample of adults living in poverty?
RQ2. What is the internal consistency reliability of the abbreviated 16-item OQ-45.2 scores with a sample of adults living in poverty?
RQ3. What is the test-retest reliability of the abbreviated 16-item OQ-45.2 scores with a sample of adults living in poverty?
Method
Participants and Procedures
Participants comprised a sub-sample from a grant-funded, community-based, relationship education program for individuals and couples at a university in the Southeastern United States. The project was funded through the U.S. Department of Health and Human Services, Administration for Children and Families, Office of Family Assistance (Grant #90FM0078). Study recruitment strategically involved passive and active recruitment strategies (Carlson et al., 2014) from various community locations that primarily serve low-income individuals and families (e.g., libraries, employment offices). Participants met inclusion criteria if they were at least 18 years old and interested in learning about healthy relationships. The relationship education intervention utilized was an evidence-based curriculum that taught individuals tools to improve their relationships in a small group setting (Prevention and Relationship Education Program [PREP]; Pearson et al., 2015).
We obtained ethical approval from the university’s IRB prior to data collection. Each person participated in a group intake session that consisted of a review of the informed consent; a battery of assessments, including the OQ-45.2; and a brief activity. Study participants (N = 615) included in this current analysis consented between July 2015 and June 2019.
Demographic Information
We collected demographic data as part of this study, which included gender, age, ethnicity, income, educational level, working status, and marital status (see Table 1). The majority of participants fell below the poverty line when factoring in number of children and/or under- or unemployment. Therefore, our sample consisted of a diverse population, including variations in income, age, ethnicity, and race.
Table 1
Participant Demographic Characteristics
Descriptive Characteristic Total Sample (n, %)
| Age
18–20 years 21–24 years 25–34 years 35–44 years 45–54 years 55–64 years 65 years or older |
34 (5.5) 52 (8.5) 130 (21.1) 139 (22.6) 137 (22.3) 91 (14.8) 32 (5.2) |
| Gender (female) | 498 (81.0) |
| Race | |
| American Indian or Alaska Native | 18 (2.9) |
| Asian | 19 (3.1) |
| Black or African American | 176 (28.6) |
| Native American or Pacific Islander | 2 (0.3) |
| White | 248 (40.3) |
| Other | 144 (23.4) |
| Ethnicity
Hispanic or Latino Not Hispanic or Latino Income |
258 (42.0) 356 (57.9) |
| Less than $500 | 216 (35.1) |
| $501–$1,000 | 108 (17.6) |
| $1,001–$2,000
$2,001–3,000 $3,001–$4,000 $4,001–$5,000 More than $5,000 |
124 (20.2)
81 (13.2) 28 (4.6) 18 (2.9) 18 (2.9) |
| Educational Level
No degree or diploma earned High school diploma Some college but no degree completion Associate degree Bachelor’s degree Master’s / advanced degree |
24 (3.9) 18 (2.9) 75 (12.2) 66 (10.7) 134 (21.8) 77 (12.5) |
| Marital Status
Married Engaged Divorced Widowed Never Married |
93 (15.1) 11 (1.8) 164 (26.7) 24 (3.9) 270 (43.9) |
| Employment Status
Full-time employment Part-time employment Temporary, occasional, or seasonal, or odd jobs for pay Not currently employed Employed, but number of hours change from week to week Selected multiple responses Number of Children 0 1 2 3 4 5 6 |
227 (36.9) 83 (13.5) 41 (6.7) 207 (33.7) 29 (13.5) 6 (1.0)
148 (24.1) 60 (9.8) 44 (7.2) 17 (2.8) 6 (1.0) 4 (0.7) 1 (0.4) |
Instrument
The Outcome Questionnaire-45.2
The OQ-45.2 is a self-report questionnaire that captures individuals’ subjective functionality in various aspects of life that can lead to common mental health concerns (e.g., anxiety, depression, substance use). The current three-factor structure of the OQ-45.2 has 45 items rated on a 5-point Likert scale, with rankings of 0 (never), 1 (rarely), 2 (sometimes), 3 (frequently), and 4 (almost always; Lambert et al., 2004). Nine OQ-45.2 items are reverse scored, with total OQ-45.2 scores calculated by summing all 45 items with a range from 0 to 180. Clinically significant changes are represented in a change score of at least 14, whether positive or negative (i.e., increased or reduced distress).
The Symptom Distress subscale (25 items) evaluates anxiety, depression, and substance abuse symptoms, as these are the most diagnosed mental health concerns (Lambert et al., 1996). The Interpersonal Relations subscale (11 items) includes items that measure difficulties and satisfaction in relationships. The Social Role Performance subscale (nine items) assesses conflict, distress, and inadequacy related to employment, family roles, and leisure activities. The OQ-45.2 also includes four critical items (Items 8, 11, 32, and 44) targeting suicidal ideation, homicidal ideation, and substance use. The Cronbach’s alpha for the OQ-45.2 in the current study was calculated at .943.
Data Analysis
We calculated descriptive statistics on the total sample population, including the mean, standard deviations, and frequencies. Subsequently, we conducted preliminary descriptive analyses to test for statistical assumptions that included missing data, collinearity issues, and multivariate normality (Byrne, 2016). In the first analysis, we used confirmatory factor analysis (CFA) to test the factor structure of the OQ-45.2 with this population (N = 615) and subsequently used exploratory factor analysis (EFA) to evaluate revised OQ models.
We conducted CFA utilizing the original three-factor oblique model (Lambert et al., 2004) as the a priori model to test the hypothesized structure of the latent variables. In addition, based on the results of the study, we tested a series of alternative structural models outlined by Bludworth and colleagues (2010). Given the non-normal distribution, we utilized MPlus (Version 8.4) with a robust maximum likelihood (MLR) parameter estimation (Satorra & Bentler, 1994). To address missing data, we employed a full information maximum likelihood (FIML) to approximate the population parameters and produce the estimates from the sample data (Enders, 2010). Results of the CFA were evaluated using several fit indices: (a) the chi-square test of model fit (χ2; nonsignificance at p > .05 indicate a good fit [Hu & Bentler, 1999]); (b) the CFI (values larger than .95 indicate a good fit [Bentler, 1990]); (c) TLI (values larger than .95 indicate a good fit [Tucker & Lewis, 1973]); (d) RMSEA with 90% CI (values between .05 and .08 indicate a good fit [Browne & Cudeck, 1993]); and (e) standardized root-mean-square residual (SRMR; values below .08 indicate good fit [Hu & Bentler, 1999]).
Following the CFA, we conducted EFA because of poor model fit across all models and several items with outer loadings of less than 0.5 (Tabachnick & Fidell, 2019). Kline (2016) recommended researchers should not be constrained by the original factor structure when CFA indicates low outer loadings and should consider conducting an EFA because the data may not fit the original number of factors suggested. Accordingly, we conducted an EFA to test the number of factors derived from the 45-item OQ-45.2 within our population. We exceeded the recommended ratio (i.e., 10:1) of participants to the number of items (12.6:1; Costello & Osborne, 2005; Hair et al., 2010; Mvududu & Sink, 2013). We conducted a principal axis factoring with Promax rotation to determine whether factors were correlated using SPSS version 25.0. We chose parallel analysis (Horn, 1965) using the 95th percentile to determine the number of factors to retain given that previous researchers have acknowledged parallel analysis to be a superior method to extract significant factors as compared to conventional statistical indices such as Cattell’s scree test (Henson & Roberts, 2006). We used stringent criterion when identifying loading and cross-loading items such as items that indicated high (i.e., equal to or exceeding 1.00) or low communality values (i.e., less than 0.40; Costello & Osborne, 2005) and items with substantive cross-loadings (< .30 between two factor loadings; Tabachnick & Fidell, 2019) were removed. To ensure the most parsimonious model, we removed items individually from Factor 1, which has the greatest number of items, to reduce the size of the model while still capturing the greatest variance explained by the items on that factor.
Results
We screened the data and checked for statistical assumptions prior to conducting factor analysis. Little’s Missing Completely at Random (MCAR) test (Little, 1988), a multivariate extension of a simple t-test, evaluated the mean differences of the 45 items to determine the pattern and missingness of data (Enders, 2010). Given the significant chi-square, data were not missing completely at random
(χ2 = 912.062, df = 769, p < .001). However, results indicated a very small percentage of values (< 1%) were missing from each variable; therefore, supporting data were missing at random (MAR; Osborne, 2013). When data are MAR, an FIML approach to replace missing values provides unbiased parameter estimates and improves the statistical power of analyses (Enders, 2010). The initial internal consistency reliability estimates (coefficient alpha) for scores on the original OQ-45.2 model were all in acceptable ranges except for Factor 3 (see Henson & Roberts, 2006): total α = .943, Symptom Distress α = .932
(k = 25 items), Interpersonal Relations α = .802 (k = 11 items), and Social Role Performance α = .683
(k = 9 items). We also conducted Bartlett’s test of sphericity (p < .001) and the Kaiser-Meyer-Olkin value (.950), indicating the data was suitable for conducting a factor analysis. We evaluated multivariate normality of the dataset with Mardia’s multivariate kurtosis coefficient. Mardia’s coefficient of multivariate kurtosis was .458; therefore, we deemed the data to be non-normally distributed
(Hu & Bentler, 1995).
Confirmatory Factor Analysis
We tested the developer’s original OQ-45.2 three-factor oblique model, and because of the results subsequently tested a series of alternative structural models outlined by Bludworth and colleagues (2010). Specifically, the alternative structural models tested included: (a) a three-factor orthogonal model, (b) a one-factor model, (c) a four-factor hierarchical model, and (d) a four-factor bilevel model. Table 2 presents the fit indices results in the series of CFAs. The original three-factor oblique model allowed all three factors (Social Role Performance, Interpersonal Relations, and Symptom Distress) to correlate, but resulted in a poor fit: χ2 (942, N = 615) = 3.014, p < .001; CFI = .779; TLI = .768; RMSEA = .057, 90% CI [.055, .060]; SRMR = .063. We next uncorrelated the factors and tested a three-factor orthogonal model, which also presented a poor fit with worsened fit metrics: χ2 (945, N = 615) = 3.825, p < .001; CFI = .689; TLI = .674; RMSEA = .068, 90% CI [.065, .070]; SRMR = .202. Accordingly, because the factors demonstrated high intercorrelation (rs = .94, .93, .91) in the three-factor oblique model and lack of factorial validity based on the CFA results of both three-factor models, we suspected the OQ-45.2 to be a unidimensional, one-factor model. However, the CFA revealed a poor fit to the OQ-45.2 one-factor model: χ2 (945, N = 615) = 3.197,
p < .001; CFI = .758; TLI = .747; RMSEA = .060, 90% CI [.057, .062]; SRMR = .062.
Table 2
Goodness-of-Fit Indices for the Item-Level Models of the OQ-45.2
| χ2 | df | p | χ2/df | CFI | TLI | RMSEA | 90% CI | SRMR | |
| One-Factor | 3021.300 | 945 | .000 | 3.197 | .758 | .747 | .060 | [.057, .062] | .062 |
| Three-Factor (orthogonal) | 3615.060 | 945 | .000 | 3.825 | .689 | .674 | .068 | [.065, .070] | .202 |
| Three-Factor (oblique) | 2839.335 | 942 | .000 | 3.014 | .779 | .768 | .057 | [.055, .060] | .063 |
| Four-Factor (hierarchical) | 2839.335 | 942 | .000 | 3.014 | .779 | .768 | .057 | [.055, .060] | .063 |
| Four-Factor
(bilevel) |
2363.263 | 900 | .000 | 2.626 | .829 | .812 | .051 | [.049, .054] | .054 |
Note. N = 615. χ2 = chi-square; df = degrees of freedom; χ2/df = relative chi-square; CFI = comparative fit index;
TLI = Tucker-Lewis Index; RMSEA = root-mean-square error of approximation; 90% CI = 90% confidence interval;
SRMR = standardized root-mean-square residual.
We proceeded to test the OQ-45.2 as a four-factor hierarchical model. In this multidimensional model, the three first-order factors (Social Role Performance, Interpersonal Relations, and Symptom Distress) became a linear combination to sum a second-order general factor (g-factor) of Psychological Distress (Eid et al., 2017). Results evidenced an unacceptable overall fit to the data: χ2 (942, N = 615) = 3.014, p < .001; CFI = .779; TLI = .768; RMSEA = .057, 90% CI [.055, .060]; SRMR = .063. Last, we examined a four-factor bilevel model. In this model, the g-factor of Psychological Distress has a direct effect on items, whereas, in the hierarchal model, it had an indirect effect on items. Therefore, the items in the four-factor bilevel model load onto both their intended factors (Social Role Performance, Interpersonal Relations, and Symptom Distress) and the g-factor (Psychological Distress). Nevertheless, although the four-factor bilevel was cumulatively the best fitting OQ-45.2 factorial model, the results still yielded a poor fit:
χ2 (900, N = 615) = 2.626, p < .001; CFI = .829; TLI = .812; RMSEA = .051, 90% CI [.049, .054]; SRMR = .054.
Overall, all models demonstrated a significant chi-square (p < .001); however, this result is common in larger sample sizes (N > 400; Kline, 2016). Because the chi-square statistic is sensitive to sample size and model complexity, researchers have recommended using other fit indices (e.g., RMSEA, CFI) to determine overall model fit (Tabachnick & Fidell, 2019). Nevertheless, the levels of the CFI values (ranging from .689 to .829) and TLI values (ranging from .674 to .812) were low, and far below the recommended referential cutoff (> .90; Tucker & Lewis, 1973). Although the models’ RMSEA values were within the recommended range of .05 to .08 (Browne & Cudeck, 1993), and the majority of SRMR values were below .08 (Hu & Bentler, 1999), these were the only fit indices that met acceptable cutoffs. We further examined outer loadings for the 45 items within the factorial models and identified that all models had outer loadings (ranging from 5 to 14 items) below the 0.5 cutoff (Tabachnick & Fidell, 2019). When CFA produces low factor loadings and poor fit indices, researchers should not be constrained to the original specified number of factors and should consider conducting an EFA (Kline, 2016). Hence, we elected to conduct an EFA to explore the factor structure with this population.
Exploratory Factor Analysis
Results from the initial EFA using principal axis factoring with the 45 OQ items produced a solution that explained 55.564% of the total variance. After multiple iterations of item deletions, we concluded with a three-factor solution. We present the internal reliability estimates of two three-factor solutions: (a) a 16-item three-factor solution—the most parsimonious—and (b) an 18-item three-factor solution, including all critical items in Table 3. We present the first three-factor solution because it was derived using stringent criteria for creating the most parsimonious solution (Costello & Osborne, 2005; Henson & Roberts, 2006; Tabachnick & Fidell, 2019), whereas the second three-factor solution included conceptual judgment determining the inclusion of the critical items from the original OQ-45.2.
Table 3
Internal Consistency Estimates
| Total | Symptom Distress | Interpersonal Relations | Social Role Performance | |
| Original OQ-45 | .943 | .932 | .802 | .683 |
| Total | Factor 1 | Factor 2 | Factor 3 | |
| 16-Item Model | .894 | .864 | .840 | .710 |
| 18-Item Model | .896 | .857 | .840 | .700 |
Three-Factor Solution
Results from the parallel analysis (Horn, 1965) indicated an initial four-factor solution. Through multiple iterations (n = 9) of examining factor loadings, removing items one at a time, and reexamining parallel analysis after each deletion, our results demonstrated that a three-factor solution was the most parsimonious. We removed a total of 29 items because of low communalities (< .5), low factor loadings (< .4), and substantive cross-loadings (> .3 between two factor loadings; Tabachnick & Fidell, 2019). Before accepting the removal of these items, we added each back to the model to determine its impact on the overall model. No items improved the model; therefore, we accepted the deletion of the 29 items. The final three-factor solution included 16 items with 57.99% of total variance explained, which indicates near acceptable variance in social science research, with 60% being acceptable (Hair et al., 2010). Factor 1 (seven items) explained 38.98% of the total variance; Factor 2 (six items) explained 11.37% of the total variance; and Factor 3 (three items) explained 7.64% of the total variance.
Three-Factor Solution With Critical Items
After finalizing the model, we added Item 8 (“I have thoughts of ending my life”) and Item 44 (“I feel angry enough at work/school to do something I might regret”) into the final model for purposes of clinical utility. Both items resulted in low factor loading (< .4). Item 8 correlated with other items on Factor 3, and Item 44 correlated with other items on Factor 1. This final 18-item three-factor solution reduced the variance explained by the items on the factors by 3.45%, indicating a questionable fit for social sciences (54.54%; Hair et al., 2010). Factor 1 (eight items) explained 36.83% of the total variance; Factor 2 (six items) explained 10.82% of the total variance; and Factor 3 (four items) explained 6.90% of the total variance. Internal consistency estimates are presented in Table 3 for all three models: (a) the original OQ-45.2 (α = .943); (b) the 16-item, three-factor solution (α = .894); and (c) the 18-item, three-factor solution (α = .896).
Test-Retest Reliability
To examine the stability of the new 16-item OQ scores over time, we assessed test-retest reliability over a 30-day interval using bivariate correlation (Pallant, 2016). Results yielded strong correlation coefficients between pre-OQ scores and post-OQ scores: (a) OQ Total Scores, r = .781, p < .001; (b) Factor 1, r = .782, p < .001; (c) Factor 2, r = .742, p < .001; and (d) Factor 3, r = .681, p < .001. The 18-item OQ scores also demonstrated significant support for test-retest reliability over a 30-day interval: (a) OQ Total Scores, r = .721, p < .001; (b) Factor 1, r = .658, p < .001; (c) Factor 2, r = .712, p < .001; and (d) Factor 3, r = .682, p < .001.
Discussion
We found that the current factor structure of the OQ-45.2 poorly fits the sample population of economically vulnerable individuals. Our preliminary results support Rice and colleagues’ (2014) claim: because of the unique stressors economically vulnerable individuals face, the OQ-45.2 does not adequately capture their psychological distress. The lack of support for the OQ-45.2’s current structure (i.e., three-factor oblique) creates doubt clinically when assessing clients’ distress. Therefore, we explored alternative structural models proposed by Bludworth and colleagues (2010) using a CFA, and subsequently an EFA, to reexamine the factor structure of the OQ-45.2.
The EFA resulted in a 16-item, three-factor solution with our sample, indicating marginal support for the validity and reliability of the items for this brief model of the OQ, meaning that this model lacked reliability (i.e., ability to produce similar results consistently) and validity (i.e., ability to actually measure what it intends to measure: distress). In social science research, total variance explained of 60% is adequate (Hair et al., 2010); therefore, the three-factor model that approaches 60% could be acceptable, indicating that this model captures more than half or more than chance of the construct distress for this population. Still, additional research is needed to support the factor structure with a similar population of low-income, diverse individuals. Economically vulnerable individuals experience unique stressors (Karney & Bradbury, 2005), and brief assessments are best practices (Beidas et al., 2015). Therefore, we encourage other researchers to reexamine the use of this brief version of the OQ with a sample of economically vulnerable individuals or develop a new instrument that may more accurately capture psychological distress in economically disadvantaged individuals.
Also, the 16-item model results differ from the original OQ-45.2 in that we were unable to find support for the social role factor with our sample population. We hypothesize this finding is largely due to the economic stressors this population faces (e.g., unreliable transportation, food scarcity, housing needs). Anecdotally, some participants commented during the initial intake session that several items (e.g., specifically items on the social role factor relating to employment) were not relevant to their situation because of under- or unemployment. Further, reducing OQ-45.2 to a 16-item assessment may provide a more user-friendly version requiring less time for respondents and more efficient use of clinical time; however, without further research, the current authors are hesitant to support its clinical use with this population of economically vulnerable individuals.
Similar to previous researchers (e.g., Kim et al., 2010; Rice et al., 2014), we also found evidence for the need for a substance use factor (e.g., Factor 3) in the 18-item abbreviated model; however, this model deviated from the original OQ-45.2. The findings of this study support the need for professional counselors to assess substance use as part of psychological distress, whether it be implementing the
18-item version of the OQ or adding an additional assessment that has greater reliability and validity of its items with this population.
Implications
We found initial, possible support for a brief version of the OQ-45.2 for economically vulnerable individuals. The abbreviated 16-item OQ assessment derived from this research requires less time to complete while capturing an individual’s distress on substance use, interpersonal relationships, and symptom distress. A brief instrument can provide professional counselors with a snapshot of the client’s concerns, which can assist in monitoring a client’s level of psychological distress throughout treatment. In clinical settings, counselors can utilize this instrument to briefly assess at intake the baseline distress of their clients and use it as a guide or conversation starter for discussing client distress. For example, a counselor may ask that the client complete the brief OQ-16 instrument with the intake paperwork. In review of all paperwork, the counselor may note to the client, “I noticed that you indicated high distress with interpersonal relationships. Is that a place you would like to begin, or do you have another place you want to begin?”
Further, we retained two critical items (i.e., Items 8 and 44) in the 18-item version of the OQ brief assessment, as psychological distress associated with economic vulnerability is linked to higher rates of suicide and homicide (Knifton & Inglis, 2020). Because of the clinical utility of this instrument, professional counselors may want to include those items to assess a client’s level of threat of harm to self or others. Dependent on the client’s answer to these critical items, professional counselors have a quick reference with which to intervene or focus the initial session to address safety. Therefore, the items of this assessment may possibly be used to start the initial dialogue regarding an individual’s psychological distress and/or suicidal intent; however, the assessment should not be used as the only tool or instrument to diagnose or treat psychological distress. We understand that these items can help professional counselors efficiently assess for suicidal or homicidal intent. Therefore, the counselor can opt to use the 16-item version and include an additional, more reliable assessment for measuring threat of harm to self and/or others. For example, counselors may opt to use an instrument such as the Ask Suicide-Screening Questions tool (Horowitz et al., 2012) to further evaluate suicidal intent.
In our experience, when following up with study participants based on a score higher than 1 on a scale of 1–5, many participants indicated they felt that way in the past but no longer feel that way now. In our use of the OQ-45.2, we find that participants tend to answer these questions based on their entire life versus the time frame indicated in the assessment instructions (the past week [7 days]). Therefore, professional counselors should be clear that respondents should answer based on the past week, rather than “ever experienced.” When offering the assessment to clients, we recommend that the counselor highlight the time frame in the instructions or clearly communicate that time frame to the client before they complete the instrument to gain the most accurate data.
Limitations and Suggestions for Future Research
As with all research, results should be considered in light of limitations. The large study sample consisted of diverse individuals; however, the majority were women, and all individuals were from the southeast region of the United States, minimizing the generalizability of these findings. In addition, although findings indicate initial, possible support for a revised three-factor model consisting of 16 items, future studies are warranted to strengthen the validity of this abbreviated version of the OQ-45.2. We suggest that future researchers test the 16-item assessment through CFA with a similar population to confirm the current study’s findings. All respondents volunteered to participate in a 6-month study, which may indicate more motivation to improve or represent a population with distress responses different from those who were recruited but chose not to participate in the study. Additionally, study participants were actively recruited, and may have experienced less distress than a help-seeking sample.
The OQ is available in a Spanish translation; however, we only included people who completed the English OQ-45.2 version in the current study. Future analyses should examine the factor structure of the Spanish OQ-45.2 as well. Next, future research on the OQ should include the development and testing of new items. Lastly, future research should aim to validate the reduced 16-item and 18-item OQ scores on a new sample and seek to establish a new criterion for clinical significance. Professional counselors may also benefit from the creation of a specific instrument assessing distress related to the unique stressors that economically vulnerable clients face. Until further analyses are conducted with a new sample population to confirm the abbreviated models, we encourage professional counselors to implement the brief version tentatively and with caution, and to follow up with the client regarding high scores on critical items prior to making clinical judgments regarding reported subscale scores.
Conclusion
Given the broad utility of the OQ-45.2 in research and mental health settings, researchers and professional counselors must understand the instrument’s structure for interpretation purposes and how the assessment should be adapted for various populations. Professional counselors can effectively support clients by assessing and recognizing how economic-related distress impacts their quality of life, which may directly relate to treatment outcomes. Findings from the current study add to previous literature that calls into question the original OQ-45.2 factor structure. Additionally, the current study’s findings support a revised 16-item, three-factor structure for economically vulnerable clients and we provide implications for use of this assessment in clinical practice. Future research should include a confirmatory analysis of the current findings.
Conflict of Interest and Funding Disclosure
This research was supported by a grant (90FM0078)
from the U.S. Department of Health and Human
Services (USDHHS), Administration for Children and
Families, Office of Family Assistance. Any opinions,
findings, conclusions, or recommendations are those
of the authors and do not necessarily reflect the views
of the USDHHS, Office of Family Assistance. The authors
reported no further funding or conflict of interest.
References
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4(6), 561–571. https://doi.org/10.1001/archpsyc.1961.01710120031004
Beidas, R. S., Stewart, R. E., Walsh, L., Lucas, S., Downey, M. M., Jackson, K., Fernandez, T., & Mandell, D. S. (2015). Free, brief, and validated: Standardized instruments for low-resource mental health settings. Cognitive and Behavioral Practice, 22(1), 5–19. https://doi.org/10.1016/j.cbpra.2014.02.002
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.
https://doi.org/10.1037/0033-2909.107.2.238
Bludworth, J. L., Tracey, T. J. G., & Glidden-Tracey, C. (2010). The bilevel structure of the Outcome Questionnaire–45. Psychological Assessment, 22(2), 350–355. https://doi.org/10.1037/a0019187
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models. SAGE.
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.
Carlson, R. G., Fripp, J., Munyon, M. D., Daire, A., Johnson, J. M., & DeLorenzi, L. (2014). Examining passive and active recruitment methods for low-income couples in relationship education. Marriage & Family Review, 50(1), 76–91. https://doi.org/10.1080/01494929.2013.851055
Carlson, R. G., Rappleyea, D. L., Daire, A. P., Harris, S. M., & Liu, X. (2017). The effectiveness of couple and individual relationship education: Distress as a moderator. Family Process, 56(1), 91–104.
https://doi.org/10.1111/famp.12172
Clark, M., Ausloos, C., Delaney, C., Waters, L., Salpietro, L., & Tippett, H. (2020). Best practices for counseling clients experiencing poverty: A grounded theory. Journal of Counseling & Development, 98(3), 283–294. https://doi.org/10.1002/jcad.12323
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7), 1–9.
https://doi.org/10.7275/jyj1-4868
Dakin, J., & Wampler, R. (2008). Money doesn’t buy happiness, but it helps: Marital satisfaction, psychological distress, and demographic differences between low- and middle-income clinic couples. The American Journal of Family Therapy, 36(4), 300–311. https://doi.org/10.1080/01926180701647512
Eid, M., Geiser, C., Koch, T., & Heene, M. (2017). Anomalous results in G-factor models: Explanations and alternatives. Psychological Methods, 22(3), 541–562. https://doi.org/10.1037/met0000083
Enders, C. K. (2010). Applied missing data analysis (1st ed.). Guilford.
Espiridion, E. D., Oladunjoye, A. O., Millsaps, U., & Yee, M. R. (2021). A retrospective review of the clinical significance of the Outcome Questionnaire (OQ) measure in patients at a psychiatric adult partial hospital program. Cureus, 13(3), e13830. https://doi.org/10.7759/cureus.13830
Foss-Kelly, L. L., Generali, M. M., & Kress, V. E. (2017). Counseling strategies for empowering people living in poverty: The I-CARE Model. Journal of Multicultural Counseling and Development, 45(3), 201–213.
https://doi.org/10.1002/jmcd.12074
Hair, J. F., Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis (6th ed.). Pearson.
Hatfield, D. R., & Ogles, B. M. (2004). The use of outcome measures by psychologists in clinical practice. Professional Psychology: Research and Practice, 35(5), 485–491. https://doi.org/10.1037/0735-7028.35.5.485
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. https://doi.org/10.1177/0013164405282485
Hodgkinson, S., Godoy, L., Beers, L. S., & Lewin, A. (2017). Improving mental health access for low-income children and families in the primary care setting. Pediatrics, 139(1). https://doi.org/10.1542/peds.2015-1175
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447
Horowitz, L. M., Bridge, J. A., Teach, S. J., Ballard, E., Klima, J., Rosenstein, D. L., Wharff, E. A., Ginnis, K., Cannon, E., Joshi, P., & Pao, M. (2012). Ask Suicide-Screening Questions (ASQ): A brief instrument for the pediatric emergency department. Archives of Pediatrics & Adolescent Medicine, 166(12), 1170–1176.
https://doi.org/10.1001/archpediatrics.2012.1276
Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). SAGE.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
https://doi.org/10.1080/10705519909540118
Karney, B. R., & Bradbury, T. N. (2005). Contextual influences on marriage: Implications for policy and intervention. Current Directions in Psychological Science, 14(4), 171–174.
https://doi.org/10.1111/j.0963-7214.2005.00358.x
Kim, S.-H., Beretvas, S. N., & Sherry, A. R. (2010). A validation of the factor structure of OQ-45 scores using factor mixture modeling. Measurement and Evaluation in Counseling and Development, 42(4), 275–295.
https://doi.org/10.1177/0748175609354616
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
Knifton, L., & Inglis, G. (2020). Poverty and mental health: Policy, practice and research implications. BJPsych Bulletin, 44(5), 193–196. https://doi.org/10.1192/bjb.2020.78
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
Lam, J. R., Tyler, J., Scurrah, K. J., Reavley, N. J., & Dite, G. S. (2019). The association between socioeconomic status and psychological distress: A within and between twin study. Twin Research and Human Genetics, 22(5), 312–320. https://doi.org/10.1017/thg.2019.91
Lambert, M. J. (2017). Measuring clinical progress with the OQ-45 in a private practice setting. In S. Walfish, J. E. Barnett, & J. Zimmerman (Eds.), Handbook of private practice: Keys to success for mental health practitioners (pp. 78–93). Oxford University Press. https://doi.org/10.1093/med:psych/9780190272166.003.0007
Lambert, M. J., Burlingame, G. M., Umphress, V., Hansen, N. B., Vermeersch, D. A., Clouse, G. C., & Yanchar, S. C. (1996). The reliability and validity of the Outcome Questionnaire. Clinical Psychology and Psychotherapy, 3(4), 249–258. https://doi.org/10.1002/(SICI)1099-0879(199612)3:4<249::AID-CPP106>3.0.CO;2-S
Lambert, M. J., Gregersen, A. T., & Burlingame, G. M. (2004). The Outcome Questionnaire-45. In M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcomes assessment: Instruments for adults (3rd ed.; pp. 191–234). Routledge.
Lambert, M. J., Smart, D. W., Campbell, M. P., Hawkins, E. J., Harmon, C., & Slade, K. L. (2006). Psychotherapy outcome, as measured by the OQ-45, in African American, Asian/Pacific Islander, Latino/a, and Native American clients compared with matched Caucasian clients. Journal of College Student Psychotherapy, 20(4), 17–29. https://doi.org/10.1300/J035v20n04_03
Levy, H. C., Worden, B. L., Davies, C. D., Stevens, K., Katz, B. W., Mammo, L., Diefenbach, G. J., & Tolin, D. F. (2020). The dose-response curve in cognitive-behavioral therapy for anxiety disorders. Cognitive Behaviour Therapy, 49(6), 439–454. https://doi.org/10.1080/16506073.2020.1771413
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202. https://doi.org/10.2307/2290157
Löwe, B., Decker, O., Müller, S., Brähler, E., Schellberg, D., Herzog, W., & Herzberg, P. Y. (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Medical Care, 46(3), 266–274. https://doi.org/10.1097/MLR.0b013e318160d093
Mueller, R. M., Lambert, M. J., & Burlingame, G. M. (1998). Construct validity of the Outcome Questionnaire: A confirmatory factor analysis. Journal of Personality Assessment, 70(2), 248–262.
https://doi.org/10.1207/s15327752jpa7002_5
Muthén, L. K., & Muthén, B. O. (2015). Mplus: Statistical analysis with latent variables. User’s guide. (7th ed.)
https://www.statmodel.com/download/usersguide/MplusUserGuideVer_7.pdf
Mvududu, N. H., & Sink, C. A. (2013). Factor analysis in counseling research and practice. Counseling Outcome Research and Evaluation, 4(2), 75–98. https://doi.org/10.1177/2150137813494766
Näher, A.-F., Rummel-Kluge, C., & Hegerl, U. (2020). Associations of suicide rates with socioeconomic status and social isolation: Findings from longitudinal register and census data. Frontiers in Psychiatry, 10(898), 1–9. https://doi.org/10.3389/fpsyt.2019.00898
Osborne, J. W. (2013). Best practices in data cleaning. SAGE.
Pearson, M., Stanley, S. M., & Rhoades, G. K. (2015). Within My Reach leader manual. PREP for Individuals, Inc.
Rice, K. G., Suh, H., & Ege, E. (2014). Further evaluation of the Outcome Questionnaire–45.2. Measurement and Evaluation in Counseling and Development, 47(2), 102–117. https://doi.org/10.1177/0748175614522268
Santiago, C. D., Wadsworth, M. E., & Stump, J. (2011). Socioeconomic status, neighborhood disadvantage, and poverty-related stress: Prospective effects on psychological syndromes among diverse low-income families. Journal of Economic Psychology, 32(2), 218–230. https://doi.org/10.1016/j.joep.2009.10.008
Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). SAGE.
Semega, J., Kollar, M., Shrider, E. A., & Creamer, J. F. (2021). Income and poverty in the United States: 2019. Current population reports. U.S. Census Bureau.
https://www.census.gov/content/dam/Census/library/publications/2020/demo/p60-270.pdf
Stanford Center on Poverty and Inequality. (2015). State of the states: The poverty and inequality report. Pathways. http://inequality.stanford.edu/sites/default/files/SOTU_2015.pdf
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Tabet, S. M., Lambie, G. W., Jahani, S., & Rasoolimanesh, S. (2019). The factor structure of the Outcome Questionnaire–45.2 scores using confirmatory tetrad analysis—partial least squares. Journal of Psychoeducational Assessment. https://doi.org/10.1177/0734282919842035
Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.
Dalena Dillman Taylor, PhD, LMHC, RPT-S, is an associate professor at the University of North Texas. Saundra M. Tabet, PhD, NCC, CCMHC, ACS, LMHC, is an assistant professor and CMHC Program Director at the University of San Diego. Megan A. Whitbeck, PhD, NCC, is an assistant professor at The University of Scranton. Ryan G. Carlson, PhD, is a professor at the University of South Carolina. Sejal Barden, PhD, is a professor at the University of Central Florida. Nicole Silverio is an assistant professor at the University of South Carolina. Correspondence may be addressed to Dalena Dillman Taylor, 1300 W. Highland St., Denton, TX 76201, Dalena.dillmantaylor@unt.edu.
