Custom tables syntax
Locked
 
|
Hi there,
I am trying to produce a custom table that shows Row % with a row total count as the last column. I want it to look something like this Yes % No % DK% Total Count of row Complaint 10% 80% 10% 198 Incident 10% 80% 10% 315 Rebate 15% 60% 25% 3188 My syntax shows Row% the whole way through the table but I only want it displayed at the end. Any help would be appreciated. * Custom Tables. CTABLES /VLABELS VARIABLES=interaction_type R2 DISPLAY=LABEL /TABLE interaction_type [C] BY R2 [C][COUNT F40.0, ROWPCT.COUNT F40.1] /CATEGORIES VARIABLES=interaction_type R2 ORDER=A KEY=VALUE EMPTY=INCLUDE TOTAL=YES POSITION=AFTER MISSING=EXCLUDE. Thank you, Vanessa. ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
|
Vanessa I wanted to produce the same output as you for tutorial purposes. Earlier versions of SPSS had a procedure MEANS with a sub-command /CROSSBREAK which you could use for summaries of %% as well as means (by recoding a criterion value to 100 and averything else to 0). Check out the exchanges from 17 March 2012 on Nabble. The subject was “In support of CTABLES” and Jon Peck supplied some syntax to do exactly what you want. Using employee data.sav In the gui, turn on totals via Categories and Totals. Then, in the Summary Statistics subdialog, check Custom Summary Statistics for Totals and Subtotals, and off you go. I also have a draft tutorial which I can send: if it won’t go via Nabble I’ll send it off-list. Years ago Raynald Levesque sent me exactly what I wanted, but I think that’s on another computer. John F Hall (Mr) [Retired academic survey researcher] Email: [hidden email] Website: www.surveyresearch.weebly.com SPSS start page: www.surveyresearch.weebly.com/spss-without-tears.html -----Original Message----- Hi there, I am trying to produce a custom table that shows Row % with a row total count as the last column. I want it to look something like this Yes % No % DK% Total Count of row Complaint 10% 80% 10% 198 Incident 10% 80% 10% 315 Rebate 15% 60% 25% 3188 My syntax shows Row% the whole way through the table but I only want it displayed at the end. Any help would be appreciated. * Custom Tables. CTABLES /VLABELS VARIABLES=interaction_type R2 DISPLAY=LABEL /TABLE interaction_type [C] BY R2 [C][COUNT F40.0, ROWPCT.COUNT F40.1] /CATEGORIES VARIABLES=interaction_type R2 ORDER=A KEY=VALUE EMPTY=INCLUDE TOTAL=YES POSITION=AFTER MISSING=EXCLUDE. Thank you, Vanessa. ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
|
|
In reply to this post by Vanessa K
This is the draft tutorial I referred to: the alignment is slightly out, but you can see that it does what you want.
Elaboration with CTABLES [Draft only 30 x 2012] Correspondence on SPSS listserver with Jonathan Peck (Senior Programmer at IBM/SPSS) [Subject: In support of CTABLES] March 2012 ________________________________________ This exchange (and pouring rain) has prompted me to explore CTABLES with some of the examples in my tutorials. The CSR, whilst thorough in listing everything CT can do, is less than helpful for working out how to do what the user wants: the Help tutorials for CT inside SPSS are uninformative and sparse. Looks like 1972 all over again when Jim Ring and I had to write a series of new handouts (effectively a manual to the manual) for researchers who came to SSRC Survey Unit for advice and assistance. These detailed, in simple language, the various stages of survey data capture, file management and statistical analysis and explained how to do this with SPSS. We even joked about writing a “Clod’s Guide to Survey Analysis Using SPSS” They formed the basis for teaching notes on our Summer Schools in Survey Methods (1970-76) and, eventually, for my Survey Analysis Workshop (1976 – 92) at the then Polytechnic of North London. Apart from workshop exercises and supplementary explanations, we used Maria Norusis wonderful book from 1987 onwards (bought in bulk and sold at cost to students). Unless I can find a downloadable CT explanation and work-through for the kind of tables I need, it seems I’ll have to write something myself. I’ve been playing with CT on some of my course data and noted that it is very, very fast. What I need to do now is work backwards from the output I used to get using BREAKDOWN (always caused a laugh from my students when it was first mentioned). . . /CROSSBREAK (now superceded) to see if CT can produce it. I’m looking for a way to get this table: sexism sex ethnic White Other Total Mean N Mean N Mean N Boys 13.41 22 11.90 20 12.69 42 Girls 9.17 30 8.64 14 9.00 44 Total 10.96 52 10.56 34 10.80 86 . . . into a format which matches this blank table: sexism Sexism Mean (n) All White Other All ( ) ( ) ( ) Boys ( ) ( ) ( ) Girls ( ) ( ) ( ) [NB: This one is for means, but it applies equally to percentages for elaboration.] A quick Google search for SPSS CROSSBREAK produced this 2009 correspondence from Jon Peck, http://listserv.uga.edu/cgi-bin/wa?A2=ind0909&L=spssx-l&P=24774 from which I hope to be able to produce the following elaboration table: Sexism by Sex, controlling for Race Sexism Percent (n) All White Other All 10.8 (86) 11.0 (52) 10.6 (34) Boys 12.7 (42) 13.4 (22) 11.9 (20) Girls 9.0 (44) 9.2 (30) 8.6 (14) The main point about such tables is that the zero-order sample statistic appears top left, first order statistics in the 1st column or row, and second order statistics in the 2nd and 3rd columns or rows: right-to-left language users may prefer the table to be flipped horizontally. I would normally do this with percentages, following on from CROSSTABS, as it is a relatively simple way of demonstrating the true meaning of analysis by breaking down (or partitioning) a statistic into constituent parts, then forcing students to think about explanations for the emerging pattern and about other variables which might be introduced as 3rd order controls. Once the concept of a mean is introduced and understood, using similar summary tables, students can progress to further statistical tests. There are three international rugby matches on BBC this afternoon, so I’ve got less than two hours to modify Jon’s syntax and see what I come up with (and fit a sandwich in for lunch: just like being back at work!). From: SPSSX(r) On Behalf Of Jon K Peck This produces almost the table you want (labelling suppressed as in your example) CTABLES /VLABELS VARIABLES=salary DISPLAY=NONE /TABLE gender > salary [MEAN, COUNT PAREN40.0] BY minority /SLABELS POSITION=ROW VISIBLE=NO /CATEGORIES VARIABLES=gender minority TOTAL=YES POSITION=BEFORE. If you want to align the counts right, you can select those cells in the table editor and change the alignment. This could be automated with the SPSSINC MODIFY TABLES extension command. You could also add striping every other row via a tableLook or preference setting. Alternatively, put the counts in adjacent cells like this: CTABLES /VLABELS VARIABLES=salary DISPLAY=NONE /TABLE gender [C] > salary [S][MEAN, COUNT PAREN40.0] BY minority [C] /SLABELS VISIBLE=NO /CATEGORIES VARIABLES=gender minority TOTAL=YES POSITION=BEFORE . One comment jumps out at me "This table is easier to interpret, but we have lost the base for percentaging at the end of each row. Without special programming beyond the scope of this tutorial, SPSS cannot produce a table with n instead of 100%. A more useful table would look like this:" In the Summary Statistics subdialog (and, of course, in the TABLE subcommand) you can choose different statistics for totals from the items tabulated. For example, CTABLES /TABLE gender [C] BY jobcat [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES=gender jobcat TOTAL=YES POSITION=AFTER . gives you percents on the non-total cells and counts for the totals cells. Jon Peck (no "h") aka Kim Senior Software Engineer, IBM peck@us.ibm.com new phone: 720-342-5621 From: "John F Hall" <johnfhall@orange.fr> To: <SPSSX-L@LISTSERV.UGA.EDU> Cc: "'Poes, Matthew Joseph'" <mpoes@illinois.edu>, Jon K Peck/Chicago/IBM@IBMUS, "'David Marso'" <david.marso@gmail.com>, <Art@DrKendall.org>, "'ViAnn Beadle'" <vab88011@gmail.com> Date: 03/18/2012 04:13 PM Subject: RE: In support of CTABLES ________________________________________ Jon Thanks for this and apologies for the lengthy reply. I’ve not tried David’s solution yet, but that’s on my to do list. I’ve spent the last couple of days experimenting with CTABLES on a larger data set (my 1975 Quality of Life in Britain) as I’m more familiar with it and the variables are a close match for the example in the CSR (sex, marital status feeling very, fairly or not at all happy). I’ve done this for “happy” using %% both in marital by happy by sex format using CROSSTABS and also my elaboration summary table format which involves recoding to 0,100 and using MEANS. I’ve then attempted to repeat the analysis using CTABLES. As it happens I use almost the same example in tutorial 3.1.3 to introduce the simultaneous tabulation of two variables. This is the first time in the course that students will come across joint frequency distributions. (aka CROSSTABS) [Extract from tutorial] ________________________________________ We'll be using data from this survey to explore the relationship: Marital status → Feeling happy . . . or is it the other way round? Is this the true story, or are there any other variables, (related or unrelated to marital status) which might influence feeling happy? What might they be? How do they affect the relationship between marital status and feeling happy? Thus as well as dependent and independent variables, we also need to think of test variables to examine the initial relationship between marital status and feeling happy by controlling for the test variables. Does marital status affect feeling happy at all? These are the kind of questions which make survey research so interesting. ________________________________________ [The following examples use CROSSTABS and are from the Quality of Life in Britain survey, conducted by Mark Abrams and myself at the SSRC Survey Unit in 1975] Exercise 1: The sequence followed in class would produce output like this: Q.53 How [happy] are you these days? * Marital status of respondent Crosstabulation Count Marital status of respondent Total Single Married Widowed Divorced or separated Q.53 How [happy] are you these days? Not too happy 7 29 17 4 57 Fairly Happy 105 337 58 16 516 Very happy 38 283 23 9 353 Total 150 649 98 29 926 Q.53 How [happy] are you these days? * Marital status of respondent Crosstabulation Marital status of respondent Total Single Married Widowed Divorced or separated Q.53 How [happy] are you these days? Not too happy Count 7 29 17 4 57 % within Marital status of respondent 4.7% 4.5% 17.3% 13.8% 6.2% Fairly Happy Count 105 337 58 16 516 % within Marital status of respondent 70.0% 51.9% 59.2% 55.2% 55.7% Very happy Count 38 283 23 9 353 % within Marital status of respondent 25.3% 43.6% 23.5% 31.0% 38.1% Total Count 150 649 98 29 926 % within Marital status of respondent 100.0% 100.0% 100.0% 100.0% 100.0% This is a bit cluttered, so just use column %: Q.53 How [happy] are you these days? * Marital status of respondent Crosstabulation % within Marital status of respondent Marital status of respondent Total Single Married Widowed Divorced or separated Q.53 How [happy] are you these days? Not too happy 4.7% 4.5% 17.3% 13.8% 6.2% Fairly Happy 70.0% 51.9% 59.2% 55.2% 55.7% Very happy 25.3% 43.6% 23.5% 31.0% 38.1% Total 100.0% 100.0% 100.0% 100.0% 100.0% . . . but it's much easier to compare figures visually down columns rather than across rows. I prefer to have the dependent variable in the columns across the top of the table and the independent variable(s) in the rows down the side, as it's mcuh easier to compare percentages down columns than across rows, viz: marital * happy Crosstabulation % within marital happy Total Not too happy Fairly happy Very happy marital Married or cohabiting 6.5% 68.6% 24.8% 100.0% Single 3.2% 46.9% 49.9% 100.0% Widowed 11.7% 62.8% 25.5% 100.0% Separated or divorced 35.3% 55.9% 8.8% 100.0% Total 5.7% 52.2% 42.1% 100.0% This table is easier to interpret, but we have lost the base for percentaging at the end of each row. Without special programming beyond the scope of this tutorial, SPSS cannot produce a table with n instead of 100%. A more useful table would look like this: marital * happy Crosstabulation % within marital happy N = 100% Not too happy Fairly happy Very happy marital Married or cohabiting 6.5% 68.6% 24.8% 150 Single 3.2% 46.9% 49.9% 649 Widowed 11.7% 62.8% 25.5% 98 Separated or divorced 35.3% 55.9% 8.8% 29 Total 5.7% 52.2% 42.1% 926 The 3-way table for elaboration: Marital status of respondent * Q.53 How [happy] are you these days? * Sex of Respondent Crosstabulation % within Marital status of respondent Sex of Respondent Q.53 How [happy] are you these days? Total Not too happy Fairly Happy Very happy Men Marital status of respondent Single 5.3% 75.0% 19.7% 100.0% Married 5.2% 55.6% 39.2% 100.0% Widowed 26.7% 60.0% 13.3% 100.0% Divorced or separated 16.7% 66.7% 16.7% 100.0% Total 6.2% 59.7% 34.0% 100.0% Women Marital status of respondent Single 4.1% 64.9% 31.1% 100.0% Married 3.9% 49.0% 47.1% 100.0% Widowed 15.7% 59.0% 25.3% 100.0% Divorced or separated 13.0% 52.2% 34.8% 100.0% Total 6.1% 52.9% 41.0% 100.0% Total Marital status of respondent Single 4.7% 70.0% 25.3% 100.0% Married 4.5% 51.9% 43.6% 100.0% Widowed 17.3% 59.2% 23.5% 100.0% Divorced or separated 13.8% 55.2% 31.0% 100.0% Total 6.2% 55.7% 38.1% 100.0% Again, it would be more useful if it looked like this: Sex of Respondent * Q.53 How [happy] are you these days? * Marital status of respondent Crosstabulation % within Sex of Respondent Marital status of respondent Q.53 How [happy] are you these days? N = 1000% Not too happy Fairly Happy Very happy Single Sex of Respondent Men 5.3% 75.0% 19.7% 76 Women 4.1% 64.9% 31.1% 74 Total 4.7% 70.0% 25.3% 150 Married Sex of Respondent Men 5.2% 55.6% 39.2% 288 Women 3.9% 49.0% 47.1% 361 Total 4.5% 51.9% 43.6% 649 Widowed Sex of Respondent Men 26.7% 60.0% 13.3% 15 Women 15.7% 59.0% 25.3% 83 Total 17.3% 59.2% 23.5% 98 Divorced or separated Sex of Respondent Men 16.7% 66.7% 16.7% 93 Women 13.0% 52.2% 34.8% 15 Total 13.8% 55.2% 31.0% 29 Total Sex of Respondent Men 6.2% 59.7% 34.0% 385 Women 6.1% 52.9% 41.0% 541 Total 6.2% 55.7% 38.1% 926 This my starting point for CTABLES. I’ve replicated the above exercises using CTABLES, but it took me a very long time to get used to the displays and routing. I’m beginning to get the hang of it, but I still think it’s far too complicated for the kind of students I taught and the timetable constraints they (and I) faced. Full-time postgrads and early career research staff have fewer constraints on their time. I took many a wrong path on the way, as would many of my students. This was one reason we abandoned a teaching experiment using SPSS PC+ on PCs: the students finished up all over the place and we reverted to SPSS-X on the mainframe using syntax on VDUs. That said I have started on a draft tutorial for CTABLES with much more explanation than the CSR or the on-line help, and with the addition of step-by-step screenshots. On the way I discovered some really nice features, such as the table layout previews, especially the ability to drag variables around from these to rows or columns to see what the table would look like. I found it irritating that the display reverted to the top of the file when starting a new analysis rather than going back to where I left off. I have yet to work out how to get row and column totals (and %%) into tables. CTABLES /VLABELS VARIABLES=marital sex happy DISPLAY=DEFAULT /TABLE marital > sex BY happy [C] [rowpct f3.1] /CATEGORIES VARIABLES=marital sex happy ORDER=A KEY=VALUE EMPTY=INCLUDE. Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Row N % Row N % Row N % Marital status of respondent Single Sex of Respondent Men 5.3 75.0 19.7 Women 4.1 64.9 31.1 Married Sex of Respondent Men 5.2 55.6 39.2 Women 3.9 49.0 47.1 Widowed Sex of Respondent Men 26.7 60.0 13.3 Women 15.7 59.0 25.3 Divorced or separated Sex of Respondent Men 16.7 66.7 16.7 Women 13.0 52.2 34.8 This table needs an additional column giving base N for % in each row, which I can do by hand, but I’ll try to do it in CTABLES Example 2: Summary table recode happy (3 = 100) (1,2 =0)(else = sysmis) into happy2. var level happy2 (scale). * Custom Tables. CTABLES /VLABELS VARIABLES=sex marital happy2 DISPLAY=DEFAULT /TABLE sex [C] BY marital [C] > happy2 [S][MEAN] /CATEGORIES VARIABLES=sex [1, 2, OTHERNM] EMPTY=INCLUDE /CATEGORIES VARIABLES=marital [1, 2, 3, 4, OTHERNM] EMPTY=INCLUDE. Marital status of respondent Single Married Widowed Divorced or separated happy2 happy2 happy2 happy2 Mean Mean Mean Mean Sex of Respondent Men 19.74 39.24 13.33 16.67 Women 31.08 47.09 25.30 34.78 So far, so good, but I want a table that looks something like this (edited rather clumsily in Word): This is a mess!! Sex of Respondent Men % 34.03 19.74 39.24 13.33 16.67 n=100% 385 76 288 15 6 Marital status of respondent All Single Married Widowed Divorced or separated All % 38.12 25.33 43.61 23.47 31.03 n=100% 926 150 649 98 29 n=100% 541 74 361 83 23 I’d prefer % as f3.1 From this table we can calculate epsilons -7.0 -11.3 -7.9 -12.0 -18.1 . . and begin to discuss how to interpret these figures and what other variables might be included. A much better example is the difference in earnings between men and women at zero order and when controlling for other variables such as qualifications, full-time or part-time, employee or self-employed etc., bu that is a lot of work. Like I said, sorry for the length of this, but it gives you a better idea of what I’m trying to do. John Email: johnfhall@orange.fr Website: www.surveyresearch.weebly.com Skype: surveyresearcher1 Phone: (+33) (0) 2.33.45.91.47 1: Zero order ctables /tables by happy [c] [ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES=happy TOTAL=YES POSITION=AFTER . Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count 6.2% 55.7% 38.1% 926 2: First order CTABLES /TABLE marital [C] BY happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital happy TOTAL=YES POSITION=AFTER . Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count Marital status of respondent Single 4.7% 70.0% 25.3% 150 Married 4.5% 51.9% 43.6% 649 Widowed 17.3% 59.2% 23.5% 98 Divorced or separated 13.8% 55.2% 31.0% 29 Total 6.2% 55.7% 38.1% 926 CTABLES command missing here. Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count Sex of Respondent Men 6.2% 59.7% 34.0% 385 Women 6.1% 52.9% 41.0% 541 Total 6.2% 55.7% 38.1% 926 The format of these tables is exactly what I want for earlier tutorials (and for reports): it’s a shame I can’t get them using CROSSTABS. 3: Second order CTABLES /TABLE marital [C] BY happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital happy TOTAL=YES POSITION=AFTER . Q.53 How [happy] are you these days? Total Sex of Respondent Total Men Women Count Count Count Marital status of respondent Total 926 385 541 Single 150 76 74 Married 649 288 361 Widowed 98 15 83 Divorced or separated 29 6 23 OOPS!! 45 year old Algol programming instinct made me try: CTABLES /TABLE marital [C] > sex [c] by happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital sex happy TOTAL=YES POSITION=after . Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count Marital status of respondent Single Sex of Respondent Men 5.3% 75.0% 19.7% 76 Women 4.1% 64.9% 31.1% 74 Total 4.7% 70.0% 25.3% 150 Married Sex of Respondent Men 5.2% 55.6% 39.2% 288 Women 3.9% 49.0% 47.1% 361 Total 4.5% 51.9% 43.6% 649 Widowed Sex of Respondent Men 26.7% 60.0% 13.3% 15 Women 15.7% 59.0% 25.3% 83 Total 17.3% 59.2% 23.5% 98 Divorced or separated Sex of Respondent Men 16.7% 66.7% 16.7% 6 Women 13.0% 52.2% 34.8% 23 Total 13.8% 55.2% 31.0% 29 Total Sex of Respondent Men 6.2% 59.7% 34.0% 385 Women 6.1% 52.9% 41.0% 541 Total 6.2% 55.7% 38.1% 926 CTABLES /TABLE sex [c] > marital [C] by happy [C] [ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital sex happy TOTAL=YES POSITION=after . Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count Sex of Respondent Men Marital status of respondent Single 5.3% 75.0% 19.7% 76 Married 5.2% 55.6% 39.2% 288 Widowed 26.7% 60.0% 13.3% 15 Divorced or separated 16.7% 66.7% 16.7% 6 Total 6.2% 59.7% 34.0% 385 Women Marital status of respondent Single 4.1% 64.9% 31.1% 74 Married 3.9% 49.0% 47.1% 361 Widowed 15.7% 59.0% 25.3% 83 Divorced or separated 13.0% 52.2% 34.8% 23 Total 6.1% 52.9% 41.0% 541 Total Marital status of respondent Single 4.7% 70.0% 25.3% 150 Married 4.5% 51.9% 43.6% 649 Widowed 17.3% 59.2% 23.5% 98 Divorced or separated 13.8% 55.2% 31.0% 29 Total 6.2% 55.7% 38.1% 926 There’s rather too much data in these tables: they might be more informative if the (sub) Total rows were dropped, leaving just the Total column. as below, but tidier: Q.53 How [happy] are you these days? Not too happy Fairly Happy Very happy Total Row N % Row N % Row N % Count Total 6.2% 55.7% 38.1% 926 Sex of respondent Men 6.2% 59.7% 34.0% 385 Women 6.1% 52.9% 41.0% 541 Marital status of respondent Single Men 5.3% 75.0% 19.7% 76 Women 4.1% 64.9% 31.1% 74 Married Men 5.2% 55.6% 39.2% 288 Women 3.9% 49.0% 47.1% 361 Widowed Men 26.7% 60.0% 13.3% 15 Women 15.7% 59.0% 25.3% 83 Divorced or separated Men 16.7% 66.7% 16.7% 6 Women 13.0% 52.2% 34.8% 23 . . . or perhaps analyse only one category at a time to generate smaller tables. The next step would be to take a criterion value for the dependent variable, ie value 1 (Not too happy) or value 3 (Very happy) and produce a summary table as in my earlier mail. In that, I used “Very happy”, but being happy is normative, so I suspect it would be more interesting to pursue the “Not too happy” category. I still need to check out David’s syntax, but now that I’ve been let loose with a new toy, preparation of and planting in the vegetable garden will be a less attractive proposition: the next few days are going to be fun. |
|
|
In reply to this post by Vanessa K
Not much good on Nabble, so here's a shot at copying the tutorial. Elaboration with CTABLES [Draft only 30 x 2012] Correspondence on SPSS listserver with Jonathan Peck (Senior Programmer at IBM/SPSS) [Subject: In support of CTABLES] March 2012 This exchange (and pouring rain) has prompted me to explore CTABLES with some of the examples in my tutorials. The CSR, whilst thorough in listing everything CT can do, is less than helpful for working out how to do what the user wants: the Help tutorials for CT inside SPSS are uninformative and sparse.
sexism
[NB: This one is for means, but it applies equally to percentages for elaboration.] A quick Google search for SPSS CROSSBREAK produced this 2009 correspondence from Jon Peck, Sexism by Sex, controlling for Race
This produces almost the table you want (labelling suppressed as in your example)
[Extract from tutorial] We'll be using data from this survey to explore the relationship:
. . . but it's much easier to compare figures visually down columns rather than across rows.
This is a mess!!
1: Zero order ctables /tables by happy [c] [ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES=happy TOTAL=YES POSITION=AFTER .
2: First order CTABLES /TABLE marital [C] BY happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital happy TOTAL=YES POSITION=AFTER .
CTABLES command missing here.
The format of these tables is exactly what I want for earlier tutorials (and for reports): it’s a shame I can’t get them using CROSSTABS. 3: Second order CTABLES /TABLE marital [C] BY happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital happy TOTAL=YES POSITION=AFTER .
OOPS!! 45 year old Algol programming instinct made me try: CTABLES /TABLE marital [C] > sex [c] by happy [C][ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital sex happy TOTAL=YES POSITION=after .
CTABLES /TABLE sex [c] > marital [C] by happy [C] [ROWPCT.COUNT TOTALS[COUNT]] /CATEGORIES VARIABLES= marital sex happy TOTAL=YES POSITION=after .
There’s rather too much data in these tables: they might be more informative if the (sub) Total rows were dropped, leaving just the Total column. as below, but tidier:
. . . or perhaps analyse only one category at a time to generate smaller tables. The next step would be to take a criterion value for the dependent variable, ie value 1 (Not too happy) or value 3 (Very happy) and produce a summary table as in my earlier mail. In that, I used “Very happy”, but being happy is normative, so I suspect it would be more interesting to pursue the “Not too happy” category. John F Hall (Mr) [Retired academic survey researcher] Email: [hidden email] Website: www.surveyresearch.weebly.com SPSS start page: www.surveyresearch.weebly.com/spss-without-tears.html -----Original Message----- Hi there, I am trying to produce a custom table that shows Row % with a row total count as the last column. I want it to look something like this Yes % No % DK% Total Count of row Complaint 10% 80% 10% 198 Incident 10% 80% 10% 315 Rebate 15% 60% 25% 3188 My syntax shows Row% the whole way through the table but I only want it displayed at the end. Any help would be appreciated. * Custom Tables. CTABLES /VLABELS VARIABLES=interaction_type R2 DISPLAY=LABEL /TABLE interaction_type [C] BY R2 [C][COUNT F40.0, ROWPCT.COUNT F40.1] /CATEGORIES VARIABLES=interaction_type R2 ORDER=A KEY=VALUE EMPTY=INCLUDE TOTAL=YES POSITION=AFTER MISSING=EXCLUDE. Thank you, Vanessa. ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Free forum by Nabble | Edit this page |