Journal of Consulting and Clinical Psychology 1978, Vol. 46, No. S, 1105-1119
Evaluating Alcoholism Treatment Programs: An Integrated Approach Ruth C. Cronkite and Rudolf H. Moos Department of Psychiatry and Behavioral Sciences Social Ecology Laboratory, Stanford University School of Medicine and Palo Alto Veterans Administration Hospital, Palo Alto, California This article examines the interrelationships among five major sets of variables (social background, intake symptoms, program type, treatment experiences, and perceptions of the environment) that are related to posttreatment functioning of alcoholic patients (alcohol consumption, rating of drinking problem, physical impairment, and occupational functioning). The sample consisted of 429 patients selected from five different treatment programs. The relative importance of each set of variables as predictors of outcome was estimated by constructing block variables, using path analyses, and partitioning the explained variance. The results showed that (a) the combined explanatory power of the programrelated variables is considerably more than would be expected from previous research; (b) the importance of patient background relative to intake symptoms varies with the outcome criterion being used; (c) both the treatment experiences and the patient's perceptions of- the treatment environment are strong predictors of outcome; and (d) a substantial proportion of the explained variance is shared between patient-related and program-related variables, suggesting important patient-program selection and congruence effects. One of the major issues in longitudinal studies of alcoholic patients is assessing the relative importance of patient background and treatment programs in determining outcome. Although contradictory findings have been reported, previous research has generally suggested that patient characteristics at intake are most strongly related to outcome and that treatment programs have little effect once sociodemographic and functioning characteristics at intake are taken into account (Armor, Polich, & Stambul, 1976; The research was supported in part by National Institute for Alcohol Abuse and Alcoholism Grant AA02863 and by Veterans Administration Research Funds. The article is based on a presentation to the Evaluation Research Session, Western Social Science Association meetings, Denver, Colorado, April 1977. Ruth Cronkite is also affiliated with Mills College, Oakland, California. Requests for reprints should be sent to Rudolf Moos, Social Ecology Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305.
Craft, Sheehan, Driggers, & DuBois, 1975; Gerard & Saenger, 1966; Pokorny, Miller, & Cleveland, 19,68; Ruggels, Armor, Polich, Mothershead, & Stephen, 1975). A related issue involves the relative contributions of different types of patient characteristics—in particular, the extent to which outcome is related to social background on the one hand and drinking symptoms at intake on the other. No clear-cut pattern of findings has been reported on the relative importance of social background variables (such as socioeconomic or marital status) compared to intake symptoms (such as alcohol consumption or behavioral or psychological impairment at intake) in predicting outcome (Armor et al., 1976; Craft et al, 1975; Ruggels et al., 1975). Inferences pertaining to both of these issues have been primarily based on examination of the increments in explained variance (Armor et al., 1976; Bromet, Moos, Bliss, & Wuthmann, 1977; Craft et al., 1975; Ruggels et al., 1975). This method is asymmetric
In the public domain
1105
1106
RUTH C. CRONKITE AND RUDOLF H. MOOS
in that it attributes variance shared by two or more sets of variables to the set that is entered first in the regression analysis (such as patient background variables), and thus may overestimate the contribution of the first set by crediting it with both its unique and shared variance. In contrast, the increment in explained variance attributed to the set of variables added last (such as programrelated variables) represents only the explained variance that is unique to that set of variables, not the variance that is shared with variables that have been entered earlier. Consequently, previous inferences about the relative effects of program-related variables, social background characteristics, and intake symptoms on outcome may be misleading.1 Such inferences have important policy implications for alcoholism treatment. For example, if the unique variance attributed to program-related variables is small, and the variance that is shaied with patient background variables is attributed only to background characteristics, then researchers may conclude that treatment effects are negligible and thus recommend less expensive and more uniform treatment programs. Another unresolved question raised by longitudinal studies of alcoholics is the role of different types of program-related variables in predicting outcome. The relationship of treatment variations to outcome has been approached in several ways. Armor et al. (1976) and Kissin, Platz, and Su (1970) focused on the type and amount of treatment both within and across programs. Bromet et al. (1977) examined the effect of level of participation on outcome in several different treatment programs. In addition to studying the effects of a variety of treatment experiences and treatment programs, Bromet, Moos, and Bliss (1976) have focused on another dimension of program-related variables, a patient's perceptions of the treatment environment. This approach is based on research which suggests that the social environments of psychiatric and correctional programs may be important factors in influencing outcome (Ellsworth, Maroney, Klett, Gordon, & Gunn, 1971; Moos, 1974b). Each of these aspects of treatment variations has been studied separately, but their effects relative
to each other have not been examined within a single study. The purpose of this article is to develop an integrated approach to studying alcoholism treatment programs that will facilitate clarification of the following issues: (a) What are the interrelationships among patient social background variables, intake symptoms, treatment programs, treatment experiences, perceptions of the treatment environment, and outcome? (b) What is the relative importance of program-related variables compared to patient background characteristics in predicting outcome? (c) What is the relative importance of a patient's social background compared to intake symptoms in predicting outcome? (d) What are the roles of different aspects of treatment variations in predicting outcome? A Model of Treatment Outcome In most longitudinal studies of alcoholic patients, the dependent variables are one or more outcome criteria related to posttreatment functioning, such as rehospitalization, alcohol consumption, and occupational, physical, and psychosocial functioning. The independent variables vary across studies, depending on their focus. From reviewing previous research, the independent variables can be divided into "blocks," labeled Blocks 1, 2, 3, 4, and 5. Block 1 consists of a set of sociodemographic variables known to be related to drinking patterns, such as age, sex, ethnicity, marital status, and socioeconomic status. Block 2 includes a patient's drinking symptoms at intake, more specifically, the type and severity of alcoholism-related characteristics, such as alcohol consumption, drinking patterns, physical impairment, and behavioral impairment. Blocks 3, 4, and 5 refer to program-related variables. Block 3 includes the type of program; Block 4 includes the amount of various treatment experiences, such as therapy sessions, Alcoholics Anonymous (AA) meetings, antabuse, and !See Newton and Spurrell (1967a, 1967b) and Mood (1971) for a complete technical discussion of partitioning the sum of squares in regression analysis and Coleman (1975) for a discussion of this issue when applied to school effects.
EVALUATING ALCOHOLISM TREATMENT
1107
so forth, in which the patient participates; cluded in the model (i.e., pp B3 ) are relatively relatively strong effects (occupational funcclose and large (ranging from .434 to .552), tioning and physical concomitants) are those indicating that the major determinant of a that were most highly correlated with social patient's perceptions is the program that the background. patient is in. Determinants of treatment experiences. The only variable hypothesized to have a di- Relationship of Patient Background and rect effect on treatment experiences is the pro- Program-Related Variables to Outcome gram type, and, in fact, the estimates of tihe effect of the program (pis) are relatively large Table 2 displays the direct, indirect, and (and statistically significant), ranging from total effects of each o'f the five variables hy.460 to .689 across all four models (see Table pothesized to affect outcome. The results show
1112
RUTH C. CRONKITE AND RUDOLF H. MOOS
Alcohol Consumption at FIF
(Alcohol Consumption at BIF) Figure 2. Estimation of path coefficients for alcohol consumption. (FIF = follow-up information form; BIF = background information form; B = background; I = intake; P = program; T — treatment experiences; E = treatment environment.)
some clear-cut consistencies across the models. The estimates of p81 (three of which are statistically significant) suggest that the higher the level of social background, the less severe the alcoholism-related symptoms are at fol-
low-up. Similarly, there is a relatively strong association between a patient's intake symptoms and the corresponding outcome criterion at follow-up, indicated by the estimates of Pus (except for a weak effect in the alcohol
Rating of Drinking Problem of FIF
(Rating of Drinking Problem at BIF) Figure 3. Estimation of path coefficients for rating of drinking problem. (FIF = follow-up information form; BIF = background information form; B — background; I = intake; P = program; T = treatment experiences; E = treatment environment.)
EVALUATING ALCOHOLISM TREATMENT
1113
Physical Concomitants at FIF
(Physical Concomitants at BIF)
Figure 4. Estimation of path coefficients for physical concomitants. (FIF = follow-up information form; BIF - background information form; B = background; I - i n t a k e ; P = program- T = treatment experiences; E = treatment environment.) '
consumption model). In fact, for three of the outcome criteria, the intake symptoms had slightly stronger direct effects than social background. The indirect effects of social background that are mediated by the program-related variables together represent between 11% and 73% ,of their total effect,
whereas the indirect effects of the intake symptom together represent between 7% and 23% of their total effect. These results indicate tlhat in three of the models, a substantial proportion of the total effect of social background is via indirect effects that are shared with the program-related variables. In con-
Occupational Functioning at FIF
(Occupational Functioning at BIF)
Figure 5. Estimation of path coefficients for occupational functioning. (FIF = follow-up information form; BIF = background information form; B = background; I = intake; P = program; T — treatment experiences; E = treatment environment.)
1114
RUTH C. CRONKITE AND RUDOLF H. MOOS
trast, most of the total effect of the intake symptoms is direct (7 7 %-93 %) and thus not shared with the program-related variables. There is a strong total effect of program type on outcome (qos). When the treatment experiences and perceptions of the environment are taken into account, however, the program variable has little direct effect (pes) (i.e., almost all of the total effect of tine program is mediated by the two program-related
variables, shown by the indirect effects, and pospss, in Table 2). Treatment experiences (PM) and perceptions of the environment (p 65 ) are both strongly associated with outcome. For all of the outcome criteria except occupational functioning, the direct effect of the perceptions of the environment (PCS) is surprisingly strong, suggesting that perceptions of the treatment program may be important predic-
Tablc 1 Direct, Indirect, and Total Effects of Patient-Related Block Variables on Program-Related Block Variables Type of intake and outcome criterion used Variable Dependent Program
Treatment experiences
Perceptions of environment
Independent Social background direct & total effect: Psi (qai) (no indirect effects) Corresponding intake symptom Direct & total effect: P32 (qsz) (no indirect effects) Social background Indirect effect via program p 43 p3i (also total effect—no direct effect) (q 4 i) Intake symptom Indirect effect via program: p43p32 (also total effect—no direct effect) (qw) Direct effect PAS
Social Background Direct effect : poi Indirect effect via program: p63psi Total effect: q 6 i (PSI + Psspsi) Intake symptom Indirect effect via program: psapsa (also total effect, q 32 ) (no direct effect) Direct effect :p 6 3
' p < .05 (calculated for direct effects only).
Alcohol consumption
.559*
Rating of drinking problem
.522*
Physical concomitants
Occupational functioning
.463*
.219*
-.150*
-.150*
.015
-.080
.257
.360
.274
.124
.007 .460*
.055 .689*
.089 .592*
.086 .570*
.185*
.104
.122*
.025
-243
.269
.256
.112
.428
.373
.378
.087
.007 .434*
.041 .515*
.083 .552*
-.077 .511*
1115
EVALUATING ALCOHOLISM TREATMENT
Table 2 Direct, Indirect, and Total Effects of Patient-Related Variables and Program-Related Block Variables on Four Outcome Criteria
Effects
Alcohol consumption
Rating of drinking problem
Physical concomitants
Occupational functioning
Social background — pci (B) Corresponding intake symptom— pc2 (I) Program — p63 (P) Treatment experiences— pci (T) Perceptions of environment— pss (E)
-.167* .036 .068 -.295* -.190*
-.061 .171* -.014 -.155* -.283*
-.169* .235* .086 -.165* -.264*
-.171* .265* .015 -.166* -.045
.038 -.035 -.076 -.046
-.007 -.029 -.056 -.076
.040 -.032 -.045 -.067
.003 .001 -.021 -.005
-.013 .015 .022
-.002 .014 .003
Direct
Indirect B via
P — Pe 3 p 3 i E
P66P51
P&T— peipiapai rt o 17 1 CxtL
PC5P53P31
I via P
P63P32
P&T— p 6 (p 4 3 p 3 2 P&E— P66P63P32
.001 -.002 -.001
.001 .009 .012
P via T
P64P43
E
Pc5p53
-.136 -.082
-.107 -.146
-.098 -.146
-.095 -.023
-.286 .047 -.150
-.229 .193 -.267
-.273 .259 -.158
-.193 .284 -.103
Total" B— q 6 l I — qea P— qes
" Within the context of the causal model used here (see Figure 1), the total effects of treatment experiences and perceptions of the environment, q M and q«5, respectively, are the same as their direct effects, pm and PCS. Consequently, they are only listed under the direct effects. The total effects listed here were calculated as the sum of the direct and indirect effects. * p < .05 (calculated for direct effects only).
tors of outcome. However, there is substantial interdependence among the program-related variables; only 0%-27% of the direct effect of treatment experiences and l%-33% o'f the direct effect of perceptions of the environment are independent of all prior variables (social background, intake symptom's, and program type). 7 Unique and Joint Contributions of the Explained Variance Table 3 presents the results of partitioning the explained variance for the four outcome criteria. The first five rows display the unique and shared variance attributed to the patient background variables, and the next six rows show the unique and shared vari-
ance attributed to the program-related variables. The rest of the table displays the explained variance that is shared among combinations of the patient-related and programrelated variables. The pattern of results is similar to those obtained from the path analysis. In general, both social background and the intake symptoms contribute substantially to the explained variance, with the combined contribution of their unique and shared variance ranging from 12% to 61% o'f the explained variance. In three of the models, the unique contributions 7
These percentages were calculated by subtracting out all other paths that are causally prior to the direct path of interest. (See Coleman, 197S, for a more detailed discussion of these procedures.)
RUTH C. CRONKETE AND RUDOLF H. MOOS
1116 Table 3
Partitioning oj Explained Variance for Four Outcome Criteria
Variable Patient related Unique variance Background Intake Shared variance Background and intake Subtotal Proportion of R* Program related Unique variance Program Treatment experiences Perceptions of treatment environment Shared variance Sum of all combinations 1 ' Subtotal Proportion of -K2 Shared variance among background, intake, & program-related variables Sum of all combinations" Proportion of R^ Total R2
Alcohol consumption
Rating of drinking problem
.019" .001
.003 .028"
.019" .047"
.026" .064"
.002
.003 .034" .16
.019" .085" .32
.022" .112" .61
.025"
.000 .012" .048"
.003 .017" .043"
.000 .018" .002
.010" .104"
.047" .107"
.013" .076"
.57
.49
.28
.009 .030" .16
.056"
.075"
.106"
.30
.35
.40
.182
.217
.268
.022" .12
.002
,067«
Physical Occupational concomitants functioning
.041" .23 .183
Note. B = background; I = intake; P = program; T = treatment experiences; and E = perceptions of treatment environment. " The explained variance is greater than 1%. b These combinations are PT, PE, TE, PTE. c These combinations are HP, BT, BE, HPT, BTE, BPE, BITE, IP, IT, IE, IPT, ITE, IPE, IPTE, BIP, BIT, BIE, BIPT, BIPK, BITE, and BIPTE.
of the intake symptoms are stronger than the unique explained variance attributed to social background. When taken together with the path analyses, the findings suggest that the intake symptom has a relatively stronger relationship to outcome than does social background. The total of the unique and shared variance attributed to the program-related variables ranges from 16% to 57
Evaluating Alcoholism Treatment Programs: An Integrated Approach Ruth C. Cronkite and Rudolf H. Moos Department of Psychiatry and Behavioral Sciences Social Ecology Laboratory, Stanford University School of Medicine and Palo Alto Veterans Administration Hospital, Palo Alto, California This article examines the interrelationships among five major sets of variables (social background, intake symptoms, program type, treatment experiences, and perceptions of the environment) that are related to posttreatment functioning of alcoholic patients (alcohol consumption, rating of drinking problem, physical impairment, and occupational functioning). The sample consisted of 429 patients selected from five different treatment programs. The relative importance of each set of variables as predictors of outcome was estimated by constructing block variables, using path analyses, and partitioning the explained variance. The results showed that (a) the combined explanatory power of the programrelated variables is considerably more than would be expected from previous research; (b) the importance of patient background relative to intake symptoms varies with the outcome criterion being used; (c) both the treatment experiences and the patient's perceptions of- the treatment environment are strong predictors of outcome; and (d) a substantial proportion of the explained variance is shared between patient-related and program-related variables, suggesting important patient-program selection and congruence effects. One of the major issues in longitudinal studies of alcoholic patients is assessing the relative importance of patient background and treatment programs in determining outcome. Although contradictory findings have been reported, previous research has generally suggested that patient characteristics at intake are most strongly related to outcome and that treatment programs have little effect once sociodemographic and functioning characteristics at intake are taken into account (Armor, Polich, & Stambul, 1976; The research was supported in part by National Institute for Alcohol Abuse and Alcoholism Grant AA02863 and by Veterans Administration Research Funds. The article is based on a presentation to the Evaluation Research Session, Western Social Science Association meetings, Denver, Colorado, April 1977. Ruth Cronkite is also affiliated with Mills College, Oakland, California. Requests for reprints should be sent to Rudolf Moos, Social Ecology Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305.
Craft, Sheehan, Driggers, & DuBois, 1975; Gerard & Saenger, 1966; Pokorny, Miller, & Cleveland, 19,68; Ruggels, Armor, Polich, Mothershead, & Stephen, 1975). A related issue involves the relative contributions of different types of patient characteristics—in particular, the extent to which outcome is related to social background on the one hand and drinking symptoms at intake on the other. No clear-cut pattern of findings has been reported on the relative importance of social background variables (such as socioeconomic or marital status) compared to intake symptoms (such as alcohol consumption or behavioral or psychological impairment at intake) in predicting outcome (Armor et al., 1976; Craft et al, 1975; Ruggels et al., 1975). Inferences pertaining to both of these issues have been primarily based on examination of the increments in explained variance (Armor et al., 1976; Bromet, Moos, Bliss, & Wuthmann, 1977; Craft et al., 1975; Ruggels et al., 1975). This method is asymmetric
In the public domain
1105
1106
RUTH C. CRONKITE AND RUDOLF H. MOOS
in that it attributes variance shared by two or more sets of variables to the set that is entered first in the regression analysis (such as patient background variables), and thus may overestimate the contribution of the first set by crediting it with both its unique and shared variance. In contrast, the increment in explained variance attributed to the set of variables added last (such as programrelated variables) represents only the explained variance that is unique to that set of variables, not the variance that is shared with variables that have been entered earlier. Consequently, previous inferences about the relative effects of program-related variables, social background characteristics, and intake symptoms on outcome may be misleading.1 Such inferences have important policy implications for alcoholism treatment. For example, if the unique variance attributed to program-related variables is small, and the variance that is shaied with patient background variables is attributed only to background characteristics, then researchers may conclude that treatment effects are negligible and thus recommend less expensive and more uniform treatment programs. Another unresolved question raised by longitudinal studies of alcoholics is the role of different types of program-related variables in predicting outcome. The relationship of treatment variations to outcome has been approached in several ways. Armor et al. (1976) and Kissin, Platz, and Su (1970) focused on the type and amount of treatment both within and across programs. Bromet et al. (1977) examined the effect of level of participation on outcome in several different treatment programs. In addition to studying the effects of a variety of treatment experiences and treatment programs, Bromet, Moos, and Bliss (1976) have focused on another dimension of program-related variables, a patient's perceptions of the treatment environment. This approach is based on research which suggests that the social environments of psychiatric and correctional programs may be important factors in influencing outcome (Ellsworth, Maroney, Klett, Gordon, & Gunn, 1971; Moos, 1974b). Each of these aspects of treatment variations has been studied separately, but their effects relative
to each other have not been examined within a single study. The purpose of this article is to develop an integrated approach to studying alcoholism treatment programs that will facilitate clarification of the following issues: (a) What are the interrelationships among patient social background variables, intake symptoms, treatment programs, treatment experiences, perceptions of the treatment environment, and outcome? (b) What is the relative importance of program-related variables compared to patient background characteristics in predicting outcome? (c) What is the relative importance of a patient's social background compared to intake symptoms in predicting outcome? (d) What are the roles of different aspects of treatment variations in predicting outcome? A Model of Treatment Outcome In most longitudinal studies of alcoholic patients, the dependent variables are one or more outcome criteria related to posttreatment functioning, such as rehospitalization, alcohol consumption, and occupational, physical, and psychosocial functioning. The independent variables vary across studies, depending on their focus. From reviewing previous research, the independent variables can be divided into "blocks," labeled Blocks 1, 2, 3, 4, and 5. Block 1 consists of a set of sociodemographic variables known to be related to drinking patterns, such as age, sex, ethnicity, marital status, and socioeconomic status. Block 2 includes a patient's drinking symptoms at intake, more specifically, the type and severity of alcoholism-related characteristics, such as alcohol consumption, drinking patterns, physical impairment, and behavioral impairment. Blocks 3, 4, and 5 refer to program-related variables. Block 3 includes the type of program; Block 4 includes the amount of various treatment experiences, such as therapy sessions, Alcoholics Anonymous (AA) meetings, antabuse, and !See Newton and Spurrell (1967a, 1967b) and Mood (1971) for a complete technical discussion of partitioning the sum of squares in regression analysis and Coleman (1975) for a discussion of this issue when applied to school effects.
EVALUATING ALCOHOLISM TREATMENT
1107
so forth, in which the patient participates; cluded in the model (i.e., pp B3 ) are relatively relatively strong effects (occupational funcclose and large (ranging from .434 to .552), tioning and physical concomitants) are those indicating that the major determinant of a that were most highly correlated with social patient's perceptions is the program that the background. patient is in. Determinants of treatment experiences. The only variable hypothesized to have a di- Relationship of Patient Background and rect effect on treatment experiences is the pro- Program-Related Variables to Outcome gram type, and, in fact, the estimates of tihe effect of the program (pis) are relatively large Table 2 displays the direct, indirect, and (and statistically significant), ranging from total effects of each o'f the five variables hy.460 to .689 across all four models (see Table pothesized to affect outcome. The results show
1112
RUTH C. CRONKITE AND RUDOLF H. MOOS
Alcohol Consumption at FIF
(Alcohol Consumption at BIF) Figure 2. Estimation of path coefficients for alcohol consumption. (FIF = follow-up information form; BIF = background information form; B = background; I = intake; P = program; T — treatment experiences; E = treatment environment.)
some clear-cut consistencies across the models. The estimates of p81 (three of which are statistically significant) suggest that the higher the level of social background, the less severe the alcoholism-related symptoms are at fol-
low-up. Similarly, there is a relatively strong association between a patient's intake symptoms and the corresponding outcome criterion at follow-up, indicated by the estimates of Pus (except for a weak effect in the alcohol
Rating of Drinking Problem of FIF
(Rating of Drinking Problem at BIF) Figure 3. Estimation of path coefficients for rating of drinking problem. (FIF = follow-up information form; BIF = background information form; B — background; I = intake; P = program; T = treatment experiences; E = treatment environment.)
EVALUATING ALCOHOLISM TREATMENT
1113
Physical Concomitants at FIF
(Physical Concomitants at BIF)
Figure 4. Estimation of path coefficients for physical concomitants. (FIF = follow-up information form; BIF - background information form; B = background; I - i n t a k e ; P = program- T = treatment experiences; E = treatment environment.) '
consumption model). In fact, for three of the outcome criteria, the intake symptoms had slightly stronger direct effects than social background. The indirect effects of social background that are mediated by the program-related variables together represent between 11% and 73% ,of their total effect,
whereas the indirect effects of the intake symptom together represent between 7% and 23% of their total effect. These results indicate tlhat in three of the models, a substantial proportion of the total effect of social background is via indirect effects that are shared with the program-related variables. In con-
Occupational Functioning at FIF
(Occupational Functioning at BIF)
Figure 5. Estimation of path coefficients for occupational functioning. (FIF = follow-up information form; BIF = background information form; B = background; I = intake; P = program; T — treatment experiences; E = treatment environment.)
1114
RUTH C. CRONKITE AND RUDOLF H. MOOS
trast, most of the total effect of the intake symptoms is direct (7 7 %-93 %) and thus not shared with the program-related variables. There is a strong total effect of program type on outcome (qos). When the treatment experiences and perceptions of the environment are taken into account, however, the program variable has little direct effect (pes) (i.e., almost all of the total effect of tine program is mediated by the two program-related
variables, shown by the indirect effects, and pospss, in Table 2). Treatment experiences (PM) and perceptions of the environment (p 65 ) are both strongly associated with outcome. For all of the outcome criteria except occupational functioning, the direct effect of the perceptions of the environment (PCS) is surprisingly strong, suggesting that perceptions of the treatment program may be important predic-
Tablc 1 Direct, Indirect, and Total Effects of Patient-Related Block Variables on Program-Related Block Variables Type of intake and outcome criterion used Variable Dependent Program
Treatment experiences
Perceptions of environment
Independent Social background direct & total effect: Psi (qai) (no indirect effects) Corresponding intake symptom Direct & total effect: P32 (qsz) (no indirect effects) Social background Indirect effect via program p 43 p3i (also total effect—no direct effect) (q 4 i) Intake symptom Indirect effect via program: p43p32 (also total effect—no direct effect) (qw) Direct effect PAS
Social Background Direct effect : poi Indirect effect via program: p63psi Total effect: q 6 i (PSI + Psspsi) Intake symptom Indirect effect via program: psapsa (also total effect, q 32 ) (no direct effect) Direct effect :p 6 3
' p < .05 (calculated for direct effects only).
Alcohol consumption
.559*
Rating of drinking problem
.522*
Physical concomitants
Occupational functioning
.463*
.219*
-.150*
-.150*
.015
-.080
.257
.360
.274
.124
.007 .460*
.055 .689*
.089 .592*
.086 .570*
.185*
.104
.122*
.025
-243
.269
.256
.112
.428
.373
.378
.087
.007 .434*
.041 .515*
.083 .552*
-.077 .511*
1115
EVALUATING ALCOHOLISM TREATMENT
Table 2 Direct, Indirect, and Total Effects of Patient-Related Variables and Program-Related Block Variables on Four Outcome Criteria
Effects
Alcohol consumption
Rating of drinking problem
Physical concomitants
Occupational functioning
Social background — pci (B) Corresponding intake symptom— pc2 (I) Program — p63 (P) Treatment experiences— pci (T) Perceptions of environment— pss (E)
-.167* .036 .068 -.295* -.190*
-.061 .171* -.014 -.155* -.283*
-.169* .235* .086 -.165* -.264*
-.171* .265* .015 -.166* -.045
.038 -.035 -.076 -.046
-.007 -.029 -.056 -.076
.040 -.032 -.045 -.067
.003 .001 -.021 -.005
-.013 .015 .022
-.002 .014 .003
Direct
Indirect B via
P — Pe 3 p 3 i E
P66P51
P&T— peipiapai rt o 17 1 CxtL
PC5P53P31
I via P
P63P32
P&T— p 6 (p 4 3 p 3 2 P&E— P66P63P32
.001 -.002 -.001
.001 .009 .012
P via T
P64P43
E
Pc5p53
-.136 -.082
-.107 -.146
-.098 -.146
-.095 -.023
-.286 .047 -.150
-.229 .193 -.267
-.273 .259 -.158
-.193 .284 -.103
Total" B— q 6 l I — qea P— qes
" Within the context of the causal model used here (see Figure 1), the total effects of treatment experiences and perceptions of the environment, q M and q«5, respectively, are the same as their direct effects, pm and PCS. Consequently, they are only listed under the direct effects. The total effects listed here were calculated as the sum of the direct and indirect effects. * p < .05 (calculated for direct effects only).
tors of outcome. However, there is substantial interdependence among the program-related variables; only 0%-27% of the direct effect of treatment experiences and l%-33% o'f the direct effect of perceptions of the environment are independent of all prior variables (social background, intake symptom's, and program type). 7 Unique and Joint Contributions of the Explained Variance Table 3 presents the results of partitioning the explained variance for the four outcome criteria. The first five rows display the unique and shared variance attributed to the patient background variables, and the next six rows show the unique and shared vari-
ance attributed to the program-related variables. The rest of the table displays the explained variance that is shared among combinations of the patient-related and programrelated variables. The pattern of results is similar to those obtained from the path analysis. In general, both social background and the intake symptoms contribute substantially to the explained variance, with the combined contribution of their unique and shared variance ranging from 12% to 61% o'f the explained variance. In three of the models, the unique contributions 7
These percentages were calculated by subtracting out all other paths that are causally prior to the direct path of interest. (See Coleman, 197S, for a more detailed discussion of these procedures.)
RUTH C. CRONKETE AND RUDOLF H. MOOS
1116 Table 3
Partitioning oj Explained Variance for Four Outcome Criteria
Variable Patient related Unique variance Background Intake Shared variance Background and intake Subtotal Proportion of R* Program related Unique variance Program Treatment experiences Perceptions of treatment environment Shared variance Sum of all combinations 1 ' Subtotal Proportion of -K2 Shared variance among background, intake, & program-related variables Sum of all combinations" Proportion of R^ Total R2
Alcohol consumption
Rating of drinking problem
.019" .001
.003 .028"
.019" .047"
.026" .064"
.002
.003 .034" .16
.019" .085" .32
.022" .112" .61
.025"
.000 .012" .048"
.003 .017" .043"
.000 .018" .002
.010" .104"
.047" .107"
.013" .076"
.57
.49
.28
.009 .030" .16
.056"
.075"
.106"
.30
.35
.40
.182
.217
.268
.022" .12
.002
,067«
Physical Occupational concomitants functioning
.041" .23 .183
Note. B = background; I = intake; P = program; T = treatment experiences; and E = perceptions of treatment environment. " The explained variance is greater than 1%. b These combinations are PT, PE, TE, PTE. c These combinations are HP, BT, BE, HPT, BTE, BPE, BITE, IP, IT, IE, IPT, ITE, IPE, IPTE, BIP, BIT, BIE, BIPT, BIPK, BITE, and BIPTE.
of the intake symptoms are stronger than the unique explained variance attributed to social background. When taken together with the path analyses, the findings suggest that the intake symptom has a relatively stronger relationship to outcome than does social background. The total of the unique and shared variance attributed to the program-related variables ranges from 16% to 57
