Changes for page Progression Report

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3	3	Note: This is not a progression over time directly, but a progression through the assessments.
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6		-===== __**CREATING A NEW PROGRESSION REPORT**__ =====
	6	+__**CREATING A NEW PROGRESSION REPORT**__
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8		-~1. To create a Progression report, when you first open the application, you must select the "Reports and Analytics" tab on the top menu bar. After the pull down menu appears, select the third option marked "Reports"
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10		-[[image:new reports option - for all reports.PNG]]
	9	+~1. To create a Progression report, when you first open the application, you must select the "Reports and Analytics" tab on the top menu bar.After the pull down menu appears, select the third option marked "Reports"
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	11	+1.1 Of the many options available to you on the drop-down menu on the right, select the first option for "Saved and Named Reports"
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	13	+[[image:progression report - named and saved reports.PNG||width="100%"]]
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14	14	2. Select the "Personal Reports" tab under Saved and Named Reports
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16	16	2.2 Next, in order to create a new report configuration, click on "New Saved Personal Report" represented by the green plus icon.
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104	104	Next, we will look at the report itself and the information it conveys.
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107		-===== **__UNDERSTANDING YOUR PROGRESSION REPORT__** =====
	110	+**__UNDERSTANDING YOUR PROGRESSION REPORT__**
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109	109	Remember, the goal of the report is to enable you to see - based on assessment types (also referred to as "reason for assessment") - how a group or population (however your program chose to define it, whether through OUs, tagged groups, etc.) is progressing in aggregate through their assessments. Refer to the sample report below for reference.
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133	133	I: **Analysis of data.** Below is a sample inquiry and mathematical model calculation of how to interpret your data.
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135		-* Is the change in the percentage of assessments classified as “0&1” and “2&3” between two reasons for assessment significant? This is shown here using the McNemar’s statistical test (when the population that has changed bucket is greater than 25 individuals) or the Binomial test (if the population that has changed bucket is 24 or less). This is color coded in red and blue to designate if the statistical significance test has been passed or has failed. Since both the McNemear’s and Binomial tests work on constant population between time one (T1) and time 2 (T2) (e.g. “Initial Assessment” to “Scheduled Update”) the tests use a subset of the population displayed in C. For example, when computing the significance of change between “Initial Assessment” and “Scheduled Update” the report considers all the clients with “Scheduled Update” assessments in the selected time frame and a corresponding “Initial Assessment”, even if the initial assessment is before the selected time frame. As explained in the footnote of the report, significance also shows the size of the population used to compute the test and the number of clients who improved or got worse.
	139	+* Is the change in the percentage of assessments classified as “0&1” and “2&3” between two reasons for assessment significant? This is shown here using the McNemar’s statistical test (when the population that has changed bucket is greater than 25 individuals) or the Binomial test (if the population that has changed bucket is 24 or less). This is color coded in red and green to designate if the statistical significance test has been passed or has failed. Since both the McNemear’s and Binomial tests work on constant population between time one (T1) and time 2 (T2) (e.g. “Initial Assessment” to “Scheduled Update”) the tests use a subset of the population displayed in C. For example, when computing the significance of change between “Initial Assessment” and “Scheduled Update” the report considers all the clients with “Scheduled Update” assessments in the selected time frame and a corresponding “Initial Assessment”, even if the initial assessment is before the selected time frame. As explained in the footnote of the report, significance also shows the size of the population used to compute the test and the number of clients who improved or got worse.