Identify sources of Examples or error types in evaluation: estimating and reporting On surveys some answers may not be completed. – error and best practices for On surveys respondent may not understand directions – avoiding these errors. Data entry errors If identify out-of-range conditions (results), may eliminate. Paraphrasing can sometimes change the meaning. Evaluator bias being too emotionally involved in the evaluation, – so inclined to shape the results for desired results. Best practices bullet points pp. 79 80. – – Recognize bias / influences Be open to findings Generate options for recommendations and next steps Review data from multiple perspectives Include demographics when people focus of analysis Organize the data by process Group issues and compare to other data findings Analyze data from the perspective of a case study View data with out of your comfort zone approaches Make linkages evaluation is all about cause and effect – Discuss the issues involved Three major tasks are involved with analyzing data: sorting, with the synthesis of data. tabulating, and comparing with summarized data. List the methods of Communication should be timely and targeted to specific communicating data results audiences. It should communicate so as to be unbiased, modest, to and match each style and consistent. All communication should reflect a tone of using with an intended purpose. evaluation data and communication to be supportive, helpful, sincere, and timely. Matching styles: Reflection thinking data until pattern or like is seen – Two-axis Matrix x-y axes chart. – Three-axis Matrix cube-shaped object with x-y-z axes – Flowchart shows process flow, decision points, input-output – Events network visual of what should be – Dichotomy This or That categorization – Argumentation similar to debate, aimed at resolving two or – more theses/positions. Ends with best possible hypothesis & its supporting logic Graphic models organizational charts, diagrams, maps… – Makes information visual and easy to understand.
Identify sources of estimating and reporting error and best practices for avoiding these errors. Examples or error types in evaluation: On surveys – some answers may not be completed. On surveys – respondent may not understand directions Data entry errors If identify out-of-range conditions (results), may eliminate. Paraphrasing can sometimes change the meaning. Evaluator bias – being too emotionally involved in the evaluation, so inclined to shape the results for desired results. Best practices – bullet points pp. 79 – 80. Recognize bias / influences Be open to findings Generate options for recommendations and next steps Review data from multiple perspectives Include demographics when people focus of analysis Organize the data by process Group issues and compare to other data findings Analyze data from the perspective of a case study View data with out of your comfort zone approaches Make linkages – evaluation is all about cause and effect Discuss the issues involved with the synthesis of data. Three major tasks are involved with analyzing data: sorting, tabulating, and comparing with summarized data. List the methods of communicating data results to and match each style with an intended purpose. Communication should be timely and targeted to specific audiences. It should communicate so as to be unbiased, modest, and consistent. All communication should reflect a tone of using evaluation data and communication to be supportive, helpful, sincere, and timely. Matching styles: Reflection – thinking data until pattern or like is seen Two-axis Matrix – x-y axes chart. Three-axis Matrix – cube-shaped object with x-y-z axes Flowchart – shows process flow, decision points, input-output Events network – visual of what should be Dichotomy – This or That categorization Argumentation – similar to debate, aimed at resolving two or more theses/positions. Ends with best possible hypothesis & its supporting logic Graphic models – organizational charts, diagrams, maps… Makes information visual and easy to understand.
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