Variations in means/statistical calculations - Revision history

Mtabadmin at 23:45, 8 August 2013

2013-08-08T23:45:38Z

Mtabadmin at 23:44, 8 August 2013

2013-08-08T23:44:39Z

Mtabadmin at 23:40, 8 August 2013

2013-08-08T23:40:33Z

Mtabadmin at 23:39, 8 August 2013

2013-08-08T23:39:43Z

Mtabadmin at 23:39, 8 August 2013

2013-08-08T23:39:24Z

Mtabadmin at 23:39, 8 August 2013

2013-08-08T23:39:04Z

Mtabadmin at 23:38, 8 August 2013

2013-08-08T23:38:33Z

Mtabadmin at 23:37, 8 August 2013

2013-08-08T23:37:13Z

Mtabadmin at 23:06, 8 August 2013

2013-08-08T23:06:19Z

Mtabadmin at 23:05, 8 August 2013

2013-08-08T23:05:08Z

← Older revision		Revision as of 23:45, 8 August 2013
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−	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "$100" or "$100 - $200". On the right you should notice two empty columns. Just click on the first empty column to the right of 100 for example and type in 100 then hit enter. Or if it was a range like 100-200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.	+	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "$100" or "$100 - $200". On the right you should notice two empty columns. Just click on the first empty column to the right of $100 for example and type in 100 then hit enter. Or if it was a range like $100-$200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.

← Older revision		Revision as of 23:44, 8 August 2013
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−	However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.	+	However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between $100,000 and $150,000 a statistical value of 125,000 even if 90% of these people actually fall in the $100,000 to $120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is $100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.


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−	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "100" or "100 - 200". On the right you should notice two empty columns. Just click on the first empty column to the right of 100 for example and type in 100 then hit enter. Or if it was a range like 100-200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.	+	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "$100" or "$100 - $200". On the right you should notice two empty columns. Just click on the first empty column to the right of 100 for example and type in 100 then hit enter. Or if it was a range like 100-200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.

@@ Line 4: / Line 4: @@
 '''How Are Means Calculated?'''
-Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is $$100, then usually the statistical value would also be $$100.
+Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is &#36;100, then usually the statistical value would also be 100.
-However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $$1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.
+However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of &#36;1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.

@@ Line 4: / Line 4: @@
 '''How Are Means Calculated?'''
-Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is $$100, then usually the statistical value would also be 100.
+Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is $$100, then usually the statistical value would also be $$100.
-However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of 1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.
+However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $$1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.

← Older revision		Revision as of 23:39, 8 August 2013
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	'''How Are Means Calculated?'''		'''How Are Means Calculated?'''

−	Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is \$100, then usually the statistical value would also be 100.	+	Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is $$100, then usually the statistical value would also be 100.

@@ Line 4: / Line 4: @@
 '''How Are Means Calculated?'''
-Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is 100, then usually the statistical value would also be 100.
+Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is \$100, then usually the statistical value would also be 100.
@@ Line 29: / Line 29: @@
 Ratings/Scoring type questions are usually not value field, fill in the blank number questions usually are.

← Older revision		Revision as of 23:38, 8 August 2013
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−	Random Words $100 to see if one messes it up. Also take \$5 as number 3 or $$4 to see if that works or $700 maybe. Or if there has to be more text.	+	Random Words 100 to see if one messes it up. Also take \$5 as number 3 or $$4 to see if that works or $700 maybe. Or if there has to be more text.

← Older revision		Revision as of 23:37, 8 August 2013
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	Ratings/Scoring type questions are usually not value field, fill in the blank number questions usually are.		Ratings/Scoring type questions are usually not value field, fill in the blank number questions usually are.
		+
		+
		+
		+	Random Words $100 to see if one messes it up. Also take \$5 as number 3 or $$4 to see if that works or $700 maybe. Or if there has to be more text.

@@ Line 4: / Line 4: @@
 '''How Are Means Calculated?'''
-Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is $100, then usually the statistical value would also be 100.
+Each response to a numeric question is usually associated by mTAB with a statistical value or range. If the response to a question is 100, then usually the statistical value would also be 100.
-However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between $100,000 and $150,000 a statistical value of 125,000 even if 90% of these people actually fall in the $100,000 to $120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is $100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.
+However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of 1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90% of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.
@@ Line 19: / Line 19: @@
-When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "$100" or "$100 - $200". On the right you should notice two empty columns. Just click on the first empty column to the right of $100 for example and type in 100 then hit enter. Or if it was a range like $100-$200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.
+When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "100" or "100 - 200". On the right you should notice two empty columns. Just click on the first empty column to the right of 100 for example and type in 100 then hit enter. Or if it was a range like 100-200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.

← Older revision		Revision as of 23:05, 8 August 2013
Line 7:		Line 7:


−	However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of 1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between 100,000 and 150,000 a statistical value of 125,000 even if 90 of these people actually fall in the 100,000 to 120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is 100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.	+	However, ranges work differently and there are two kinds of range questions. Some range questions come that way from the survey itself. For example, respondents are asked what income range they fall into. The other kind of range question is the kind we implement here at mTAB. For example, if a respondent is asked to write in his income in thousands, you do not want to look at every break of $1,000. That is a useless amount of detail, especially in higher income brackets, so we cluster the respondents into ranges that are more useful. Our older ranges would automatically give ANY respondent who falls between $100,000 and $150,000 a statistical value of 125,000 even if 90% of these people actually fall in the $100,000 to $120,000 range. This would obviously skew the numbers. In older databases, there is nothing that can be done about that unless you want to guess where these respondents fall and then manually alter the stat values. This may be useful sometimes when the last response to a question is $100,000 or more. In a case like this the upper (right hand) stat value may be higher or lower than someone who knows the data well would want.


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−	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "100" or "100 - 200". On the right you should notice two empty columns. Just click on the first empty column to the right of 100 for example and type in 100 then hit enter. Or if it was a range like 100-200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.	+	When you are finished setting up your tab, go to the subset window for the question that you are not getting a mean for. In this window you will see on the left a list of responses such as "$100" or "$100 - $200". On the right you should notice two empty columns. Just click on the first empty column to the right of $100 for example and type in 100 then hit enter. Or if it was a range like $100-$200 you should type 100 in the first column to the right of that response, and 200 in the second column. Do this for all the responses that need a statistical value. "No Answer" should not have a statistical value associated with it.