Monitoring Glossary - Revision history

Wiki Gardener at 10:57, 20 January 2015

2015-01-20T10:57:54Z

Show changes

Wiki Gardener at 14:13, 14 August 2014

2014-08-14T14:13:23Z

Ranisha at 15:13, 23 July 2014

2014-07-23T15:13:34Z

Ranisha at 13:06, 17 July 2014

2014-07-17T13:06:01Z

Wiki Gardener at 13:53, 18 June 2014

2014-06-18T13:53:52Z

Katharina Wiedemann at 08:04, 29 May 2013

2013-05-29T08:04:55Z

Wiki Gardener at 09:12, 21 November 2012

2012-11-21T09:12:07Z

Show changes

Lisafeldmann at 08:36, 14 April 2011

2011-04-14T08:36:20Z

Rauwol mar at 12:12, 2 June 2010

2010-06-02T12:12:55Z

Intern at 13:01, 8 December 2009

2009-12-08T13:01:40Z

@@ Line 1: / Line 1: @@
 = Overview =
 = Glossary =
-{| border="1" cellspacing="0"
+{| cellspacing="0" border="1"
 |-
 | style="background-color: rgb(255, 255, 153)" | '''Term'''
@@ Line 25: / Line 29: @@
 |-
 | Experimental
-| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black; font-size: 8pt; mso-bidi-font-family: arial"></span>
+| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black;  font-size: 8pt;  mso-bidi-font-family: arial"></span>
 |-
 | Extrapolation
@@ Line 123: / Line 127: @@
 = Further Information =
-*[[Impact_Monitoring_Guidelines|Impact Monitoring Guidelines]]<br/>
+*[[Impact Monitoring Guidelines|Impact Monitoring Guidelines]]<br/>
-*[[Portal:Impacts|Portal:Impacts]]<br/>
+*[[Portal:Impacts|Impact Portal on energypedia]]<br/>
-*[[Online_Monitoring_Tools|Online Monitoring Tools]]<br/>
+*[[Online Monitoring Tools|Online Monitoring Tools]]<br/>
 = References =
-*[http://www.stats.gla.ac.uk/steps/glossary/http://info.worldbank.org/etools/docs/library/207203/Module3.pdf stats.gla.ac.uk]
+*[http://www.stats.gla.ac.uk/steps/glossary/ Statistics Glossary, V. Easton, J. McColl]
-*[http://info.worldbank.org/etools/docs/library/207203/Module3.pdf info.worldbank.org]
+*[http://info.worldbank.org/etools/docs/library/207203/Module3.pdf Basic Poverty Measurement & Analysis Course, World Bank Institute]
-*[http://www.fao.org/docrep/x5307e/x5307e08.htm fao.org]
+*[http://www.fao.org/docrep/x5307e/x5307e08.htm Semi-structured interviews, FAO]
 *Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C. [http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners]
 *Joint Committee on Standards for Educational Evaluation. (1994). The Program Evaluation Standards, 2nd ed. Thousand Oaks, CA: Sage.
 *OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.
 [[Category:Monitoring]]

@@ Line 122: / Line 122: @@
 = Further Information =
 = References =
@@ Line 132: / Line 136: @@
 *OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.
 [[Category:Impacts]]

@@ Line 25: / Line 25: @@
 |-
 | Experimental
-| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black;  font-size: 8pt;  mso-bidi-font-family: arial"></span>
+| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black; font-size: 8pt; mso-bidi-font-family: arial"></span>
 |-
 | Extrapolation
@@ Line 122: / Line 122: @@
 = Further Information =
 = References =
@@ Line 134: / Line 132: @@
 *OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.
 [[Category:Monitoring]]

@@ Line 1: / Line 1: @@
-''Feel free to add and revise content ....''
+= Overview =
-{| cellspacing="0" border="1"
+= Glossary =
 |-
-| '''Term'''
+| style="background-color: rgb(255, 255, 153)" | '''Term'''
-| '''Definition'''
+| style="background-color: rgb(255, 255, 153)" | '''Definition'''
 |-
 | Absolute poverty line
@@ Line 29: / Line 31: @@
 |-
 | Focus Group Discussion
-| <span>A qualitative method to obtain in-depth information on concepts and perceptions about a certain topic through spontaneous group discussion of approximately 6–12 persons, guided by a facilitator.</span>
+| A qualitative method to obtain in-depth information on concepts and perceptions about a certain topic through spontaneous group discussion of approximately 6–12 persons, guided by a facilitator.
 |-
 | Independent Sampling
@@ Line 38: / Line 40: @@
 |-
 | Logical Framework (Logframe)
-| <span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">Management tool used to improve the design of interventions, most often at the project level. It involves identifying strategic elements (inputs, outputs, outcomes, impact) and their causal relationships, indicators, and the assumptions or risks that may influence success and failure. It thus facilitates planning, execution and evaluation of a development intervention.</font></span>
+| Management tool used to improve the design of interventions, most often at the project level. It involves identifying strategic elements (inputs, outputs, outcomes, impact) and their causal relationships, indicators, and the assumptions or risks that may influence success and failure. It thus facilitates planning, execution and evaluation of a development intervention.
 |-
 | Median
@@ Line 47: / Line 49: @@
 |-
 | (Multiple) Regression Analysis
-| <span style="font-size: 10pt"><font face="arial">In statistics, <span style="mso-bidi-font-weight: bold">regression analysis</span> includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.</font></span>
+| In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
 |-
 | Percentile
@@ Line 67: / Line 69: @@
+|
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.</font></span>
+*''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.</font></span>
+*''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.</font></span>
+*''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.</font></span>
+*''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.
 |-
@@ Line 90: / Line 92: @@
 | Selection Bias
+|
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples.</font></span>
+*Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples.
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">In quasi-experimental designs, statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>
+*In quasi-experimental designs, statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.
 |-
 | Semi-Structured Interview
+|
-*<span>Semi-structured interviews are conducted with a fairly open framework which allow for focused, conversational, two-way communication. They can be used both to give and receive information.</span>
+*Semi-structured interviews are conducted with a fairly open framework which allow for focused, conversational, two-way communication. They can be used both to give and receive information.
-*<span>U</span><span>nlike the questionnaire framework, where detailed questions are formulating ahead of time, semi structured interviewing starts with more general questions or topics. Relevant topics (such as cookstoves) are initially identified and the possible relationship between these topics and the issues such as availability, expense, effectiveness become the basis for more specific questions which do not need to be prepared in advance.</span>
+*Unlike the questionnaire framework, where detailed questions are formulating ahead of time, semi structured interviewing starts with more general questions or topics. Relevant topics (such as cookstoves) are initially identified and the possible relationship between these topics and the issues such as availability, expense, effectiveness become the basis for more specific questions which do not need to be prepared in advance.
-*<span>Not all questions are designed and phrased ahead of time. The majority of questions are created during the interview, allowing both the interviewer and the person being interviewed the flexibility to probe for details or discuss issues.</span>
+*Not all questions are designed and phrased ahead of time. The majority of questions are created during the interview, allowing both the interviewer and the person being interviewed the flexibility to probe for details or discuss issues.
 |-
@@ Line 117: / Line 119: @@
 |}
-'''Sources:'''
+<br/>
-*[http://www.stats.gla.ac.uk/steps/glossary/http://info.worldbank.org/etools/docs/library/207203/Module3.pdf http://www.stats.gla.ac.uk/steps/glossary/]
-*[http://info.worldbank.org/etools/docs/library/207203/Module3.pdf http://info.worldbank.org/etools/docs/library/207203/Module3.pdf]
+= Further Information =
-*[http://www.fao.org/docrep/x5307e/x5307e08.htm http://www.fao.org/docrep/x5307e/x5307e08.htm]
-*Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C. [http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners.&lr&pg=PP1#v=onepage&q&f=false http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners.&lr&pg=PP1#v=onepage&q&f=false]
 *Joint Committee on Standards for Educational Evaluation. (1994). The Program Evaluation Standards, 2nd ed. Thousand Oaks, CA: Sage.
-*<span style="font-size: 10pt;  mso-bidi-font-family: arial;  mso-bidi-font-weight: bold"><font face="arial">OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.</font></span>
+*OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.
 [[Category:Impacts]]

@@ Line 1: / Line 1: @@
 ''Feel free to add and revise content ....''
@@ Line 22: / Line 23: @@
 |-
 | Experimental
-| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black; font-size: 8pt; mso-bidi-font-family: arial;"></span>
+| The plan of an experiment, including selection of subjects, order of administration of the experimental treatment, the kind of treatment, the procedures by which it is administered, and the recording of the data (with special reference to the particular statistical analyses to be performed.<span style="color: black;  font-size: 8pt;  mso-bidi-font-family: arial"></span>
 |-
 | Extrapolation
@@ Line 28: / Line 29: @@
 |-
 | Focus Group Discussion
-| <span style="font-family: &quot;arial","sans-serif"; font-size: 10pt; mso-bidi-font-family: "times new roman"; mso-fareast-font-family: calibri; mso-fareast-theme-font: minor-latin; mso-bidi-theme-font: minor-bidi; mso-fareast-language: en-us; mso-ansi-language: de; mso-bidi-language: ar-sa;">A qualitative method to obtain in-depth information on concepts and perceptions about a certain topic through spontaneous group discussion of approximately 6–12 persons, guided by a facilitator.</span>
+| <span>A qualitative method to obtain in-depth information on concepts and perceptions about a certain topic through spontaneous group discussion of approximately 6–12 persons, guided by a facilitator.</span>
 |-
 | Independent Sampling
@@ Line 37: / Line 38: @@
 |-
 | Logical Framework (Logframe)
-| <span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">Management tool used to improve the design of interventions, most often at the project level. It involves identifying strategic elements (inputs, outputs, outcomes, impact) and their causal relationships, indicators, and the assumptions or risks that may influence success and failure. It thus facilitates planning, execution and evaluation of a development intervention.</font></span>
+| <span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">Management tool used to improve the design of interventions, most often at the project level. It involves identifying strategic elements (inputs, outputs, outcomes, impact) and their causal relationships, indicators, and the assumptions or risks that may influence success and failure. It thus facilitates planning, execution and evaluation of a development intervention.</font></span>
 |-
 | Median
@@ Line 46: / Line 47: @@
 |-
 | (Multiple) Regression Analysis
-| <span style="font-size: 10pt;"><font face="arial">In statistics, <span style="mso-bidi-font-weight: bold;">regression analysis</span> includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.</font></span>
+| <span style="font-size: 10pt"><font face="arial">In statistics, <span style="mso-bidi-font-weight: bold">regression analysis</span> includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.</font></span>
 |-
 | Percentile
@@ Line 66: / Line 67: @@
+|
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of</font></span><span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.</font></span>
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators</font></span><span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.</font></span>
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation</font></span><span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.</font></span>
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.</font></span>
 |-
@@ Line 89: / Line 90: @@
 | Selection Bias
+|
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an</font></span><span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">Selection bias relates to unobservables that may bias outcomes (for example, individual ability, preexisting conditions). Randomized experiments solve the problem of selection bias by generating an</font></span><span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">experimental control group of people who would have participated in a program but who were randomly denied access to the program or treatment. The random assignment does not remove selection bias but instead balances the bias between the participant and nonparticipant samples.</font></span>
-*<span style="font-size: 10pt; mso-bidi-font-family: arial;"><font face="arial">In quasi-experimental designs, statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial"><font face="arial">In quasi-experimental designs, statistical models (for example, matching, double differences, instrumental variables) approach this by modeling the selection processes to arrive at an unbiased estimate using nonexperimental data. The general idea is to compare program participants and nonparticipants holding selection processes constant. The validity of this model depends on how well the model is specified.</font></span>
 |-
 | Semi-Structured Interview
+|
-*<span style="font-family: &quot;arial","sans-serif"; font-size: 10pt;">Semi-structured interviews are conducted with a fairly open framework which allow for focused, conversational, two-way communication. They can be used both to give and receive information.</span>
+*<span>Semi-structured interviews are conducted with a fairly open framework which allow for focused, conversational, two-way communication. They can be used both to give and receive information.</span>
-*<span style="font-family: &quot;arial","sans-serif"; font-size: 10pt;">U</span><span style="font-family: &quot;arial","sans-serif"; font-size: 10pt;">nlike the questionnaire framework, where detailed questions are formulating ahead of time, semi structured interviewing starts with more general questions or topics. Relevant topics (such as cookstoves) are initially identified and the possible relationship between these topics and the issues such as availability, expense, effectiveness become the basis for more specific questions which do not need to be prepared in advance.</span>
+*<span>U</span><span>nlike the questionnaire framework, where detailed questions are formulating ahead of time, semi structured interviewing starts with more general questions or topics. Relevant topics (such as cookstoves) are initially identified and the possible relationship between these topics and the issues such as availability, expense, effectiveness become the basis for more specific questions which do not need to be prepared in advance.</span>
-*<span style="font-family: &quot;arial","sans-serif"; font-size: 10pt;">Not all questions are designed and phrased ahead of time. The majority of questions are created during the interview, allowing both the interviewer and the person being interviewed the flexibility to probe for details or discuss issues.</span>
+*<span>Not all questions are designed and phrased ahead of time. The majority of questions are created during the interview, allowing both the interviewer and the person being interviewed the flexibility to probe for details or discuss issues.</span>
 |-
@@ Line 117: / Line 118: @@
 '''Sources:'''
 *[http://www.stats.gla.ac.uk/steps/glossary/http://info.worldbank.org/etools/docs/library/207203/Module3.pdf http://www.stats.gla.ac.uk/steps/glossary/]
 *[http://info.worldbank.org/etools/docs/library/207203/Module3.pdf http://info.worldbank.org/etools/docs/library/207203/Module3.pdf]
 *[http://www.fao.org/docrep/x5307e/x5307e08.htm http://www.fao.org/docrep/x5307e/x5307e08.htm]
-*Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C. [http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker%2C%20J.%20L.%20(2000)%3A%20Evaluating%20the%20Impact%20of%20Development%20Projects%20on%20Poverty.%20A%20Handbook%20for%20Practioners.&lr&pg=PP1#v=onepage&q&f=false http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker%2C%20J.%20L.%20(2000)%3A%20Evaluating%20the%20Impact%20of%20Development%20Projects%20on%20Poverty.%20A%20Handbook%20for%20Practioners.&lr&pg=PP1#v=onepage&q&f=false]
+*Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners. The World Bank, Washington, D.C. [http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners.&lr&pg=PP1#v=onepage&q&f=false http://books.google.de/books?id=AMgvY6vpFN8C&lpg=PR6&ots=g_TivF0nbL&dq=Baker, J. L. (2000): Evaluating the Impact of Development Projects on Poverty. A Handbook for Practioners.&lr&pg=PP1#v=onepage&q&f=false]
 *Joint Committee on Standards for Educational Evaluation. (1994). The Program Evaluation Standards, 2nd ed. Thousand Oaks, CA: Sage.
-*<span style="font-size: 10pt; mso-bidi-font-family: arial; mso-bidi-font-weight: bold;"><font face="arial">OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.</font></span>
+*<span style="font-size: 10pt;  mso-bidi-font-family: arial;  mso-bidi-font-weight: bold"><font face="arial">OECD (2009): Glossary of Key Terms in Evaluation and Results Based Management. DAC Network on Development Evaluation.</font></span>
 [[Category:Impacts]]

← Older revision		Revision as of 12:12, 2 June 2010
Line 131:		Line 131:
	<br><br><br>		<br><br><br>

−	[[Category:~~Monitoring~~]]	+	[[Category:Impacts]]

@@ Line 45: / Line 45: @@
 | The mode is the most frequently occurring value in a set of discrete data.
 |-
 | (Multiple) Regression Analysis
-| <span style="font-size: 10pt"><font face="Arial">In statistics, <span style="mso-bidi-font-weight: bold">regression analysis</span> includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.<span class="MsoPageNumber"><span style="font-family: 'arial','sans-serif'; mso-bidi-font-family: 'times new roman'; mso-bidi-theme-font: minor-bidi"><o:p></o:p></span></span></font></span>
+| <span style="font-size: 10pt"><font face="Arial">In statistics, <span style="mso-bidi-font-weight: bold">regression analysis</span> includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.</font></span>
 |-
 | Percentile