Result Based M&E and SPSS

Venue: Istanbul, Turkey

The Fee Covers: Training, Training Materials, Certificates, Accommodation Lunch and refreshments

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Result Based Monitoring and Evaluation

Course Objectives:

By the end of this workshop, the participants will be able to:

• Recognize functions of an M&E plan?
• Recognize functions of an M&E plan by level of the health system
• Develop an M&E plan?
• List down elements of the M&E plan
• Brief project description
• Purposes of M&E plan
• Brief history of M&E plan development
• Development evaluation framework (Log Frame, Results and conceptual)
• Develop indicator system
• Plan Field visits professionally
• Basics of SPSS
• Impact evaluation design
• Dissemination and utilization plan
• Recognize the value of E-Governance i Health System
• Discuss the importance and building blocks of Health Strengthening Systems on Health
• Discuss E-Governance in Health in Turkey, India and Thailand
• How to report and deliver presentation

Course Outline:

Concepts:

• Terms and concepts applied in M & E Processes
• The value for monitoring and evaluation in the implementation of projects
• Monitoring and Evaluation in the Project Cycle Management
• Role of in Monitoring and Evaluation Project planning

M&E Plans

• The M&E Plan
• Why are M&E Plans Important?
• M&E Plan Components:
• Introduction
• Program Description and Framework
• Detailed description of the plan indicators
• Data collection plan
• Plan for monitoring
• Plan for evaluation
• Plan for the utilization of the information gained
• Mechanism for updating the plan

Frameworks:

• What are frameworks?
• Conceptual framework
• Result framework
• Logic Model
• Summary of Frameworks

Indicators

• Factors to consider when selecting indicators
• Indicator metrics, pyramids, and reference sheets
• Linking indicators with frameworks
• Selecting indicator targets
• Common pitfalls in indicator selection
• How many indicators are enough?

Data Collection/ Capture/Data Quality Checks

• Identify types of data collection tools(This will comparing these tools with the tools currently in use in your organization to identify areas of improvement)
• Identify type of tools at each M &E level
• Define data capture
• Identify key steps in data capture
• Define data quality
• Quantitative methods
• Qualitative methods
• Data Analysis/Interpretation & Reporting

Role of data analysis in monitoring and evaluation

• Types of data analysis
• Interpretation of data
• Deciding which reporting methods to use
• Writing an effective M/E report
• Communication of M&E findings for action and accountability

Result Based Management

• Result Based Monitoring and Evaluation
• Link between RBM and RBMM
• Responsibilities of M&E Team
• The manager
• The support team: Data collectors, data analyst, data entry team
• Community Based Program Implementation M&E
• Participatory vs non-participatory  Key concepts: Values of development work - Accountability
• Impact based M&E

SPSS and Data Management

Daily Topics:

Basics of SPSS. Descriptive statistics, charts and graphs, hypothesis analysis, testing dependence/independence.

• Different levels of measure: scale, ordinal, nominal.
• Basic descriptive statistics. Measures of central tendency: mean, median, mode. Measures of dispersion: range, standard deviation, variance.
• Graphs and charts: bar chart, pie chart, histogram, scatter plot. Which of them should be used in different situations?
• Hypothesis analysis with SPSS. Testing dependence/independence, Pearson’s chi-square. Levels of significance.

SPSS in practice, some useful tips. The "tricks" of dependence/independence testing

• Converting different types of files into ".sav" files. How can one enter raw data into SPSS efficiently, how to label the data.
• Transforming the data.
• Multiple independence analysis - a useful way to circumvent "significance level problems".
• Elementary principal component analysis.

Principal Component Analysis with SPSS.

• Definition and meaning of the principal component.
• Communalities, extraction, variance.
• Usability of the method (the cases of scale and ordinal measures).
• Information content and the distribution of the principal component.
• Omission of variables with insufficient communalities.

Factor Analysis with SPSS using a "real example".

• The factor matrix and its interpretation.
• The Maximum Likelihood method. Repairing the model.
• Factor rotation and the varimax method.
• Omission of variables belonging to more than one factor, the appearance of latent variables.
• Establishing factor scores. Statistics of factor scores.
• When the factors explain more than 100%. A common pitfall.

Explanatory models. Analysis of Variance, Regression Analysis.

• Using SPSS for Analysis of Variance (ANOVA)
• Twofold ANOVA, interaction.
• Linear regression analysis. When should we accept a regression line?
• Two variable regressions.

Charts and tables

• Simple charts: bar, pie, histogram
• Compound charts, chart builder
• Chart editing, 3D effects, color and fill
• Adding special effect to charts, jittering, plethora of graphs
• Saving and manipulating tables

SPSS Syntax    

• Syntax files
• The syntax editor
• Writing syntax using the Log
• Open up SPSS files using syntax
• Define variables using syntax
• Create compute and auto-record procedures
• Create simple and multiple IF and DO-IF statements