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Inclusive Evaluation

Inclusive evaluation emerged in response to increasing pressures to be responsive to cultural pluralism, as well as a redefinition of the role of the evaluator in relation to social change. The transformative paradigm provides the underlying philosophical assumptions that guide the work of the inclusive evaluator. Methodologically similar to democratic deliberative evaluation in its use of collective deliberation, stakeholder inclusiveness, and dialogical data collection methods, inclusive evaluation derives its difference from its deliberate emphasis on including groups that have historically experienced oppression and discrimination on the basis of gender, culture, economic level, ethnicity or race, disability, sexual orientation, language, or religious preference.

The role of the evaluator in an inclusive context is to function as a member of a team whose function is to bring about social change. This work is conducted with a conscious awareness of the social injustices that are part of the everyday living experience of many groups of people. The recognition of social injustice is accompanied by a willingness to challenge the status quo. For example, schools are failing the poorest children of color, prisons are inordinately full of men of color, programs for youth are not universally successful in preventing drug use or teenage pregnancy, and the burden falls more on the poor, minorities, and other known segments of the population. Thus the evaluator's job is to seek to uncover the weaknesses in the present system that contribute to a continuation of poor education, poverty, and other social ills. The evaluator can encourage those in power to go beyond a “blame the victim” stance to a position in which the failures within the system can be revealed. The challenge lies in finding ways to bring the thinking of critical theorists, feminists, and others who write from the antidiscriminatory paradigm to the evaluation community and the stakeholders. Society expects evaluators to be objective, and thus it is incumbent upon the inclusive evaluator working within the transformative paradigm to explain the meaning of objectivity thusly: Objectivity, within this framework, means a lack of bias that is achieved by inclusion of all relevant stakeholders in such a way that authentic and accurate representations of their viewpoints are considered.

In terms of actual practice of evaluation, the application of an inclusive approach to evaluation has implications for every step in the process: design of the study, definition of the problem, selection of indicators of success, sampling and data collection decisions, development of intervention strategies, addressing power differentials in the study, and setting standards for a good evaluation.

The Design of the Study

An inclusive approach to evaluation is amenable to quantitative, qualitative, or mixed methods designs. One underlying principle that guides the choice of design is that members of the community affected by the evaluation would be involved to some degree in the methodological and programmatic decisions. Several issues arise related to design choice, including being responsive to the community's perceptions and involvement, ethical issues concerning denial of treatment, and sharing of perks with those involved in the study.

Ethical concerns arise in design choice when the design involves denial of treatment, lack of community involvement in the decision to participate, or lack of fully informed consent as to the consequences of participating, especially as these relate to the use of a control group or a placebo. Denial of treatment is one strategy used in experimental designs to establish a control group. However, this is viewed as especially problematic in terms of the ethics associated with the transformative-emancipatory paradigm. Assignment to treatments or denial of treatment on a random basis is also considered to be unethical and illegal in many schools and social service agencies. In those settings, reliance on a true experimental design with random selection and random assignment to conditions is not possible. Evaluators can recommend designs that avoid such ethical dilemmas, such as use of the next best current treatment, mixed methods, qualitative approaches, time series designs, use of known alternative treatments, comparison with an extant group, or comparison with a larger statistical base in terms of known levels of incidence.

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