Validation of the Good Lives Assessment of Domains in an Adult U.S. Sample

Abstract

This article offers initial validation of the Good Lives Assessment of Domains (GLAD). Data were collected from an electronic survey of 1,484 American adults. Participants were recruited via paid research panels using quotas set to match the U.S. population on Age, Race/Ethnicity, Sex/Gender, Education, and Household Income. Participants responded to a set of items including 48 original items to assess perceptions of life satisfaction in the 11 domains described in the GLM and the 5 Satisfaction with Life Scale (SWLS) items. Factor Analysis indicated 45 final items that loaded onto 9 unique factors, with all loadings ranging between 0.391 and 0.854 with acceptable model fit (RMR = 0.070, CFI = 0.866, RMSEA = 0.063). Cronbach’s Alphas demonstrated acceptable reliability, with items achieving alpha scores greater than .7 in all individual domains and for overall GLAD scores. The correlation between GLAD and SWLS scores was .610 (p < .001). An Independent samples T-test found a significant mean difference (t = 4.360, p < .001, mean difference = 8.15737) in GLAD scores between respondents who reported no engagement in crime and deviance and those who reported engagement in crime and deviance.

Keywords

satisfaction with life the Good Lives Model happiness life satisfaction

The Good Lives Model of Offender Rehabilitation (GLM) is a strengths-based intervention that seeks to teach people ways to attain primary human goods in prosocial ways (Ward, 2002b). Originally created for criminal offenders, the GLM focuses on developing and improving clients’ perceptions of their own satisfaction within 11 life domains that the GLM views as universal to human happiness. As clients work through the GLM with their clinicians, they complete a series of activities to help improve their satisfaction in the important life domains. These steps include identifying which domains they feel are important to them, exploring ways in which their behaviors have led to increases or decreases in their satisfaction with identified important domains, revealing obstacles to improved domain satisfaction, and developing plans to amplify their strengths and overcome obstacles to domain satisfaction (Yates et al., 2010; Yates & Prescott, 2011). According to the propositions of the GLM, clients can lead Good Lives when their attempts to gain and maintain domain satisfaction align with prosocial values and norms (Harris et al., 2019).

It is important for any intervention seeking to change specific and identifiable variables, such as domain satisfaction, to have direct and full measures of those variables. Without direct and full measures of those variables clinicians, clients, and researchers cannot be sure that a putative outcome is a result of the intervention (Mears, 2010). Presently, the only available measure of clients’ perceptions of GLM domain satisfaction is the Common Good Lives Questionnaire (CGLQ). The CGLQ asks clients to rate two items for each GLM domain. One item asks the clients to rate their perceived satisfaction in the domain. The other item asks the clients to rate the importance of that domain to their current life positioning. Given that only one of these items is a direct measure of clients’ perceptions of domain satisfaction and this item asks only a global perception of domain satisfaction, the CGLQ does not provide a direct and full measure of GLM domain satisfaction.

The present study sought to address the lack of direct and full measures of GLM domain satisfaction by validating the Good Lives Assessment of Domains (GLAD). Validation procedures included Confirmatory Factor Analysis to determine if individual GLAD items would load onto factors in a manner consistent with GLM domains, internal reliability testing of individual domain items within their respective GLM domains and of aggregated GLAD items, and convergent validity analysis between GLAD scores and Satisfaction with Life Scale scores (Diener et al., 1985).

Modeling Satisfaction With Life

Aristotle suggested that happiness was the ultimate aim of human existence. He viewed happiness as a primary condition—one that people seek for the sake of achieving happiness itself. He suggested that people achieve happiness by acquiring conditions of satisfaction in relevant life domains, or somewhat universal human needs that are important to all individuals (Aristotle, 1999). Arguably, scientific quantitative efforts to discern these life domains began with Campbell et al. (1976). Since that time, researchers have reported sets of life domains that are strikingly similar to each other, and to several of the original ideas posed by Aristotle. As can be seen in Table 1, these domain sets often include primary human needs like basic survival needs, connections to other people, means of productivity and work, leisure or recreation, and mental and physical health, among others.

Table 1.

Domains by Author.

Author	Domains
Campbell et al. (1976)	Leisure/non-work activities; Finances; Work; Education; Friends; Family/marriage; Physical environment; Health; Civic involvement; National satisfaction
Martikainen (2009)	Martial status and intimate relationships; Children; Education; Occupational status; Smoking & drinking; Leisure-time activity; Social relationships; Feelings toward future
Musikanski et al. (2017)	Psychological well-being; Health; Time balance; Lifelong learning, arts, and culture; Community; Social support, Environment; Governance; Standard of living/economy; Work; Demographics
Sheldon et al. (2001)	Pleasure-stimulation; Money/luxury; Relatedness; Popularity/influence; Security; Autonomy; Competence; Physical thriving; Self-esteem; Self-actualization/meaning
Ramm (1996)	Renewing recreation; Money; Rewarding occupation; Companionship; Intimacy; Affirmation; Health; Freedom; Security; Meaningful material objects

While these domains are consistent across models, achieving happiness does not require achieving satisfaction in all domains at all times. Rather, the importance of the domains fluctuates across people and across their lifetimes. For instance, a 15-year-old Western student may give greater importance to their freedom in decision making while preparing to take the test for their driver’s license. That same student, 10 years later, may shift importance to building connections with others and achieving greater satisfaction with their work. Fifty years later, that student may shift focus to health as they work to lose excess weight gained over the early part of their retirement. Because they are universal—yet individualized—the consistency of these domains makes them a logical place to start when looking to improve satisfaction with life. One SWL model has garnered attention of practitioners and researchers from several countries around the world. It was originally built as an intervention with criminal offenders under the assumption that improving satisfaction with life would help decrease offending behaviors.

The Good Lives Model of Offender Rehabilitation

Introduced by Tony Ward (2002a, 2002b), The Good Lives Model of Offender Rehabilitation (hereafter The Good Lives Model or GLM) calls attention to 11 domains consistent with the greater research into SWL. Figure 1 offers a graphical representation of the GLM domains. Table 2 defines the GLM domains.

Figure 1.

Domains of the Good Lives Model.

Table 2.

Domains of the Good Lives Model and Their Definitions.

Domain	Definition
Agency	The freedom to make your own choices in life without interference from others. Living a life with a sense of self-determination and autonomy.
Inner peace	Stability in emotions and mental health; being free from psychological disorders, emotional turmoil, and stresses.
Knowledge	Having an understanding of important information in order to move through life with overall ease.
Life	Being physically able to do activities important to my life, having the basic survival needs, working toward optimal health, and being able to recover after a physical health incident.
Spirituality	Having a meaning or purpose in life. People with a sense of spirituality believe their time on earth is for something bigger than themselves . . . something more than their own life/experience.
Creativity	Making, expressing, or trying things that are new, unique, or original. Creative people can do something new in their own life or they can make something new for others in the world.
Pleasure	Feeling good in the here and now. Overall sense that one’s life is happy.
Community	The sense of acceptance and value a person gets from social interactions with other people.
Relatedness	The deep and intimate relationships we have in life. We feel relatedness when we have close bonds with people in our lives.
Excellence in work	Having a job that we can master and enjoy and that pays us fairly for our time and effort. We feel the work is meaningful, others tell us we are good at it and we make enough money to meet our other needs.
Excellence in play	Having an unpaid activity or hobby that we can master and enjoy. We feel the activity or hobby is meaningful and others tell us we are good at it.

To engage the Good Lives Model, practitioners undertake a multi-step process targeting SWL in the life domains. These steps include studying the GLM and using it to help people understand the domains that are most important to their lives. Practitioners then assess clients’ current perceptions of satisfaction in these domains and their strengths and improvement abilities in each important domain. Practitioners then work with clients to build a plan to achieve satisfaction in their important domains. During follow-up sessions, clients and practitioners work to assess progress and overcome obstacles to achieving SWL.

Conceptual validation and systemic review have supported the foundations and interventions of the GLM (Mallion et al., 2020; Willis et al., 2014). Details for applying the GLM in intervention are available for practitioners (Olson et al., 2016; Prescott, 2013; Yates et al., 2010; Yates & Prescott, 2011). And, the GLM has been supported in working with people from diverse populations and in diverse settings (Barendregt et al., 2018; Damme et al., 2016; Harris et al., 2019; Langlands et al., 2009; Siegert et al., 2007; Walgrave et al., 2021; Whitehead et al., 2007).

Despite this attention and effort, the GLM does not yet have a validated measure of achievement in its domains. The only existing measure of GLM domain satisfaction is the Common Good Lives Questionnaire (personal communication, G. Willis, July 25, 2015). The Common Good Lives Questionnaire (CGLQ) uses a Likert-type scale as it asks clients to globally rate both their perceived importance of, and their current satisfaction in, each domain. Because of its global rating on these two items, the CGLQ is inadequate for practitioners who desire greater depth in assessing areas to target in clients’ SWL work. For instance, a CGLQ rating of 1 in satisfaction for the Spirituality domain does not help indicate why satisfaction is low, nor does it help target ways to improve satisfaction in Spirituality. The simplicity of the CGLQ is also inadequate for researchers wishing to evaluate the outcomes of GLM interventions and/or compare interventions against each other.

The only other known attempt to create a measure of GLM domains failed to account for a majority of GLM domains. Harper et al. (2021) work on their Good Lives Questionnaire validated only five domains, three of which related directly to the original GLM domains. The three GLM domains accounted for were Inner Peace, Social Connectedness, and Spirituality. Thus, like the Common Good Lives Questionnaire, the Good Lives Questionnaire does not fill the void in GLM measurement. These inadequacies limit the ability of psychology and criminology to expand the use, and to evaluate the effectiveness, of the GLM.

The present study corrects these inadequacies by offering initial empirical validation of the Good Lives Assessment of Domains (GLAD). The GLAD is a self-report instrument designed to assess satisfaction in GLM domains.¹ Rather than single and global items, the GLAD asks people to consider their satisfaction on a set of three to six items for each GLM domain. As a planned part of a multi-hypothesis study, participants were asked to rate their satisfaction on 48 original GLAD items. Validation efforts for these GLAD items included factor analysis to determine whether the proposed GLAD items loaded onto 11 factors consistent with their intended placement within the GLM domains. Then, construct replicability analysis determined if the model could replicate across studies. Next, a series of inter-item reliability tests determined whether the remaining individual GLAD items were answered consistently with other items within each domain, and within all GLAD items. Then, construct validity testing determined if the items appeared to measure the latent variables of interest and whether GLAD scores related to scores on Diener et al.’s (1985) Satisfaction with Life Scale. Finally, predictive validity testing determined whether respondents who reported higher satisfaction in the combined GLM domains also reported lower engagement in crime and deviance.

Methods

Scale Development

Prior to the current study, the primary author led a different group of researchers in developing the GLAD. The process began with all researchers reading current literature on the Good Lives Model (GLM), including several publications and websites by GLM creators. Weekly research meetings insured that all researchers were familiar with the definitions and meanings of the GLM domains. Once researchers demonstrated appropriate knowledge of all domains, each researcher was assigned to a domain for item development. Once items for each domain were developed, all researchers met to discuss proposed items and agree on a set of initial GLAD items.

Those initial items were then piloted to a convenience sample of college students. Responses were subjected to a series of validation tests to determine whether they would be included in the GLAD version for full testing. Items from this pilot were kept if they met the criteria described below for exploratory Factor Analysis and Cronbach’s Alpha, and when all researchers agreed to their inclusion. Items retained from pilot testing were then subjected to the current validation study with a national quota sample of adults in the United States.

Participants and Sampling

The current validation study recruited U.S. adults based on quotas that matched five demographics of the U.S. population. The demographics and their quotas were Gender (Female 51%, Male 49%); Age (18–34, 30.5%; 35–54, 34.4%; 55+, 35.2%); Education (Some HS or less 10.9%, HS/GED 40.8%, Some college 10%, Associate degree 18.3%, Bachelor degree 10%, and Graduate degree 10%); Race (Non-Hispanic White 62.3%, Non-Hispanic Black 12.4%, Hispanic 17.3%, Asian 5.4%, and Other race 2.6%); and Household Income ($0–<$50k USD 40.3%, $50k–>$100k USD 32.5%, $100k+ USD 27.4%). Participants were excluded if they reported Age below 18 or above 89. Participants were also excluded if they matched a previously filled quota, if they chose not to complete the survey, or if they did not correctly answer two Attention Check items embedded within the survey. These items were included to help ensure that participants were not speed checking response items.

Participants were recruited via a convenience strategy by Qualtrics Research Services. Using a national panel network, Qualtrics and its affiliates contacted people who had expressed interest in participating in research studies on the internet. Contacts occurred via electronic means such as email, website interactions, customer loyalty panels, etc. While at a location of their choosing, interested participants clicked a link to navigate to an informed consent statement for the electronic survey. Clicking a radio button on the informed consent page opened the survey. Participants who completed the survey on the Qualtrics platform were compensated between $2.10 and $3.50 USD. Participation was anonymous with no personally identifying information collected.

The analysis plan for validation of the GLAD items included using Confirmatory Factor Analysis to test model fit. The original GLAD consisted of 48 items that were hypothesized to fit onto the 11 domains of the Good Live Model, with three to six items loading to each domain. Using Koran’s (2020) estimates of sample size for these procedures at conservative estimates of 12 factors with 6 indicators for each factor, a minimum sample of 350 was required for the present study.

Data was collected between September and November 2021. Recruitment continued until all quotas were reasonably reached, with all demographic categories opened for approximately the last 75 participants. 2,244 participants started the survey, and 1,508 participants (67.1%) successfully completed their responses. The study protocol was reviewed and classified Exempt by the Institutional Review Board at the researchers’ academic institution.

Measures and Instrumentation

Good Lives Assessment of Domains (GLAD)

The present study sought to validate the Good Lives Assessment of Domains (GLAD). The GLAD is a self-report instrument designed to assess respondents’ perceived satisfaction in the 11 domains of the Good Lives Model. There were 48 items in the original GLAD design. The domains and their number of items were Agency (5), Inner Peace (4); Knowledge (5); Life (5); Spirituality (3); Creativity (3); Pleasure (5); Community (4); Relatedness (4); Excellence in Work (6); and Excellence in Play (4).

Each GLAD item was answered on a 5-point Likert-type scale. GLAD response choices were 1 = Disagree; 2 = Somewhat Disagree; 3 = Neither Agree nor Disagree; 4 = Somewhat Agree; and 5 = Agree. Responses to each original item are added together and result in domain scores whose ranges vary with the number of items, and an overall GLAD score that ranged from 48 to 240. Higher scores reflect higher satisfaction. The final 45 GLAD items, arranged in their GLM domains, are presented in Supplemental Appendix 1.

Satisfaction with Life Scale

The Satisfaction with Life Scale, or SWLS, (Diener et al., 1985) is a public domain, self-reported instrument designed to assess respondents’ global satisfaction with life (e.g., “In most ways, my life is close to my ideal”; “I am completely satisfied with my life”). There are five items on the SWLS, all of which are answered on a 7-point Likert-type scale. SWLS response choices are 1 = Strongly Disagree; 2 = Disagree; 3 = Slightly Disagree; 4 = Neither Agree nor Disagree; 5 = Slightly Agree; 6 = Agree; 7 = Strongly Agree. Responses to all items are added together and results in an SWLS score ranging from 5 to 35. Higher scores reflect higher satisfaction with life.

Previous studies have found the SWLS to be reliable and valid, both in and out of the U.S. (Aishvarya et al., 2014; Pavot & Diener, 2008; Zanon et al., 2014) Means of SWLS scores generally fall between 23.0 and 25.2 with standard deviations between 2.8 and 6.4. The five items were found to load onto one factor, with item loadings between 0.588 and 0.911 (Diener et al., 1985; Pavot & Diener, 2008; Slocum-Gori et al., 2009). Internal reliability as assessed in a sample of college students revealed a Cronbach Alpha of .816 (Olson et al., 2020).

Strain Scale

Broidy’s (2001) Strain scale measured respondents’ emotional reactions when they are “unable to reach (their) goals” and “bad things happen to (them).” The scale consisted of 14-items related to respondents’ reactions to blocked goals and unwanted events (e.g., When bad things happen to me, I feel. . .Angry). Each item was rated on a scale of 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Always. Several individual items were chosen from this scale for construct validation.

Positive and Negative Affect Schedule

The Positive and Negative Affect Schedule (Watson et al., 1988) measured respondents’ ratings for positive and negative affect (e.g., Indicate the way you have felt . . . Interested) over the last 30 days. Each item was rated on a scale of 1 = Very slightly or not at all, 2 = A little, 3 = Moderately, 4 = Quite a bit, 5 = Extremely. Several individual items were chosen from this scale for construct validation.

Crime and Deviance Items

Respondents also self-reported experience with criminal justice processes and engagement in crime and deviance. There were two items for victimization experiences and four items for criminal justice processing experiences (e.g., arrest, probation/parole). Finally, there were 16 items, adapted from the Self-Report Delinquency, General Delinquency Scale (Huizinga & Elliott, 1986), to measure engagement in crime and deviance (e.g., In the last 12 months, how many times have you . . . stolen [or tried to steal] something?).” As described below, we employed the crime and deviance items for validation.

Other Measures Collected

Demographic characteristics collected included Age, Race, Gender, Marital Status, Last Year of Education Completed, Type of Area of Residence (e.g., small-city, rural area), Number of Adults in Household, Number of Children in Household, Employment Status, and Household Income. Descriptive statistics for demographics are reported below but are not analyzed as part of this study.

Data Diagnostics

Planned data diagnostics included several steps to ensure data quality of the present study. First, prior to release of data to the researchers, Qualtrics would scrub the data to ensure that numerical answers were provided where numerical information was requested (e.g., the number 3 was given rather than spelling three). Next, researchers would search for and remove any responses where SWLS or GLAD scores were outside of the possible ranges of 5 to 35 and 48 to 240, respectively, or where cases were missing this data. Normality of data would be confirmed visually using SPSS histogram plots and by interpretation of skewness and kurtosis. Given a sample size greater than 300, normality of both the SWLS and GLAD scores would be assumed if skewness was at or below 2 and if kurtosis was at or below 7, in absolute value (Kim, 2013).

Analysis Plan

The analysis plan was developed using recommendations for scale evaluation from Boateng et al. (2018) as they relate to initial validation of the GLAD with a single sample. Related to their Recommendation 7.1, Factor Analysis will analyze whether the GLAD items load onto factors in a way that is consistent with the 11 domains of the Good Lives Model. The present study will use Confirmatory Factor Analysis as is recommended when constructs are comprised of multiple, linearly related items with a priori fit to the proposed scale (Levine, 2005). DiStefano and Hess’ (2005) suggested that loadings of 0.55 represent a good fit and loadings of 0.45 represent a fair fit for items onto factors. Model fit is generally deemed acceptable with a Comparative Fit Index, or CFI, >0.9 (Bentler & Bonnett, 1980); while Model Fit via Root Mean Square Error of Approximation, or RMSEA, of <0.05 is viewed as close fit and <0.08 as reasonable fit (Browne & Cudek, 1993).

Based on Recommendation 7.4, GLAD scores will be calculated using raw and unweighted data. Then, both individual domain and overall GLAD scores will be subjected to reliability testing using Cronbach’s Alpha (Recommendation 8.1). Consistent with other researchers (Taber, 2018), an alpha of .7 or higher will be considered demonstration of acceptable reliability.

To test construct validity (Recommendation 9.2), the overall GLAD scores will be subjected to correlation tests with the Satisfaction with Life Scale (Diener et al., 1985). A minimum Pearson r between +.5 and +.69 will be considered a moderate association between the GLAD and SWLS, while a score ≥+.7 will be considered a strong association (Akoglu, 2018; Schober et al., 2018). Because both the GLAD and SWLS are designed such that higher scores reflect greater perceived satisfaction with life, a negative Pearson r will be considered failed concurrent validity against the SWLS. Once the GLAD is correlated against the SWLS, separate correlations will be run between the individual domains and selected individual items in the PANAS and Strain Scale. Items will be selected based on how they theoretically correspond to the domains. Selection will include items with both positive and negative theoretical relationships.

Finally, an independent samples t-test will determine if there is a difference in mean GLAD scores between respondents who reported engagement in crime and deviance and those who did not. Positive psychology models like the GLM operate on the hypothesis that higher satisfaction with life can bring about lower engagement in crime and deviance. Thus, the hypothesis of the concurrent validity test will be that those who do not engage in crime and deviance will have higher perceptions of satisfaction with life as indicated by their GLAD scores.

All tests will be conducted using IBM SPSS 28 and Amos 28. The analysis plan was not preregistered.

Transparency and Openness

All data and methods appearing in this study are original to the authors; where instruments of others are incorporated proper citations are given in the manuscript. Raw data are available by making a request to the first author. SPSS syntax for reproducing the analyses is available by making a request to the first author. Research materials are available in the public domain and/or by making a request to the first author. Sample size calculations, data exclusions, manipulations, and all study measures are all reported herein. The study was not preregistered. The analysis plan was not preregistered.

Results

Descriptive Statistics

Sample Characteristics

A total of 2,246 people began the electronic survey between September and November 2021. Of these, 57 (2.5%) were removed during data scrubbing because their item responses were inconsistent with the requested type of response. Six hundred eighty-one people either failed attention checks or did not complete the survey (30.3%). A total of 24 respondents had missing SWLS (n = 2) and/or GLAD (n = 22) data and were removed from the final GLAD analyses. Thus, 1,484 (66.1% completion rate) respondents are included in the final analyses here. Table 3 offers descriptive statistics for the sample and their targeted quotas. Quotas were determined based on the estimated representation of each demographic in the 2020 U.S. general population.

Table 3.

Demographic Descriptive Statistics.

	Targeted quota (%)	Sample (N/Percent)
Gender
Female	51	780/51.8%
Male	49	726/48.2%
Age
18–34	30.5	482/32%
35–54	34.4	515/34.2%
55+	35.2	509/33.8%
		M = 46.158, SD = 17.939, Range = 18–87
Education
Some high school or less	10.9	165/11%
HS Diploma/GED	40.8	599/39.8%
Some college (no degree)	10	153/10.2%
Associate degree	18.3	280/18.6%
Bachelor degree	10	155/10.3%
Graduate degree	10	153/10.2%
Race
Non-Hispanic White	62.3	922/61.2%
Non-Hispanic Black	12.4	188/12.5%
Hispanic	17.3	271/18.0%
Asian	5.4	84/5.6%
Other race	2.6	41/2.7%
Household income
$0–<$50,000 USD	40.03	648/43%
$50,000–<$100,000 USD	32.56	512/34%
>$100,000 USD	27.41	345/22.9%

Note. SD = standard deviation.

GLAD and SWLS Characteristics

Descriptive statistics for the SWLS, the original 48 GLAD items, and the final 45 GLAD items are presented in Table 4. Visual histogram analysis and review of the skewness and kurtosis revealed normal distributions for all measures.

Table 4.

GLAD and SWLS Descriptive Statistics.

	SWLS	GLAD	GLAD final
N
Valid	1,504	1,484	1,484
Missing	2	22	22
Mean	21.81	191.5708	179.0620
Std. Error of mean	0.188	0.77529	0.72369
Median	23.00	195.0000	182.00
Mode	23	212.00^a	195.00
SD	7.299	29.86636	27.87844
Variance	53.279	892.000	777.207
Skewness	−0.394	−0.544	−0.513
Std. Error of Skewness	0.063	0.064	0.064
Kurtosis	−0.532	−0.210	−0.252
Std. Error of Kurtosis	0.126	0.127	0.127
Range	30	170.00	160.00
Minimum	5	70.00	65
Maximum	35	240.00	225

Note. a = mixed modes.

Validation of the GLAD

Exploratory Factor Analysis

Principal Components analysis on the initial 48 GLAD items revealed nine factors with Eigenvalues above 1.0. These nine factors accounted for 64.932% of the variance in GLAD scores. Table 5 shows the results of the Principal Components analysis for these nine factors.

Table 5.

Principal Components Analysis—Unrotated.

Total variance explained
Component	Initial eigenvalues			Extraction sums of squared loadings
Component	Total	% of Variance	Cumulative (%)	Total	% of Variance	Cumulative (%)
1	16.505	34.385	34.385	16.505	34.385	34.385
2	2.836	5.909	40.294	2.836	5.909	40.294
3	2.427	5.056	45.350	2.427	5.056	45.350
4	2.082	4.338	49.688	2.082	4.338	49.688
5	1.892	3.942	53.630	1.892	3.942	53.630
6	1.538	3.204	56.834	1.538	3.204	56.834
7	1.454	3.029	59.863	1.454	3.029	59.863
8	1.287	2.681	62.543	1.287	2.681	62.543
9	1.147	2.389	64.932	1.147	2.389	64.932

Note. Extraction method: Principal component analysis.

With parameters set to nine factors, principal axis extraction with varimax rotations indicated removal of three items (Knowledge 4, Pleasure 3, and Pleasure 4) as they did not load with other items from their respective domains. Kaiser-Meyer-Olkin sampling adequacy (0.954) and Bartlett’s test of sphericity (χ² = 43467.987, p < .001) indicated the data were suitable for factor analysis. Table 6 shows factor loadings for the 45 GLAD items rotated after removal of the three items noted above.

Table 6.

Factor Loadings—Principal Axis Factoring (100 Iterations)—Varimax Rotation.

	Component
	1	2	3	4	5	6	7	8	9
Agency_1					.629
Agency_2					.544
Agency_3					.482
Agency_4					.519
Agency_5					.585
IP_1		.657
IP_2		.767
IP_3		.700
IP_4		.718
Know_1									.588
Know_2									.538
Know_3									.557
Know_5									.399
Life_1						.708
Life_2						.464
Life_3						.609
Life_4						.666
Life_5						.523
Spirit_1								.673
Spirit_2								.701
Spirit_3								.532
Create_1							.633
Create_2							.758
Create_3							.657
Pleasure_1		.437
Pleasure_2		.441
Pleasure_5		.393
Commun_1	.417
Commun_2	.619
Commun_3	.590
Commun_4	.613
Relate_1	.672
Relate_2	.669
Relate_3	.615
Relate_4	.627
Work_1			.652
Work_2			.787
Work_3			.786
Work_4			.647
Work_5			.585
Work_6			.557
Play_1				.815
Play_2				.854
Play_3				.828
Play_4				.698
Cronbach α (items)	.877 (8)	.874 (7)	.877 (6)	.925 (4)	.789 (5)	.811 (5)	.837 (3)	.865 (3)	.814 (4)

Note. Extraction method: Principal axis factoring. Rotation method: Varimax.

Rotation converged in 100 iterations.

Factor loadings also found that the items for Inner Peace combined with those for Pleasure and that the items for Community combined with those for Relatedness. All remaining items clustered with other items in their a priori domains.² Seven items, including all items for Pleasure, loaded onto their respective factors less than 0.5. In addition to the three items noted above, these seven items were removed from a conservative model of the GLAD.

Confirmatory Factor Analysis of the GLAD

Confirmatory factor analysis performed on two models emerging from exploratory factor analysis produced similar results in Amos. The full model consisted of the 45 items remaining after removal of the three items that failed to cluster with their domains (Knowledge 4, Pleasure 3, and Pleasure 4). Both models allowed latent factors to covary. Figure 2 shows the path of the full model.

Figure 2.

Confirmatory factor model for full GLAD model.

The conservative model consisted of the 38 items that loaded 0.5 or greater. Figure 3 shows the path of the conservative model.

Figure 3.

Confirmatory factor model for conservative GLAD model.

Both models demonstrated acceptable data, considering the large sample size. In the full model, χ² = 6201.990, p = .000 with a comparative fit index (CFI) = 0.866, a root mean square residual (RMR) = 0.070 and a Root Mean Square Error of Approximation (RMSEA) = 0.063. In the conservative model, χ² = 4124.796, p = .000 with a CFI = 0.895, RMR = 0.062, and RMSEA = 0.061.

In an effort to disconfirm the subdomain structure of the GLAD, a unidimensional model consisting of all 45 items of the full model was run. The data fit the unidimensional model poorly (χ² = 18745.743, p = .000; CFI = 0.551; RMR = 0.108; RMSEA = 0.113). Because of the similar fits between the full and conservative models, results from the full model of 45 items³ are reported hereafter.

Construct Replicability of the GLAD

To assess the replicability of the GLAD constructs, H values were analyzed. According to its developers (Hancock & Mueller, 2000), H values 0.7 or above indicate a well-defined and stable latent variable. Some more recent authors suggest H values should be greater than 0.8 (Rodriquez et al., 2016) while others note that measures such as H sometimes have an acceptable lower bound of 0.5 (Schmitt et al., 2018). As shown in Table 7, six of the GLAD domains met the criteria for 0.7 or higher, four met the bounds of 0.8 or higher, and all domains met the lower bound of 0.5 or higher.

Table 7.

H Values of GLAD Domains.

Domain	H value
Agency	0.69
Inner peace and pleasure	0.83
Knowledge	0.61
Life	0.75
Spirituality	0.69
Creativity	0.74
Community and relatedness	0.83
Work	0.85
Play	0.89

Reliability Testing of the GLAD

Internal consistency of the GLAD domains was assessed via Cronbach’s alpha. As shown in Table 8, all domains achieved alphas above .7, demonstrating acceptable internal consistency.

Table 8.

Cronbach’s Alphas of GLAD Domains.

Domain	Alpha
Agency	.789
Inner peace and pleasure	.874
Knowledge	.814
Life	.811
Spirituality	.865
Creativity	.837
Community and relatedness	.877
Work	.877
Play	.925
GLAD all domains	.862

Inter-item correlations for the GLAD domains revealed moderate and significant (p < .001) correlations between all domains, with the exceptions of Creativity and Play which demonstrated weaker, but still significant (p < .001), correlations with the other domains. Table 9 shows the inter-item correlation matrix between all domains.

Table 9.

Inter-Item Correlation Matrix.

	Agency	Inner peace and pleasure	Knowledge	Life	Spirituality	Creativity	Community and relatedness	Work	Play
Agency	1.000
Inner peace and pleasure	.498	1.000
Knowledge	.586	.574	1.000
Life	.455	.527	.529	1.000
Spirituality	.492	.579	.517	.485	1.000
Creativity	.347	.352	.353	.366	.418	1.000
Community and relatedness	.501	.541	.518	.467	.577	.437	1.000
Work	.387	.472	.448	.457	.452	.452	.472	1.000
Play	.318	.365	.388	.321	.403	.467	.475	.383	1.000

Note. All correlations significant, p < .001.

Construct Validity of the GLAD

Correlation to Satisfaction With Life Scale

Before construct validity testing, the Satisfaction with Life Scale (SWLS) items were analyzed for validity and reliability. In Confirmatory Factor Analysis, the five SWLS items loaded onto one factor with an Eigenvalue greater than 1 (Eigenvalue = 3.579). This factor explained 71.580% of variance. All Communalities were greater than 0.5. A clear elbow was evident at Factor 2 in the SWLS Scree Plot. The five items of the SWLS achieved a Cronbach’s Alpha score of .896.

Using raw scores for both the GLAD and SWLS revealed a significant Pearson Correlation of .610 (p < .01). This r demonstrates moderate, positive association between the SWLS and the GLAD. Thus, the GLAD demonstrates acceptable concurrent validity for overall satisfaction with life. It is noted that while concurrence between the SWLS and the GLAD denotes some agreement between the SWLS and GLAD on overall perceptions of satisfaction with life, it does not necessarily indicate a validation of the GLAD subscales.

Concurrent Validity of Domains

In an effort to validate the latent variables of the domains, correlations were run against a set of items chosen from the PANAS and Strain instruments. For each domain, PANAS and Strain items that best reflected the underlying construct of the domain were selected. For example, the construct underlying Inner Peace and Pleasure is an ability to enjoy one’s day without interference from obstructive emotions. Thus, PANAS items of “Nervous” and “Enthusiastic” were chosen, along with Strain items for “Depressed” and “Stressed.” Here, it was hypothesized that “Nervous,” “Stressed,” “Depressed” would have negative correlations with Inner Peace and Pleasure, while “Enthusiastic” would have a positive correlation. As shown in Table 10, all of the chosen PANAS and Strain items had significant correlations in directions expected of higher satisfaction within each domain. These results lend credence to the constructs within each GLAD domain.

Table 10.

Domain Correlations With Selected PANAS and Strain Items.

Domain	PANAS and strain items	Correlations
Agency	Indicate the way you have felt this way over the last 30 days: Strong	.319**
	Indicate the way you have felt this way over the last 30 days: Guilty	−.317**
	Indicate the way you have felt this way over the last 30 days: Inspired	.266**
	Indicate the way you have felt this way over the last 30 days: Jittery	−.235**
Inner peace + pleasure	When I am unable to reach my goals, I feel: Depressed	−561**
	When I am unable to reach my goals, I feel: Stressed	−.520**
	When bad things happen to me, I feel: Depressed	−.538**
	When bad things happen to me, I feel: Stressed	−.522**
	Indicate the way you have felt this way over the last 30 days: Nervous	−.430**
	Indicate the way you have felt this way over the last 30 days: Enthusiastic	−.415**
Knowledge	When I am unable to reach my goals, I feel: Insecure	−.351**
	When I am unable to reach my goals, I feel: Worthless	−.344**
	Indicate the way you have felt this way over the last 30 days: Interested	.333***
	Indicate the way you have felt this way over the last 30 days: Nervous	−.258**
Life	In most ways, my life is close to my ideal	.373**
	The conditions of my life are excellent	.415**
	When I am unable to reach my goals, I feel: Angry	−.174**
	Indicate the way you have felt this way over the last 30 days: Active	.385**
	Indicate the way you have felt this way over the last 30 days: Afraid	−.244**
Spirituality	If I could live my life over, I would change nothing	.271**
	When I am unable to reach my goals, I feel: insecure	−.336**
	When bad things happen to me, I feel: insecure	−.323**
	Indicate the way you have felt this way over the last 30 days: Inspired	.389**
	Indicate the way you have felt this way over the last 30 days: Ashamed	−.262**
Creativity	Indicate the way you have felt this way over the last 30 days: Excited	.316**
	Indicate the way you have felt this way over the last 30 days: Proud	.332**
	Indicate the way you have felt this way over the last 30 days: Inspired	.374**
Community + relatedness	Indicate the way you have felt this way over the last 30 days: Active	.354**
	Indicate the way you have felt this way over the last 30 days: Interested	.368**
	Indicate the way you have felt this way over the last 30 days: Ashamed	−.272**
	Indicate the way you have felt this way over the last 30 days: Distressed	−.231**
	When bad things happen to me, I feel: Resentful	−.256**
Work	When I am unable to reach my goals, I feel: Worthless	−.270**
	Indicate the way you have felt this way over the last 30 days: Proud	.388**
	Indicate the way you have felt this way over the last 30 days: Ashamed	−.141**
	When I am unable to reach my goals, I feel: Resentful	−.154**
Play	When I am unable to reach my goals, I feel: Worthless	−.217**
	When bad things happen to me, I feel: Resentful	−.170**
	Indicate the way you have felt this way over the last 30 days: Determined	.298**
	Indicate the way you have felt this way over the last 30 days: Proud	.317**

p < .01 *** p < .001

Criterion-Related Validity of the GLAD

The predictive ability of the GLAD was assessed via an Independent Samples T-test where mean GLAD scores were compared between those who self-reported engagement in crime and deviance and those who did not report such engagement. It was hypothesized that respondents who did not report engaging in crime and deviance would have significantly higher GLAD scores. The hypothesis was supported (t = 4.360, p < .001) with the mean difference (8.15737) representing 19.16% of the range in GLAD scores.

Discussion

The Good Lives Model Offender Rehabilitation (GLM) is a positive psychology-based intervention that is seeing increased, global use with people who offend, and with people who are at risk of offending. Its foundational theory is that improving the circumstances within domains important to individuals can improve their satisfaction with life, and subsequently decrease their chances of engaging in crime and deviance. However, the GLM does not yet have an empirically sound instrument to assess clients’ perceptions of satisfaction in important domains. This study sought to fill that void by undertaking an initial validation of the Good Lives Assessment of Domains (GLAD). Data for analyses came from 1,484 responses to a national survey of American adults, with quotas set to approximate the population on five demographics. Tests of constructs and internal consistency, as well as a correlation to a global measure of life satisfaction, found that the GLAD is a reliable and valid measure of life satisfaction for 9 factors, and where these 9 factors cover all 11 domains of the GLM. As such, the GLAD can be useful to several ends.

Clinical and Research Uses of the GLAD

Because the 45 items of the GLAD are built from a model of satisfaction with life (SWL) that is already in clinical use in various parts of the world, the GLAD allows immediately for deeper investigations into respondents’ perceptions of domain satisfaction than does the sole existing measure of GLM global domain satisfaction. The Common Good Lives Questionnaire (CGLQ) only assesses whether a respondent feels the domain is important and how satisfied the respondent is with any domain overall. Probing further into domain satisfaction will allow clinicians and clients to better assess domain satisfaction, target areas for improvements, and evaluate progress. For instance, instead of knowing simply that a client is dissatisfied with Inner Peace from the CGLQ, the GLAD responses might reveal both that the client is dissatisfied in Inner Peace and Pleasure and that the dissatisfaction arises primarily from overwhelming emotions (GLAD item IP2) and disturbed thoughts (GLAD item IP3). Work can then begin to identify the source of overwhelming emotions and disturbing thoughts, and on overcoming their impact to domain satisfaction. Returning to the GLAD several months into treatment can help determine if the client’s satisfaction with Inner Peace and Pleasure is resolving. Because the Good Lives Model is being used with both people who offend and people who don’t offend, more empirically sound clinical work can now be carried out forthwith by practitioners serving clients across multiple fields, including but not limited to psychology, criminal justice, community mental health, and school counseling.

Improved clinical work with people is not the only benefit of the GLAD. SWL has been associated with resilience (Fredrickson, 2001; Tugade & Fredrickson, 2004), prevention from bullying (Estevez et al., 2009), desistance from substance use (Laudet et al., 2009), and disengagement from crime (MacDonald et al., 2005; Olson et al., 2020). It is feasible that universal education and targeted interventions focused in SWL could help prevent deviance in general and help change the behavioral course for people who have begun to experiment in deviance. For example, elementary, secondary, and higher education institutions could augment or replace current programs to drive a focus on SWL. Rather than primarily addressing the physical and mental harms of bullying or using illicit substances, universal and targeted prevention programs could teach a model of SWL like the GLM and focus on the benefits of achieving SWL through a sense of Community and Inner Peace. Subsequent discussion can then work to build plans to achieve satisfaction in the domains important to participants and address how behaviors like bullying or substance use can reduce satisfaction in those domains. Here again, the GLAD can help with targeting important domains and offering a way for participants to visualize improvements to their satisfaction in those domains.

The GLAD can also improve various research efforts in SWL. Having a quantitative measure for satisfaction in the GLM domains allows for program evaluation and comparison. Using participant data collectively, evaluators can work to determine if SWL improvements are likely the result of participation in any one or more of the GLM implementations. The GLAD will also allow for comparison of program effectiveness across different types of implementations (e.g., community or institutional), populations (e.g., youths vs. adults; mental health or criminal justice), geographies (e.g., U.S. vs. Australia), and cultures (e.g., collectivist Eastern vs. capitalist Western). As researchers learn where and how SWL programs like the GLM are most effective, program refinements can be made to improve their effectiveness and their reach.

Uses for the GLAD span clinical work, preventative efforts, and research into SWL. Building knowledge about the universality and potential benefits of models like the GLM will deepen the understanding of positive psychology. Demonstrating effectiveness of intervention and educational programs built on such models can help make positive psychology more attractive to more people.

Limitations and Future Directions

As clinicians and researchers begin their use of the GLAD, their efforts can help reduce or eliminate several limitations of the present study. First, the GLAD validated nine factors, where two of the factors each combine two GLM domains (Inner Peace & Pleasure, and Community & Relatedness). While the GLAD assesses all 11 GLM domains, future work should discern whether Inner Peace/Pleasure and Community/Relatedness are standalone domains. If any of them are unique domains, refine items to assess their individual achievement.

The reliability and validity of the GLAD discussed herein is based on a single convenience sample of U.S. adults. While quotas within the sampling methods approximated that population on five demographics, it is not a random and representative sample of the general population. Care should be taken when generalizing these results, especially to populations of youths. Additionally, cross-section samples do not permit full validation testing, such as comparisons among and between clinical, offender, adult, or youth groups and repeated testing among the same samples. As participant data are collected with the GLAD, future studies can aid validation efforts by undertaking such longitudinal and cross-group analyses. If data are specific to identifiable populations, or are collected randomly across populations, then the generalizability of the GLAD can be assessed more accurately.

While some respondents did report engagement in crime and deviance, this study did not target a population of people who offend. Since the GLM was created to intervene in the lives of people who offend, future studies should target people from this population in order to assess both the validity of the GLAD in that population and the utility of positive psychology ideology to help prevent or reduce crime. Random, longitudinal studies employing the GLM and GLAD with people in- and at-risk can help determine whether increases to SWL are an efficient and effective strategy to reducing crime.

Finally, concurrent validity of the GLAD was assessed via correlation with the Satisfaction with Life Scale, or SWLS (Diener et al., 1985). The SWLS is a global measure of SWL. The GLAD and SWLS scores achieved moderate associations between each other. This suggests that the GLAD is also a good indicator of global SWL. Construct validity suggests that the GLAD items assess 9 distinct factors in a way consistent with the 11 GLM domains. Those domains are similar to domains posited in other models of SWL (cf. Table 1 above). Though they are not necessarily tied to clinical interventions or psychoeducational prevention programs, other assessments of domain satisfaction do exist, like the Happiness Index (Musikanski et al., 2017). To further validate the GLAD, and to confirm the similarity and universality of life domains in SWL models, future research should collect data from indices of other SWL models for correlational analyses. A fully validated GLAD that assesses domains consistent across models of SWL can be relied upon to help people and societies build their own versions of a good life.

Conclusion

Both Aristotle (1999) and the Dalai Lama (Lama & Cutler, 1998) wrote that the goal of humans is to seek and attain happiness. Frankl (2006) believed that happiness could even be sought and found in the depths of despair and suffering. Building on those philosophical perspectives, Seligman (1999) suggested a science that would lead people to achieving happiness. The initial validity of the Good Lives Assessment of Domains allows people to take one more step toward achieving happiness. It also offers a little more hope that future work within positive psychology can help build the kinds of “. . .positive individuals, flourishing communities, and just society” that Seligman (1999, p. 560) saw.

Supplemental Material

sj-docx-1-ijo-10.1177_0306624X241240711 – Supplemental material for Validation of the Good Lives Assessment of Domains in an Adult U.S. Sample

Supplemental material, sj-docx-1-ijo-10.1177_0306624X241240711 for Validation of the Good Lives Assessment of Domains in an Adult U.S. Sample by Jeremy Olson, Dennis Giever and Rebecca S. Sarver in International Journal of Offender Therapy and Comparative Criminology

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a grant from the George Bierly Endowment of the Chancellor Endowment Funds at Penn State Wilkes-Barre, CEF #128.

ORCID iD

Jeremy Olson

Data Availability

Data for this study is available by contact with the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

Notes

References

Aishvarya

Maniam

Karuthan

Sidi

Ruzyanei

Jaafar

Oei

(2014). Psychometric properties and validation of the Satisfaction With Life Scale in psychiatric and medical outpatients in Malaysia. Comprehensive Psychiatry, 55(1), S107–S113.

Akoglu

(2018). Users’ guide to correclation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93. https://doi.org/10.1016/j.tjem.2018.08.001

Aristotle. (1999). Nicomachean Ethics ( Ross

W. D.

, Trans.). Batoche Books.

Barendregt

C. S.

Van der Laan

A. M.

Bongers

I. L.

Van Nieuwenhuizen

(2018). Quality of life, delinquency, and psychosocial functioning of adolescents in secure residential care: Testing two assumptions of the Good lives Model. Child and Adolescent Psychiatry and Mental Health, 12(1), 1–10. https://doi.org/10.1186/s13034-017-0209-9

Bentler

P. M.

Bonnett

D. G.

(1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588

Boateng

G. O.

Nellands

T. B.

Frongillo

E. A.

Melgar-Quinonez

H. R.

Young

S. L.

(2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Public Health, 6(149), 1–18. https://doi.org/10.3389/fpubh.2018.00149

Broidy

L. M.

(2001). A test of general strain theory. Criminology, 39(1), 9–35.

Browne

M. W.

Cudek

(1993). Alternate ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp. 136–162). Sage.

Campbell

Converse

P. E.

Rodgers

W. L.

(1976). The quality of American life: Perceptions, evaluations and satisfactions. Russell Sage Foundation.

10.

Damme

L. V.

Hoeve

Vermeiren

Vanderplasschen

Colins

O. F.

(2016). Quality of life in relation to future mental health problems and offending: Testing the Good Lives Model among detained girls. Law and Human Behavior, 40(3), 285–294. https://doi.org/10.1037/lhb0000177

11.

Diener

Emmons

R. A.

Larsen

R. J.

Griffin

(1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75.

12.

DiStefano

Hess

(2005). Using confirmatory factor analysis for construct validation: An empirical review. Journal of Psychoeducational Assessment, 23(3), 225–241. https://doi.org/10.1177/073428290502300303

13.

Estevez

Murgui

Musitu

(2009). Psychological adjustment in bullies and victims of school violence. European Journal of Psychology of Education, 24(4), 473–483.

14.

Frankl

V. E.

(2006). Man’s search for meaning. Beacon Press.

15.

Fredrickson

B. L.

(2001). The role of positive emotions in Positive Psychology: The Broaden-and-Build Theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037///0003-066X.56.3.218

16.

Hancock

G. R.

Mueller

R. O.

(2000). Rethinking construct reliability within latent variable systems. In Cudek

duToit

S. H. C.

Sorbom

D. F.

(Eds.), Structural equation modeling: Present and future (pp. 195–216). Scientific Software.

17.

Harper

C. A.

Lievesley

Blagden

Akerman

Winder

Baumgartner

(2021). Development and validation of the Good Lives Questionnaire. Psychology, Crime & Law, 27(7), 678–703. https://doi.org/10.1080/1068316X.2020.1849695

18.

Harris

D. A.

Pedneault

Willis

(2019). The pursuit of primary human goods in men desisting from sexual offending. Sexual Abuse, 31(2), 197–219.

19.

Huizinga

Elliott

D. S.

(1986). Reassessing the reliability and validity of self-report delinquency measures. Journal of Quantitative Criminology, 2(4), 293–327.

20.

Kim

H.-Y.

(2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. REstorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52

21.

Koran

(2020). Indicators per factor in Confirmatory Factor Analysis: More is not always better. Structural Equation Modeling: A Multidisciplinary Journal, 27(5), 765–772. https://doi.org/10.1080/10705511.2019.1706527

22.

Lama

Cutler

H. C.

(1998). The art of happiness: A handbook for living. New York: Riverhead Books.

23.

Langlands

R. L.

Ward

Gilchrist

(2009). Applying the Good Lives Model to male perpetrators of domestic violence. Behaviour Change, 26(2), 113–129.

24.

Laudet

A. B.

Becker

J. B.

White

W. L.

(2009). Don’t wanna go through that madness no more: Quality of life satisfaction as predictor of sustained remission from illicit drug misuse. Substance Use & Misuse, 44, 227–252.

25.

Levine

(2005). Confirmatory factor analysis and scale validation in communication research. Communication Research Reports, 22(4), 335–338. https://doi.org/10.1080/00036810500317730

26.

MacDonald

J. M.

Piquero

A. R.

Valois

R. F.

Zullig

K. J.

(2005). The relationship between life satisfactions, risk-taking behaviors, and youth violence. Journal of Interpersonal Violence, 20(11), 1495-1518.

27.

Mallion

J. S.

Wood

J. L.

Mallion

(2020). Systematic review of “Good Lives” assumptions and interventions. Aggression and Violent Behavior, 55, 1–17. https://doi.org/10.1016/j.avb.2020.101510

28.

Martikainen

(2009). The many faces of life satisfaction among Finnish young adults. Journal of Happiness Studies, 10, 721–737.

29.

Mears

D. P.

(2010). American criminal justice policy: An evaluation approach to increasing accountability and effectiveness. Cambridge University Press.

30.

Musikanski

Cloutier

Bejarano

Briggs

Colbert

Strasser

Russell

(2017). Happiness Index Methodology. Journal of Social Change, 9(1), 4–31. https://doi.org/10.5590/JOSC.2017.09.1.02

31.

Olson

Martin

R. L.

Connell

N. M.

(2020). Satisfaction with life and crime: Testing the link. Journal of Psychology, Crime, and Law, 27(7), 631–655. https://doi.org/10.1080/1068316X.2020.1849692

32.

Olson

Sarver

R. S.

Labishak

J. M.

(2016). Getting my Good Life: Staff training manual. Unpublished

33.

Pavot

Diener

(2008). The Satisfaction with Life Scale and the emerging construct of life satisfaction. The Journal of Positive Psychology, 3(2), 137–152.

34.

Prescott

D. S.

(2013). The Good Lives Model (GLM) in theory and practice. UNAFEI.

35.

Ramm

D. R.

(1996). Clinically formulated principles of morality. New Ideas in Psychology, 14(3), 237–256.

36.

Rodriquez

Reise

S. P.

Haviland

M. K.

(2016). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98(3), 223–237. https://doi.org/10.1080/00223891.2015.1089249

37.

Schmitt

T. A.

Sass

D. A.

Chappelle

Thompson

(2018). Selecting the “best” factor structure and moving measurement validation forward: An illustration. Journal of Personality Assessment, 100(4), 345–362. https://doi.org/10.1080/00223891.2018.1449116

38.

Schober

Boer

Schwarte

(2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864

39.

Seligman

M. E. P.

(1999). The president’s address (in the annual report). American Psychologist, 54(8), 559–562.

40.

Sheldon

K. M.

Elliot

A. J.

Kim

Kasser

(2001). What is satisfying about satisfying events?: Testing 10 candidate psychological needs. Journal of Personality and Social Psychology, 80(2), 325–339. https://doi.org/10.1037//O022-3514.80.2.325

41.

Siegert

R. D.

Ward

Levack

W. M. M.

McPherson

K. M.

(2007). A Good Lives Model of clinical and community rehabilitation. Disability and Rehabilitation, 29(20-21), 1604-1615.

42.

Slocum-Gori

S. L.

Zumbo

B. D.

Michalos

A. C.

Diener

(2009). A note on the dimensionality of Quality of Life scales: An illustration with the Satisfaction with Life Scale (SWLS). Social Indicators Research, 92, 489–496.

43.

Taber

K. S.

(2018). The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1723-1796. https://doi.org/10.1007/s11165-016-9602-2

44.

Tugade

M. M.

Fredrickson

B. L.

(2004). Resilient individuals use positive emotions to bounce back from negative emotional experiences. Journal of Personality and Social Psychology, 86(2), 320–333.

45.

Walgrave

Ward

Zinsstag

(2021). When restorative justice meets the Good Lives Model: Contributing to a criminology of trust. European Journal of Criminology, 18(3), 444–460. https://doi.org/10.1177/1477370819854174

46.

Ward

(2002a). Good lives and the rehabilitation of offenders: Promises and problems. Aggression and Violent Behavior, 7(5), 513–528. https://doi.org/10.1016/S1359-1789(01)00076-3

47.

Ward

(2002b). The management of risk and the design of good lives. Australian Psychologist, 37(3), 172–179. https://doi.org/10.1080/00050060210001706846

48.

Watson

Clark

L. A.

Tellegen

(1988). Development and validation of brief measures positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.

49.

Whitehead

P. R.

Ward

Collie

R. M.

(2007). Time for a change: Applying the Good Lives Model of rehabilitation to a high-risk offender population. International Journal of Offender Therapy and Comparative Criminology, 51(5), 578–598.

50.

Willis

Ward

Levenson

J. S.

(2014). The Good Lives Model (GLM): An evaluation of GLM operationlization in North American treatment programs. Sexual Abuse: A Journal of Research and Treatment, 26(1), 58–81.

51.

Yates

P. M.

Prescott

Ward

(2010). Applying the Good Lives and self-regulation models to sex offender treatment: A practical guide for clinicians. The Safer Society Press.

52.

Yates

P. M.

Prescott

D. S.

(2011). Building a better life: A Good Lives and self-regulation workbook. The Safer Society Press.

53.

Zanon

Bardagi

Layous

Hutz

(2014). Validation of the Satisfaction with Life Scale to Brazilians: Evidences of measurement noninvariance across Brazil and US. Social Indicators Research, 119(1), 443–453.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB