Why Logic Models?

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● Session Plan
● Objectives
● Theories of action (logic models) define the meaning and relevance of data
● Frameworks for Performance Measures and Decisions
● There are many performance measurement “results” frameworks
● Why Logic Models?
● Types of Evaluation
● Why Logic Models?
● Centrality of Engagement
● Stakeholder Analysis for
Planning and Evaluation
● Dimensions of Data Collection
● Types of Data
● Data Collection Issues
● Data Collection Strategies
● Data Collection Methods
● Coping with Complexity
● Helpful Data Management Tools
● Selected Davidson’s Principles
● Data Collection Public Resources
● Focusing Assessment Indicators
● Focusing Questions and Selecting Indicators: Exercises 4 & 5
● Community Indicator Systems
● General Characteristics of Community Indicator Systems
● Some Missouri Systems
● National Award Winners & Sustainability Sites
● Constructing Composites
● Development of Senior Report

نوع زبان: انگلیسی حجم: 2.54 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 39 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/06/15 12:35:10

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

datum, –, model, indicator, logic, ., collection, focus, question, program, information, community,

توجه: این مطلب در تاریخ 2019/06/15 12:35:10 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

https://oseda.missouri.edu/presentations/eval_indicator_development_mocda_03_2006.ppt

در صورتی که محتوای فایل ارائه شده با عنوان مطلب سازگار نبود یا مطلب مذکور خلاف قوانین کشور بود لطفا در بخش دیدگاه (در پایین صفحه) به ما اطلاع دهید تا بعد از بررسی در کوتاه ترین زمان نسبت به حدف با اصلاح آن اقدام نماییم. جهت جستجوی پاورپوینت های بیشتر بر روی اینجا کلیک کنید.

عبارات پرتکرار و مهم در این اسلاید عبارتند از: datum, –, model, indicator, logic, ., collection, focus, question, program, information, community,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

evaluation and monitoring developing indicators and measuring impact community development academy march ۲ ۶ bill elder university of missouri columbia office of social economic data analysis oseda session plan introductions overview of concepts and issues exercises in focusing questions and selecting indicators review of community indicator systems community indicator review and dialogue discussion objectives understand the development and use of indicators for program evaluation value a focus on outcomes connect data and conceptual models be aware of data collection issues strategies use a process to focus evaluative questions use a process to select indicators explore community indicator systems theories of action logic models define the meaning and relevance of data data information knowledge wisdom theories of action logic models define the meaning and relevance of data data information knowledge wisdom the construction of knowledge involves the orderly loss of information not it’s mindless accumulation. boulding theories of action logic models define the meaning and relevance of data data information knowledge wisdom indicators are conceptually connected data— they’re answers to questions arising from the logic of the program model the construction of knowledge involves the orderly loss of information not it’s mindless accumulation. boulding frameworks for performance measures and decisions basic research theories lead to hypotheses policy applied research policy frameworks logic models focus key questions…especially results inputs resources activities outputs the results of our efforts are what make a difference in community development. outcomes impact results there are many performance measurement results frameworks budget guidance state of missouri utilization focused evaluation patton program logic models kellogg foundation balanced score card state of missouri oit why logic models a program logic model links outcomes with program activities … and the theoretical principles of the program kellogg ۲ ۱ thus logic models set up both formative and summative questions evaluative answers are useful when they reduce the risks of making the wrong decision types of evaluation formative improve periodic and timely focus on program activities and outputs leads to early recommendations for program improvement summative prove were resources committed worthwhile focus on outcomes and impact measures value of program based on impact kellogg logic model development guide inputs outputs outcomes program investments activities participation short medium what we invest what we do who we reach what results long term there are multiple dimensions and feedback loops—logical..if then relationships… evaluation check and verify what do you want to know how will you know it planning start with the end in mind why logic models a program logic model links outcomes with program activities … and the theoretical principles of the program kellogg ۲ ۱ thus logic models set up both formative and summative questions evaluative answers are useful when they reduce the risks of making the wrong decision centrality of engagement develop and sustain relationships ability to mobilize resources collaboration ….. beyond communication and coordination achieving focus sustainable actions stakeholder analysis for planning and evaluation who – reputational sampling what information information for what how to mobilize … interests types of influence resources coping with differences source credibility expertise—trust dimensions of data collection types of data data collection issues data collection strategies data collection methods types of data quantitative counts rates means closed ended questions hard requires adequate statistical treatment require clear context for interpretation qualitative focus groups case studies open ended questions soft requires interpretation can be powerful or perceived as self serving data collection issues validity and reliability reproducible—transparent public consistent—accurate—precise number of cases timeliness and frequency of measurement lagging indicators infrequent sources u.s. census expense data collection issues representative measures selection bias – intended or otherwise types of sampling cluster stratified confidentiality hippa irb historical and future availability trends disaggregation categories nclb security encryption personnel servers data collection strategies quality assurance field control training pilot testing ongoing monitoring documentation units of analysis smallest appropriate data linkage merging ids and confidentiality – extract files without ids careful about size of files data handling – transfers data collection strategies proxy measures proxy measures of health care status mothers level of education repeat clients — customer satisfaction collaborations sharing existing data files bundling effort teams samples infrastructure mous partnerships stratified sampling categories of interest data collection methods existing data secondary data sources census mcdc mica meric oseda agency files and records access new data collection adjusting practices clear planning roles and responsibilities direct costs impact on business practices personnel impact on transaction files data collection methods sample surveys interviews direct and phone questionnaires differential response rates direct observation protocols design issues instrument construction selecting existing items sampling multiple methods as n declines so does reliability web applications simple—complex data collection methods qualitative methods focus groups case studies open ended interviews design issues emergent issues time frames representativeness analysis and reporting coping with complexity build as simple a plan as possible—determine what you really need stick to it plan all the way through analysis reporting build a capable team to work your plan consider both internal and external talent adopt an appropriate approach helpful data management tools database management systems pick up trucks access and dump trucks sql design design and design architecture statistical analysis systems sas spss spreadsheets graphics geographic information systems gis web applications dynamic on line analytical processing olap dynamic looking menu guided pages with tables and charts gif images selected davidson’s principles back it up do it now you can’t analyze what you don’t measure. take control of the structure and flow of your data—save a copy of the original data. change awareness—keep a record of data changes and manipulations diagrams help . implausibility—always check for outliers. source davidson fred ۱۹۹۶ principals of statistical data handling sage publications thousand oaks ca. data collection public resources universities truman school – affiliated centers extension – oseda cpac cares state agencies including.. meric ded missouri information for community assessment mica dhss mcdc – missouri census data center focusing assessment indicators smart indicators specific measurable attainable results oriented timed assessing indicators cost access availability compatible clear reliability and validity focusing questions and selecting indicators exercises ۴ ۵ select a logic model morenet or other scan chapter ۴ of logic model guide develop one formative question and one summative question from a focus area ۴ then define audience question and use then develop an indicator for each question ۵ community indicator systems general characteristics of community indicator systems open for involvement of a wide constituency a big picture perspective a plan of work based on an objective review of data action …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: datum, –, model, indicator, logic, ., collection, focus, question, program, information, community,

این فایل پاورپوینت شامل 39 اسلاید و به زبان انگلیسی و حجم آن 2.54 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/06/15 12:35:10 استخراج شده است.

https://oseda.missouri.edu/presentations/eval_indicator_development_mocda_03_2006.ppt

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