Confirmatory Analysis of an Organizational Culture Instrument for Corrections

Abstract

Research has determined that organizational culture is related to employee turnover, job commitment, and job satisfaction. Assessment of this culture requires an instrument that befits the type of organization under examination. Using exploratory factor analysis, Stohr and her colleagues were able to demonstrate that the Organizational Culture Instrument (OCI) had a solid reliability and validity profile. The current study reanalyzes these data, using confirmatory factor analysis and structural equation modeling. The findings indicate that there is statistical evidence to claim validation of the OCI and its seven theoretically based dimensions.

Keywords

organizational culture jails validity reliability

Introduction

Being imprisoned is a painful experience for inmates (R. Johnson, 2002), and working in such an environment also exerts tremendous pressure on correctional officers. Although researchers have examined in some detail the potential effect of correctional administrative structure on both inmate and correctional officers’ conduct, the potential influence of “organizational culture” in a correctional setting has been understudied. Correctional administrators and officers who ignore the impact of aspects of the organization may subject themselves to its possible negative influences (Stohr et al., 2012). Although scholars have long been interested in assessing organizational culture in a variety of social settings, such as public health and private organizations (Schneider, Ehrhart, & Macey, 2013), no standardized instrument has been created to assess the organizational culture of correctional settings (Stohr et al., 2012).

As Morris (2014) noted, there are myriad definitions of organizational culture. Cooke and Rousseau (1988) defined organizational culture as “the shared beliefs and values guiding the thinking and behavioral styles of members” (p. 245). Schein (1985) offered a definition of organizational culture, adopted by many, as

a pattern of basic assumptions invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration that has worked well enough to be considered valid, and to be taught to new members as the correct way to perceive think, and feel in relation to problems. (p. 9)

Investigation of an organization’s culture is considered important because it is thought that it drives both positive and negative outcomes. For instance, job satisfaction, commitment to the organization and its goals, stress experienced by members, ethical behavior, and turnover are all thought to be related to organizational culture. Criminal justice agencies are personnel laden. Therefore, if culture in such organizations is not positive, it is likely that organizational maladies such as stress, low job satisfaction and commitment, and greater turnover will result. In this research, we reanalyzed data from the Organizational Culture Instrument (OCI) developed by Stohr and her colleagues (2012) for correctional organizational settings. Using second-order modeling, we attempted to confirm that the hypothesized validity and reliability of the OCI stood up under more detailed scrutiny.

Literature Review

Key Attributes of the Organizational Culture

Several general findings can be gleaned from the literature on organizational culture. First, certain aspects of organizational culture are directly related to positive outcomes. These aspects include the primacy of leadership behavior, perceptions of organizational fairness and supervision, the existence of caring supervisors, an employee-friendly culture, letting the employees know that they are responsible for their behavior, providing staff with opportunities to have operational input into the organization and their own work, and rewarding those who do the job well. Kiekbusch, Price, and Theis (2006) surveyed five county jails to discover the causes of corrections officer turnover. They found that the procedures and policies implemented by the sheriffs, along with their management style, predicted turnover rates. Specifically, when jail operations were clearly defined, it was more conducive to career development. Furthermore, sheriffs and senior staff who visited with employees, listened to their ideas, and showed concern for complaints were more likely to elicit greater staff job satisfaction and less turnover (Kiekbusch et al., 2006; Patenaude, 2001). These findings demonstrate that leadership is a key attribute of organizational culture in a jail setting, insofar as the leader should demonstrate positive concern for employees to reduce turnover and work dissatisfaction.

Similar research has found that organizational fairness and supervision strongly influences turnover among jail officers (Lambert, 2006). Specifically, when supervisor decisions that affect staff are regarded as fair and when supervisors are perceived by as caring (Yang, Brown, & Moon, 2011), these aspects affect job satisfaction (Lambert & Hogan, 2011). In turn, high levels of organizational commitment, heightened by a positive organizational culture, lead to lower turnover rates (Lambert & Paoline, 2010).

These results also apply to occupations other than corrections. D. C. Lee, Hung, and Chen (2012) concluded that higher levels of organizational commitment enhance organizational cohesiveness and reduce turnover among office workers. Cohesiveness is important among employees, especially if fostered with team-oriented work. Erodogan, Liden, and Kraimer (2006) reported that team-oriented personnel practices increased employee satisfaction among teachers, whereas found the existence of a group-oriented culture among factory workers did the same. Parish, Cadwallader, and Busch (2008) noted that better communication between leaders and employees positively influences commitment to organizational change. A meta-analysis conducted by Dulebohn, Bommer, Liden, Brouer, and Ferris (2012) confirmed that leader–employee exchange is critical for the functioning of an organization. Schein (1992) came to the more general conclusion: Communication and innovation depend on organizational culture. Masood, Dani, Burns, and Backhouse (2006) determined that transformational leaders are better able to lead organizational innovation and change.

Research by Saxena and Shah (2008) documented that learned helplessness in an organization (generally defined as the act of giving up trying as a result of consistent failure to be rewarded or a condition in which a person suffers from a sense of powerlessness, arising from a persistent failure to succeed or be recognized) can be triggered by performance pressures, challenging projects, lack of free time, or the opportunity to engage in socialization. Positivity within these environments can best be achieved by providing an employee-friendly culture, letting the employees know that they are responsible for their behavior, and rewarding those who do the work. In Markos and Sridevi’s (2010) study, engaged staff lead to stronger work environments and more successful organizations. They noted that building this engagement starts at the top, with leaders establishing a clear mission, communicating with staff, providing opportunities for growth and development, securing the necessary resources for them to be successful, offering feedback, and incentivizing work (Schaufeli, 2012).

In sum, it is clear that a number of organizational attributes can lead to positive outcomes. One of the best sources of increased job satisfaction and productivity is exemplified by organizational leadership—leaders who communicate effectively with staff, show they care, and challenge employees. A corollary is leaders who create an engaging atmosphere, welcome staff input, provide opportunities for growth, and increase organizational engagement and communication—all leading to a higher level of job satisfaction and commitment, less staff turnover, and an improved organizational culture.

Instrument Design

The literature indicates that an OCI has greater validity when it is created to assess a particular culture (Ghosh & Srivastava, 2014). Scott, Mannion, Davies, and Marshall (2003) reviewed 13 culture measurement instruments in the health field. They found these instruments did not all measure the same organizational attributes. Some measured the type of organizational culture found within the institution, whereas others described the culture based on variables found outside it. Some instruments were created with a theoretical underpinning, whereas others were more applied in nature. What is most significant for the current research is the conclusion reached by Scott and his colleagues (2003): There is not one ideal instrument to measure the culture of health organizations—and, there is not just one instrument suited to measure the culture of all organizations. “Culture” is an anthropological term that can cover myriad dimensions, including thinking styles, power, pay and benefits, and growth opportunities, to name a few. OCIs may, therefore, not be able to account for all these dimensions.

Because the factors that influence a culture are broad in scope, it is imperative that a researcher identifies features that are indispensable to an organization before creating a tool to measure these dimensions (Ghosh & Srivastava, 2014). Bellot (2011) echoed this sentiment. In her examination of the process of defining and assessing organizational culture, she determined that each organization’s culture is unique and that all are subject to change. She noted that, because of the uniqueness of culture, there is a debate among scholars as to how it should be assessed. These remarks on OCIs were supported by Jung and colleagues (2009). In their review of 48 instruments that were designed to examine “culture,” they also concluded that there is no single instrument that can be used to measure organizational culture; different instruments may offer different insights, and it is up to the researcher to decide what is appropriate for their particular project. In sum, then, it seems that OCIs must be tailored to the organization under study and the elements of the organization the researcher wishes to assess.

The literature does suggest that one must assess organizational culture to determine whether it is changing or whether it needs changing. Cameron and Quinn (2006) argued that many corporations fail at implementing change because the company never considers the attributes of the culture when trying to effectuate change. Before you can change the culture, they conclude, you must diagnose and assess it.

Naranjo-Valencia, Jiménez-Jiménez, and Sanz-Valle (2011) examined the role organizational culture has in fostering or inhibiting innovation and imitation. Examining 471 corporations, the authors concluded that organizational culture significantly influences innovation within an organization. Interestingly, it can do so either positively or negatively. These findings support the assertion put forth by Schein (1992) that leaders must assess culture before innovation can occur, and organizational culture can influence innovation.

Workers, Rather Than Management, Know the Culture More

Another interesting finding is that the organizational culture and its attributes are often most accurately identified by those who rank lower in the organization. Goleman, Boyatzis, and McKee (2002) concluded that, for a variety of reasons, pertinent organizational information is not shared between employees and leaders, making the latter a less reliable source. Furthermore, Glaser, Zamanou, and Hacker (1987) found that leaders routinely believed their organizational climate was better than did employees workers; a similar finding was unearthed in the medical field by K. Johnson (2004). Finally, Gregory, Harris, Armenakis, and Shook (2009), in their study of organizational culture and effectiveness, noted that the best way to measure the culture of an organization is to include all its members. In general, it appears that the most valid assessment of organizational culture will come from the lower level employees.

The Need for Assessment Instruments

Research supports the notion that an assessment of a corrections facility organizational culture cannot take place if there are no instruments attuned to its specific attributes. There are a dearth of OCIs designed for assessing corrections cultures; in fact, none was found for jails. What prior studies document, however, is that jail staff work in organizations that are both similar to and different from others. Jails have hierarchies and bureaucratic features that are similar to any number of other organizations, but their work and clientele differ greatly. Jails are somewhat like prisons, but their function is to hold more short-term inmates who are generally less serious offenders. Also, jails are part of their local community in a way that prisons are not, generally operated by local government and located in the “county seat.” Related to the role of jails, numerous scholars have noted that unethical behavior among jail staff is not uncommon (Braswell, McCarthy, & McCarthy, 1991; Crank & Caldero, 2000; Iannacchione et al., 2015; J. A. Lee & Visano, 1994; Pollock, 2004; Stohr & Collins, 2009). Therefore, an OCI devised for the jail setting should include items related to ethical practice.

Assessing the Organizational Culture in a Jail Setting

Stohr and her colleagues conducted a pretest of an instrument to assess the organizational culture of a jail, with a specific focus on “lower level” workers. Their Organizational Culture Instrument (OCI) 68-item questionnaire was developed based on prior ethics- and role-focused research in a number of prisons and jails. As the research progressed, it became apparent that the nature of the organization affected whether an individual behaved in an ethical or unethical manner, and it was determinative of the kind of role the individual adopted.

Once the instrument was created, the researchers obtained practitioner and scholar critiques to determine its face validity. They also met with three upper level staff members to discuss the instrument at length. A revised instrument was then pretested with command staff, leading to a set of final revisions. The final OCI was developed with seven dimensions in mind, including the following:

Participatory management or lack thereof (derived from Drucker, 1954; Lovrich, 1989; Maslow, 1961/1998; McGregor, 1957/2001; Ouchi, 1981; Stohr, Lovrich, Menke, & Zupan, 1994). This includes Items 1, 4, 11, 13, 25, 28, 33, 38, 43, 52, 55, 59, 62.

Satisfaction with pay, training, promotions, and personnel decisions (derived from Gordon & Milakovich, 1998). This includes Items 5, 7, 10, 17, 21, 44, 45, 50, 51, 53, 54, 64.

Ethics (derived from Braswell et al., 1991; Crank & Caldero, 2000; J. A. Lee & Visano, 1994; Pollock, 2004). This includes Items 6, 9, 16, 18, 23, 30, 36, 40, 51, 58.

Job enrichment, including task significance, task identity, skill variety, autonomy, and feedback (derived from Hackman & Oldham, 1974). This includes Items 12, 15, 22, 27, 35, 49, 57, 60, 61, 63, 65, 66.

Learning organization (derived from Crank & Giacomazzi, 2009; Gordon & Milakovich, 1998; Maslow, 1961/1998; McGregor, 1957/2001; Peters, 1992, 1995). This includes Items 2, 8, 14, 19, 24, 34, 39.

Respect and acceptance (derived from Gordon & Milakovich, 1998; Maslow, 1961/1998; McGregor, 1957/2001). This includes Items 3, 20, 31, 32, 37, 41, 46, 47, 48, 56.

Public perception of the work/pride in the work (derived from Conover, 2000; R. Johnson, 2002; Lombardo, 1989 and Lt. Shepherd at the County Detention Center). This includes Items 29, 42, 67, 68.

The original research took place in 2007, and the process and protocols were reviewed by an institutional review board at Boise State University. Two hypotheses were tested for this project.

Hypothesis 1: There will be a high level of congruence (α of at least .70) between instrument items.

This hypothesis was supported. The Cronbach’s alpha was .96, indicating an extremely high level of reliability. Cooper and O’Connor (1993) argued that in applied settings where there is intended change, the alpha for an instrument should be more than .90, because any organizational change will have a direct impact on the lives of employees. Therefore, instruments that measure constructs in this type of environment should have a higher degree of reliability, which this one did.

The researchers were concerned that the high alpha may have been enlarged because the N (105, once missing cases were removed) was not much larger than the number of items in the questionnaire (68). To determine whether the high alpha was related to the number of variables, factor analysis was conducted for each dimension separately and then together. All dimensions, when run separately, had acceptable alphas (>.70), except for the public perception dimension, which had an alpha of .67. Factor analyzing these items separately led to some items being removed from two dimensions (participatory management and satisfaction). Once these items were removed, a separate factor analysis of all dimensions was run, resulting in an alpha of .926. This indicated that the high alpha was not caused by the number of variables, but by the reliability of the instrument.

Hypothesis 2: The items would load on their respective seven dimensions.

An exploratory factor analysis (EFA) was run to determine whether the items in the OCI questionnaire would load on the predetermined seven dimensions. This second hypothesis was not supported; the authors found that the items loaded on 19 components, with an eigenvalue >1 (74.67% of the variance was explained).

In sum, there was general agreement on 34 of the 68 items, suggesting that many staff viewed their work favorably. The other 34 items, however, showed less agreement, specifically related to nine items. These items focused primarily on satisfaction with pay, training, promotions, personnel decisions, management, job enrichment, and public perception of their work/pride in their work. These findings highlight the importance of assessing the organizational culture to support administrative efforts to identify and (hopefully) address problematic issues in their facility. Once this study was completed, attention turned to conducting a confirmatory factor analysis (CFA) and as well as structural equation modeling (SEM) or second-order modeling.

EFA Versus CFA

Factor analysis has multiple purposes, but a primary one is to explain variation among many original items using fewer created factors (DeVellis, 2003). As noted above, EFA on the items of the OCI was the appropriate technique to use initially. As DeVellis (2003) reported, EFA is often used to determine the underlying structure of items. Because this was the first time this instrument was tested, examining this underlying structure was crucial. Fabrigar, Wegener, MacCallum, and Strahan (1999) also explained that EFA is beneficial when the goal of a research project is to arrive at a “parsimonious representation of the associations among measured variables.” In addition, Hurley et al. (1997) argued that EFA is appropriately utilized when scales are being developed, which was the case in the Stohr et al. (2012) study.

CFA, however, is used to “confirm a particular pattern of relationships predicted on the basis of theory or previous analytic results” (DeVellis, 2003, 77). Furthermore, a strong theoretical basis must be developed before CFA can be implemented (Hurley et al., 1997; Thompson, 2004). Because these items and factors were based on theory and the authors’ results from the 2012 study, we argue that CFA is now appropriate. As noted previously, items were dropped from the questionnaire, and all factors were analyzed individually to confirm their reliably and we can conclude that the initial analysis would indicate that a theoretically sound instrument was created. Hurley et al. (1997) conducted a discussion panel on EFA versus CFA and found that the overriding sentiment was that researchers must not wed themselves to one or the other. Both have merit, and they noted that research may often start with studies using EFA; later work can employ CFA to demonstrate what can be confirmed.

Gerbing and Hamilton (1996) tested this approach to these two factor analysis procedures, concluding that EFA can be a useful investigative strategy prior to validation with CFA. Berberoglu and Tosunoglu (1995) adopted this approach in developing and testing their Environmental Attitude Scale (EAS). The EAS was created to measure the attitudes and behavior of people relative to environmental deterioration. The 47-item scale was given to a pilot group, and the data were analyzed by EFA, resulting in four meaningful factors. CFA was then run, confirming the four factors found in the EFA analysis.

B. Johnson and Stevens (2001) adapted a version of this approach as well. When testing the School Level Environment Questionnaire (SLEQ), the authors split their sample in half, testing half of the data with EFA and the other half with CFA. EFA was used to examine the factor structure of the SLEQ. CFA was then employed to determine whether the factor structure acquired in the EFA could be confirmed. Seven meaningful factors were detected in the EFA, and the CFA found all seven factors to be significant. However, two of the models had nonsignificant parameters, leading the authors to create a five-factor version of the SLEQ. This suggests that CFA is a helpful extension of EFA, allowing researchers to refine their instruments.

Other scholars, such as Diamantopoulus and Siguaw (2006), utilized this approach in examining organizational scale development. The EFA located five items that cross-loaded and a sixth that did not load on either of their two predetermined factors. These six items were dropped from their scale. Further CFA testing determined that the fit of the overall model was quite acceptable. Similar studies were conducted by Cassidy, Hestenes, Hedge, Hestenes, and Mims (2005), who used CFA to evaluate the three factors found in their EFA models, and Ang (2005), who used CFA to test the factor structure of the scores originally obtained from EFA.

SEM or Second-Order Modeling

Driven by the theoretically conceptualized structure of organizational culture in correctional settings and available scientific evidence derived from the previous study, the research team believes that organizational culture is a multifaceted construct that consists of at least these seven dimensions (Stohr et al., 2012). Therefore, in the current study, we anticipated that if the CFA yielded solid statistical results regarding the goodness of the properties of each dimension, then we would test the hypothesized structure of the organizational culture by utilizing a second-order (or higher order) modeling strategy within the framework of SEM.

According to Chen, Sousa, and West (2005), second-order modeling is a common technique for validating a measurement instrument that assesses multiple dimensions (each dimension is defined by multiple items) that are theoretically and statistically correlated. Second-order modeling is typically applied when researchers attempt to answer the question, “is there a general higher order factor that accounts for the independent, but correlated lower level constructs?” Research should consider employing a second-order model when lower order constructs are highly correlated with one another, and researchers have theoretically conceptualized or hypothesized that a higher order overarching factor that accounts for the variance and covariance among lower level factors or dimensions.

In fact, there are more benefits that are associated with using second-order modeling than simply employing CFA when the construct property of first-order factors is well established (Chen et al., 2005). First, second-order modeling allows researchers to build a statistically based structure that reflects the conceptualized framework that is hierarchical in nature. Second, whereas the CFA accounts for the commonality shared among multiple indicators, second-order modeling accounts for the commonality shared by the factors among those factors. Third, on one hand, by using CFA, only the true score variance is pulled out to estimate to individual factors. One the other hand, second-order modeling not only produces error-free estimates of individual factors but also separates the variability among first-order factors that are accounted for by the higher order factor from the variability that is uniquely associated with first-order factor variance. In other words, the error-free unique lower order factor variance that is not accounted for by the higher order factor is represented by the low-order factor disturbance. Finally, when the constructs that are being assessed are theoretically conceptualized within a hierarchical framework, by applying second-order modeling, the researcher will produce fewer parameters, resulting in a more parsimonious theoretical and statistical model, while simplifying and clarifying the complex nature of the measurement structure.

Method

The purpose of this current research, then, is to advance our analysis of the OCI by conducting a CFA, which was to confirm our previously concluded pattern of results. Drawing on Kline (1994), the CFA is a better factor analysis to utilize when testing hypotheses, which the current research will also undertake.

In addition, this study will attempt to test the conceptualized organizational cultural structure in correctional settings by using second-order modeling strategy. Stohr and her colleagues (2012) hypothesized that there are many identifiable attributes or dimensions of organizational culture in a jail setting (as noted in the foregoing; and see, Stohr et al., 2012). In order words, organizational culture could be conceptualized with a second-order model, in which each of the dimensions is a partial reflection of the organizational culture in a given correctional agency. By using second-order model analysis, we will be able to test whether the conceptualized higher order factor (organizational culture in this case) “accounts for the pattern of relationships between the first-order factors” (Chen et al., 2005, p. 473).

Hypotheses

This research will put the conceptualized measurement structure through rigorous statistical tests to determine whether (a) this instrument accurately depicts each dimension of organizational culture and (b) organizational culture is a construct that is multifaceted and hierarchical in nature, in that participatory management, job satisfaction, job enrichment, learning organization, ethics, respect and acceptance, and public perception and pride serve as lower order factors.

Analytic Strategy

The questionnaire administration and instrument construction are further described in the work by Stohr and her colleagues (2012). In this study, we employed CFA and second-order modeling within the framework of SEM to test the hypothesized research questions. All analyses were conducted using Mplus version 7. Model parameters were estimated using weighed least square mean variance (WLSMV) adjusted algorithm because we are dealing with a relatively small sample size (N = 135) with ordinal indicators. The absolute fit index, chi-square statistics to indicate the goodness of model fit were considered with alternative model fit indices, including the other absolute fit index, namely, root mean square error of approximation (RMSEA) and comparative fit index (CFI) as well as Tucker–Lewis index (TLI). For chi-square statistics, a nonsignificant result is preferred to indicate a good fitting model. The cutting point of RMSEA for indicating a close fitting model is a value less than .05, and the value that constitutes an acceptable model fit is less than .08. For both CFI and TLI, a value of .90 or larger constitutes an acceptable fit, and a value of .95 or larger indicates a good model fit.

Due to the fact that, statistically, a second-order model cannot fit better than CFA without a second-order overarching factor when determining the goodness of the second-order model, it was based on the absolute chi-square difference statistics. Besides answering the question “how adequate the second-order model is,” we also sought to answer the question, “does the second-order model result in decremental fit as compared with the general CFA model?”

Findings

CFA of First-Order Factors

As indicated in Table 1 and Figure 1, the result of chi-square significant test did not meet the standard for indicating an adequate model fit. However, with a CFI value of .977, TLI value of .971, and RMSEA value of .051 [.032, .067], all these alternative model fit indices met the strict cutting values to determine a good model fit for the measurement model. Moreover, all the first-order standardized loadings were substantial, significant, and made sense theoretically. After checking the modification indices, no localized illness of fit was found; as a result, no correlated residuals were added. Therefore, taking all this into consideration, the statistical evidence demonstrates excellent properties for each dimension of organizational culture.

Table 1.

Measurement Model.

χ²	p	df	RMSEA	CFI	TL1
227.162	.0016	168	.051 [.032-.067]	.977	.971
Dimension	Items		Completely standardized loadings
Participatory Management	PM1		.795***
	PM2		.572***
	PM3		.792***
Ethic	ETH1		.856***
	ETH2		.730***
	ETH3		.585***
Job Satisfaction	JS1		.793***
	JS2		.836***
	JS3		.661***
Learning Organization	LO1		.917***
	LO2		.675***
	LO3		.683***
Job Enrichment	JE1		.786***
	JE2		.626***
	JE3		.670***
Respect and Acceptance	RA1		.847***
	RA2		.718***
	RA3		.715***
Public Perception and Pride	PP&P1		.815***
	PP&P2		.561***
	PP&P3		.736***

Note. No correlated residual is added. RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index.

*p < .05. **p < .01. ***p < .001.

Figure 1.

Measurement model of organizational culture with seven dimensions.

Notably, the first-order factor correlation ranged from .474 to .877, meaning each organizational cultural dimension or factor was substantially correlated (results are only summarized here due to space limitations). Consequently, by imposing a higher order structure and testing the goodness of the second-order model by examining the statistical criterion, we tested the second hypothesis, which is whether the organizational cultural factor could account for correlation among first-order factors while maintaining a good fitting model that does not result in significant decremental fit.

Second-Order Modeling

As indicated in Table 2 and Figure 2, the second-order model for organizational culture with lower order seven factors presents an adequate model fit. Although the chi-square significance test did not meet the standard for indicating a good model fit, with a CFI value of .977, a TLI value of .972, and an RMSEA value of .050 [.031, .066], these alternative model fit indices values meet the strict cutting value to indicate a good fitting model fit.

Table 2.

Second-Order Model of Organizational Culture.

χ²	p	df	RMSEA	CFI	TL1	Δχ²	Δdf	p
234.879	.0020	176	.050 [.031-.066]	.977	.972	11.401	8	.180
		Completely standardized second-order factor loadings
Participatory Management		.818***
Ethic		.951***
Job Satisfaction		.756***
Learning Organization		.898***
Job Enrichment		.841***
Respect and Acceptance		.744***
Public Perception and Pride		.680***
Dimension		Items	Completely standardized first-order item loadings
Participatory Management		PM1	.795***
		PM2	.570***
		PM3	.794***
Ethic		ETH1	.858***
		ETH2	.728***
		ETH3	.585***
Job Satisfaction		JS1	.793***
		JS2	.835***
		JS3	.662***
Learning Organization		LO1	.918***
		LO2	.676***
		LO3	.682***
Job Enrichment		JE1	.786***
		JE2	.624***
		JE3	.671***
Respect and Acceptance		RA1	.846***
		RA2	.721***
		RA3	.714***
Public Perception and Pride		PP&P1	.815***
		PP&P2	.562***
		PP&P3	.736***

Note. Significant first-order factor disturbance is allowed to correlate with one and another. No first-order item correlated residual is added. Δχ² = chi-square change; Δdf = change in degree of freedom; RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index.

*p < .05. **p < .01. ***p < .001.

Figure 2.

Second-ordered organizational culture with seven dimensions.

Furthermore, as shown in Table 2, all the second-order factor loadings are statistically significant, strong, and theoretically founded. No first-order correlated indicator residual was added into the model; the first-order individual-specific factor disturbance freely correlated with each other. Most important, the chi-square significant statistic resulted in a nonsignificant result. This means that imposing the second-order structure, in which the organizational culture factor acts as the overarching factor that accounts for the commonality among first-order factors, did not result in a decremental fit. In other words, not only is our hypothesized second-order model as good a fit as the first-order measurement model, but also the second-order model is more parsimonious and structured on the basis of a theoretical foundation. Therefore, we retain the second-order model and argue that, on the basis of our theoretical foundation and supportive statistical results, the selected items in the pretested instrument demonstrate good organizational cultural properties as a second-ordered model with seven lower order factors, with each of them defined by three corresponding indicators.

Discussion

Oftentimes, questions imposed by researchers are composed of abstract concepts or multifaceted constructs. The merits and drawbacks of using a quantitative approach versus a qualitative approach are beyond the scope of the current study. However, when conducting research that is quantitative in nature, we argue that good-quality research starts with good measurement to ultimately minimize systematic and random measurement error (Singleton & Straits, 2010). In measurement development, especially when dealing with an abstract notion with enriched internal meanings, levels, and structures, researchers are more likely to introduce more measurement error. These measurement errors derive from, but are not limited to, the process of conceptualization, operationalization, survey distribution, data collection, and data analysis.

As in this case of measuring organizational culture in correctional settings, capturing the nature of the construct that is being studied is an ongoing process that demands testing and retesting. Methodologists believe that a solid instrument will not only demonstrate a high level of reliability but also construct validity that is constituted from the content validity, and criterion validity (concurrent validity, predictive validity, convergent validity, and discriminant validity).

Stohr et al. (2012) created an OCI with a high level of reliability, but the validity of the instrument was awaiting statistical verification. Therefore, the current study was designed as a response to the question that was left unanswered in the previous research. As discussed in the “Results” section, this study has further confirmed the pattern or the structure of theoretically conceptualized organizational culture by the statistical results produced by using CFA and SEM. We have demonstrated that this OCI has a high level of reliability and solid content validity (see the appendix for dimensions and their respective items). To further validate the instrument, criterion validity could be obtained by using the OCI in tandem with other measures of organizational outcomes to determine whether a positive organizational culture assessment by the OCI is aligned with decreased turnover and stress and increased job satisfaction and commitment and ethical practices.

This research is part of an effort to further validate one critical construct in correctional settings, organizational culture. With the available statistical and scientific evidence derived from the current and previous study, we conclude that organizational culture and its attributes are identifiable and testable. Our findings, based on the use of CFA and second-order modeling, indicate that there is sufficient statistical evidence to claim further validation of the OCI and its seven theoretically based dimensions.

Conclusion

Organizations form and function for years without any analysis of why they operate the way they do. Correctional agencies with their legally sanctioned power are particularly in need of an assessment of their culture. The serious evaluation of such cultures can provide information to managers interested in fostering productive, healthy, and ethical work and living environments for staff and inmates.

The current research is not without limitations. First, the results may be lacking in generalizability as the data were obtained from one jail. To render the statistical results generalizable, this instrument needs to be rigorously retested in multiple settings.

Second, a large sample size is preferred when conducting CFA and SEM. Given our small sample study size, this shortcoming was mediated by the robust statistical results. In other words, the statistical results shown in Tables 1 and 2 demonstrated high factor loadings and high statistical significance levels, and all model fit indices meet the strict cutting value for indicating a good model.

Finally, organizational culture is a multidimensional abstract notion, and theoretically, there might be more organizational cultural dimensions than we originally hypothesized. For instance, Stohr et al. (2012) argued that a separate leadership dimension might be one of the organizational cultural dimensions that was left out of the OCI.

We do believe the OCI holds much promise for researchers and prison administrators interested in learning more about the culture of correctional institutions. Further research is necessary, to refine the items and determine the applicability of the instrument to other institutions in other states, but we would argue that research on the organizational culture of correctional facilities has been advanced with the development and validation of the OCI.

Footnotes

Appendix

Organizational Culture Items.^a

PM1	Staff usually have the opportunity to determine, in a meaningful way, how their job is done.
PM2	Managers tend to tell everyone else what to do without asking for input.
PM3	Before making a decision, staff input is usually sought out by managers here.
ETH1	When we are asked to complete a new task, we are sufficiently trained in how to do it.
ETH2	The rationale for new policies and procedures are usually thoroughly explained to staff.
ETH	Staff do not receive enough training to prepare them to do their job.
JS1	The managers “model” what it means to be ethical.
JS2	When unethical or illegal practices are uncovered, the guilty party or parties are punished.
JS3	Everyone who works here knows the code of ethics that governs correctional work.
LO1	My work allows me to practice a number of skills.
LO2	My work allows me to participate in projects from the beginning to the end.
LO3	I’m allowed to work independently when I know how to do a particular task.
JE1	The staff at this facility believe in innovation and positive change.
JE2	Most of the people I work with are willing to try new policies and practices that they believe will improve the workplace.
JE3	Staff are sometimes publicly humiliated by supervisors when they make a mistake.
RA1	Coworkers generally treat each other with respect here.
RA2	Women are less likely to be accepted as staff at this facility.
RA3	Staff generally treat inmates with respect at this facility.
PP&P	I discourage people who I like from applying for the kind of job that I have.
PP&P	I have relatives or friends who would like a job like mine.
PP&P	If I could choose over again, I would choose this work.

Note. PM = participatory management; Eth = ethic; JS = job satisfaction; LO = learning organization; JE = job enrichment; R&A = respect and acceptance; PPP = public perception and pride; OCI = Organizational Culture Instrument.

Use of the OCI requires permission of the authors. Direct queries to Mary K. Stohr or Craig Hemmens at mary.stohr@wsu.edu or craig.hemmens@wsu.edu.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Xiaohan Mei is a PhD student in the Department of Criminal Justice and Criminology at Washington State University. His research interests include quantitative methods, scientific measurement, and corrections.

Brian Iannacchione is an assistant professor in the Department of Criminal Justice at the University of Northern Colorado. His research interests include corrections and rural crime.

Mary K. Stohr is a professor in the Department of Criminal Justice and Criminology at Washington State University. She has published more than 90 academic works in the areas of correctional organizations and operation, correctional personnel, inmate needs and assessment, program evaluation, gender, and victimization. With her colleague Craig Hemmens, she has developed role, ethics, and organizational culture instruments for use in jail and prison settings.

Craig Hemmens is chair and professor in the Department of Criminal Justice and Criminology at Washington State University. He has published 20 books and more than 200 articles and other works. His research interests include corrections and legal issues in criminal justice.

Marianne Hudson has an MA in criminal justice from Boise State University, where she presently works as a lecturer.

Peter A. Collins is an assistant professor in the Criminal Justice Department at Seattle University. His interests include law and courts, management and organizations, corrections, and criminal justice/public policy. His recent scholarship has focused on the death penalty, youth incarceration, and the intersection of the law and homelessness.

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