IBM SPSS 19 manual

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Buen manual de instrucciones

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Desafortunadamente pocos usuarios destinan su tiempo a leer manuales IBM SPSS 19, sin embargo, un buen manual nos permite, no solo conocer una cantidad de funcionalidades adicionales del dispositivo comprado, sino también evitar la mayoría de fallos.

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Sobre todo, un manual de instrucciones IBM SPSS 19 debe contener:
- información acerca de las especificaciones técnicas del dispositivo IBM SPSS 19
- nombre de fabricante y año de fabricación del dispositivo IBM SPSS 19
- condiciones de uso, configuración y mantenimiento del dispositivo IBM SPSS 19
- marcas de seguridad y certificados que confirmen su concordancia con determinadas normativas

¿Por qué no leemos los manuales de instrucciones?

Normalmente es por la falta de tiempo y seguridad acerca de las funcionalidades determinadas de los dispositivos comprados. Desafortunadamente la conexión y el encendido de IBM SPSS 19 no es suficiente. El manual de instrucciones siempre contiene una serie de indicaciones acerca de determinadas funcionalidades, normas de seguridad, consejos de mantenimiento (incluso qué productos usar), fallos eventuales de IBM SPSS 19 y maneras de solucionar los problemas que puedan ocurrir durante su uso. Al final, en un manual se pueden encontrar los detalles de servicio técnico IBM en caso de que las soluciones propuestas no hayan funcionado. Actualmente gozan de éxito manuales de instrucciones en forma de animaciones interesantes o vídeo manuales que llegan al usuario mucho mejor que en forma de un folleto. Este tipo de manual ayuda a que el usuario vea el vídeo entero sin saltarse las especificaciones y las descripciones técnicas complicadas de IBM SPSS 19, como se suele hacer teniendo una versión en papel.

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Sobre todo es en ellos donde encontraremos las respuestas acerca de la construcción, las posibilidades del dispositivo IBM SPSS 19, el uso de determinados accesorios y una serie de informaciones que permiten aprovechar completamente sus funciones y comodidades.

Tras una compra exitosa de un equipo o un dispositivo, vale la pena dedicar un momento para familiarizarse con cada parte del manual IBM SPSS 19. Actualmente se preparan y traducen con dedicación, para que no solo sean comprensibles para los usuarios, sino que también cumplan su función básica de información y ayuda.

Índice de manuales de instrucciones

  • Página 1

    1 Using IBM SPSS 19* Descriptive Statistics SPSS Help. SPSS has a good online help system. Once SPSS is up and running, you can nd it by going to Help>T opics in the menu bar , i.e., click Help in the menu bar and then click T opics in the drop window that opens. Y ou will now be in the help contents window . Click T utorial . _______________[...]

  • Página 2

    2 Y ou can then open any of the books comprising the tutorial by clicking on the + to get to the various subtopics. Once in a subtopic is open, you can just keep clicking on the right and left arrows to move through it page by page. I suggest going through the entire Overview booklet. Once you are working with a data set, and have an idea of what y[...]

  • Página 3

    3 Sorting the Data . From the menu, choose Data>Sort Cases… , click the right arrow to move protein to the Sort by box, make sure Ascending is chosen, and click OK . Our data column is now in ascending order . However , the rst thing that come up is an output page telling you what has happened. Click the table with the Star on it to get bac[...]

  • Página 4

    4 Click the Statistics... button, then make sure Descriptives and Percentiles are checked. W e will use 95% for Condence Interv al for Mean . Click Continue . Then click Plots... . Under Bo xplots , select F actor levels together , and under Descriptiv e , choose both Stem-and-leaf and Histogr am . Then click Continue .[...]

  • Página 5

    5 Then click OK . This opens an output window with two frames. The frame on the left contains an outline of the data on the right.[...]

  • Página 6

    6 The Standard Error of the Mean is a measure of how much the value of the mean may vary from repeated samples of the same size taken from the same distribution. The 95% Condence Interv al for Mean are two numbers that we would expect 95% of the means from repeated samples of the same size to fall between. The 5% T rimmed Mean is the mean after [...]

  • Página 7

    7[...]

  • Página 8

    8 Now click on a number on the horizontal axis and then click on Number Format . In the diagram to the left below , we see that we have 2 decimal places. The values in this window can be changed as desired. Next, click on one of the bars and then Binning in the Properties window . Suppose we want bars of width 20 beginning at 30. Check Custom , Int[...]

  • Página 9

    9 Next choose P ercentiles from either output frame. The following comes up. Obviously , there are two dif ferent methods at work here. The formulas are given in the SPSS Algorithms Manual . T ypically , use the W eighed A verage . T ukey’ s Hinges was designed by T ukey for use with the boxplot. The box covers the Interquartile range (IQR) = Q 7[...]

  • Página 10

    10 Then click back to Data View . From the menu, choose T ransform>Compute V ariable... . When the Compute V ariable window comes up, click Reset , and type cum_bin in the box labeled T arget V ariable . Scroll down the Function group: window to CDF & Noncentral CDF to select it, then scroll to and select Cdf .Binom in the Functions and Spec[...]

  • Página 11

    1 1 Poisson Distribution. Let us assume that l =.5. W e will rst nd P(X ≤ x | .5)for x = 0, ..., 15, i.e., the cumula- tive probabilities. First put the numbers 0 through 15 in a column of a worksheet. (W e have already done this above. Again, you only need to enter the numbers whose cumulative probability you desire.) Then click V ari- abl[...]

  • Página 12

    12 cumulative Poisson probabilities are now found in the column cum_pois. Now we want to put the individual Poisson probabilities into the column pois_pro . Do basically the same as above, except make the T arget V ariable “ pois_pro ,” and the Numeric Expression “ CDF . POISSON(number ,.5) - CDF .POISSON(number-1,.5) .” The Data View now l[...]

  • Página 13

    13 The probability is now found in the column cum_norm . Staying with the normal distribution with mean 100 and standard deviation 20, suppose we with to nd P(90 ≤ X ≤135). Do as above except make the T arget V ariable “ int_norm ,” and the Numeric Expression “ CDF . NORMAL(135,100,20) - CDF .NORMAL(90,100,20) .” The probability is n[...]

  • Página 14

    14 Condence Intervals and Hypothesis T esting Using t A Single Population Mean . W e found earlier that the sample mean of the data given on page 2, which you may have saved under the name protein.sav , is 73.3292 to four decimal places. W e wish to test whether the mean of the population from which the sample came is 70 as opposed to a true mea[...]

  • Página 15

    15 SPSS gives us the basic descriptives in the rst table. In the second table, we are given that the t -value for our test is 1.110 . The p -value (or Sig. (2-tailed) ) is given as .272 . Thus the p -value for our one-tailed test is one- half of that or .136 . Based on this test statistic, we would not reject the null hypothesis, for instance, f[...]

  • Página 16

    16 and again press Add . Then hit OK and complete the V ariable View as follows. Returning to Data View gives a window whose beginning looks like that below . Now we wish to test the hypotheses H 0 : m 1 - m 2 = 0 H a : m 1 - m 2 ≠ 0 where m 1 refers to the population mean for the non-smokers and m 2 refers to the population mean for the smokers.[...]

  • Página 17

    17 Then click Continue . As before, click Options... , enter 95 (or any other number) for Condence Inter- val , and again click Continue followed by OK . The rst table of output gives the descriptives. T o get the second table as it appears here, I rst double-clicked on the Independent Samples T est table, giving it a fuzzy border and brin[...]

  • Página 18

    18 discount this hypothesis, so we will take our results from the Equal V ariances Assumed column. W e see that, with 30 degrees of freedom, we have t =-2.468 and p =.020, so we reject the null hypothesis H 0 : m 1 - m 2 = 0 at the a =.05 level of signicance. That we would reject this null hypothesis can also be seen in that the 95% Con- denc[...]

  • Página 19

    19 The rst output table gives the descriptives and a second (not shown here) gives a correlation coefcient. From the third table, which has been pivoted to interchange rows and columns, we see that we have a t -score of 12.740. The fact that Sig.(2-tailed) is given as .000 really means that it is less than .001. Thus, for our one-sided test, [...]

  • Página 20

    20 H 0 : m N = m F = m C H a : Not all of m N , m F , and m C are equal. From the menu we choose Analyze>Compare Means>One- W ay ANOV A... . In the window that opens, place volume under Dependent List and Smok er[smoking] u nder F actor . Then click P ost Hoc... For a post-hoc test, we will only choose T ukey (T ukey's HSD test) with Sig[...]

  • Página 21

    21 Then we click options and choose Descriptive , Homogeneity of variance test , and Means plot . The Homogeneity of v ariance test calculates the Levene statistic to test for the equality of group variances. This test is not dependent on the assumption of normality . The Brown-Forsythe and W elch statistics are better than the F statistic if the a[...]

  • Página 22

    22 The results of the T est of Homogeneity of V ariances is nonsignicant since we have a p value of .974 , showing that there is no reason to believe that the variances of the three groups are different from one another . This is reassuring since both ANOV A and T ukey's HSD have equal variance assumptions. W ithout this reassur- ance, inte[...]

  • Página 23

    23 Simple Linear Regression and Corr elation W e will use the following 109 x-y data pairs for simple linear regression and correlation. The x 's are waist circumferences (cm) and the y 's are measurements of deep abdominal adipose tissue gathered by CA T scans. Since CA T scans are expensive, the goal is to nd a predictive equation. F[...]

  • Página 24

    24 Then click OK to get the following scatter plot, which leads us to suspect that there is a signicant linear relation- ship. Regression. T o explore this relationship, choose Analyze>R egression>Linear ... from the menu, select and move y under Dependent and x under Independent(s) .[...]

  • Página 25

    25 Then click Statistics... , and in the window that opens with Estimates and Model t already checked, also check Condence interv als and Descriptives . Then click Continue . Next click Plots... . In the window that opens, enter *ZRESID for Y and *ZPRED for X to get a graph of the standardized residuals as a function of the standardized predi[...]

  • Página 26

    26 Then click Continue followed by OK to get the output. W e rst see the mean and the standard deviation for the two variables in the Descriptive Statistics . In the Model Summary , we see that the bivariate correlation coefcient r ( R ) is .819, indicating a strong positive linear relationship between the two variables. The coefcient of d[...]

  • Página 27

    27 reject the null hypothesis of b =0. W e now return to the scatter plot. Double click on the plot to bring up the Chart Editor and choose Options>Y Axis R eference Line from the menu. In the window that opens, select Refernce Line and, from the drop- down menue for Set to: , choose Mean and then click Apply . Next, from the Chart Editor menu, [...]

  • Página 28

    28[...]

  • Página 29

    29 for the mean value m y|74.5 is ( 32.41572, 52.72078) , corresponding to the limits of the inner bands at x=74.5 in the scatter plot, and the 95% condence interval for the individual value y I (74.5)is (-23.7607,108.8972), correspond- ing to the limits of the outer bands at x =74.5. The rst pair of acronyms lmci and umci stand for “lower [...]

  • Página 30

    30 W e see again that the Pearson Correlation r is .819, and from the Sig. of .000, we know that the p -value is less than .001 and so we would reject a null hypothesis of r =0. Multiple Regr ession W e will use the following data set for multiple linear regression. In this data set, required ram , amount of input , and amount of output , all in ki[...]

  • Página 31

    31 Choose Analyze>R egression>Linear ... from the menu, select and move minutes under Dependent and ram , input , and output , in that order , under Independent(s) . Then ll in the options for Statistics , Plots , and Save exactly as you did for simple linear regression. Finally , click OK to get the output. W e rst see the mean and the[...]

  • Página 32

    32 1.049x 3 . From the last two rows of numbers in the table, one gets that 95% condence intervals are (-.694,2.645) for a , (.061,.138) for b 1 , (.000,.487) for b 2 , and (.692,1.407) for b 3 . The t test is used for testing the various null hypotheses b i =0. It can be used similarly to test the null hypothesis a =0, but this is of much less [...]

  • Página 33

    33 Finally , consider the residual plot below . On the horizontal axis are the standardized y values from the data points, and on the vertical axis are the standardized residuals for each such y . If all the regression assumptions were met for our data set, we would expect to see random scattering about the horizontal line at level 0 with no notica[...]

  • Página 34

    34 Then click back to Data View . From the menu, choose T ransform>Compute V ariable... . When the Compute V ariable window comes up, click R eset , then type lny in the box labeled T arget V ariable . Then scroll down the Function Group window to Arithmetic and then down the Functions and Special V ariables window to Ln to select it and press t[...]

  • Página 35

    35 Choosing a Model using Curve Estimation. T o nd an appropriate model for a given data set, such as the one in the previous section, choose Analyz e>Regression>Curve Estimation... . In the Curve Estimation window that opens, enter y under Dependent(s) , x under Independent with V ariable selected, and make sure Include constant in equati[...]

  • Página 36

    36 Finally , click OK . W e show below the output for the Quadratic model. The regression equation is ŷ=336.790- 693.691x+295.521x 2 . The other data, although arranged differently , is similar to that for linear and multiple regression. W e do note that the Standard Error is 1 1 1.856. Although they are not shown here, the regression equation for[...]

  • Página 37

    37 Chi-Squar e T est of Independence For data, we will use a survey of a sample of 300 adults in a certain metropolitan area where they indicated which of three policies they favored with respect to smoking in public places. W e wish to test if there is a relationship between education level and attitude to- ward smoking in public places. W e test [...]

  • Página 38

    38 This is not very well documented, but the rst thing we need to do for c 2 is to tell SPSS which column contains the frequency counts. Choose Data>W eight Cases... from the menu, and in the window that opens, choose W eight cases by and move the variable count under Frequency V ariable . Then click OK . Now choose Analyze>Descriptive Sta[...]

  • Página 39

    39 Check Observed and Expected under Counts , followed by Continue and OK . The rst table of output simply provides a table of the Counts and the Expected Counts if the variables are independent. From the second table, the Pearson Chi-Square statistic is 22.502 with a p -value ( Asymp . Sig. (2-sid- ed) ) of .001. Thus, for instance, we would re[...]

  • Página 40

    40 From the menu, choose Analyze>Nonpar ametric T ests>Legacy Dialogs>2 Related Samples... . In the window that opens, rst click output followed by the arrow to make it V ariable 1 for Pair 1 , then con- stant followed by the arrow to make it V ariable 2 . Make sure Wilcoxon is checked. If you want descriptive statistics and/or quartile[...]

  • Página 41

    41 The Z in the second table is the standardized normal approximation to the test statistic, and the Asymp. Sig (2-tailed) of .140, which we will use as our p -value, is estimated from the normal approximation. Because of the size of this p -value, we will not reject the null hypothesis at any of the usual levels of signicance. The Mann-Whitney [...]

  • Página 42

    42 Put 1 in the box for Group 1 and 2 in the box for Group 2 . Then click Continue . Y ou may click Options... if you want the output to include descriptive statistics and/or quartiles. Finally , click OK to get the output. W e see from the rst table, after ranking the hemoglob values from least to greatest, the Mean R ank and Sum of R anks for [...]

  • Página 43

    43 T o create the control chart(s), click Analyze>Quality Control>Control Charts... from the menu bar , and in the window that opens, select X -Bar , R, s under V ariable Charts and make sure Cases are units is checked under Data Organization . Then click Dene , and in the new window that opens, move g_per_l under Process Measurement and d[...]

  • Página 44

    44 Click Options , and enter 2 for Number of Sigmas . After clicking Continue, since we have specications for the mean, we click Statistics... , and in the window that opens, based on our specied mean and standard deviation, enter 50.756 for Upper and 49.244, Lower for Specication Limits , and 50 for T arget . Then select Estimate using S-[...]

  • Página 45

    45 Control Charts for the Proportion. T o illustrate control charts for the proportion, we use the number of defec- tives in samples of size 100 from a production process for twenty days in August. August: 6 7 8 9 10 1 1 12 13 14 15 Defectives: 8 15 12 19 7 12 3 9 14 10 August: 16 17 18 19 20 21 22 23 24 25 Defectives: 22 13 10 15 18 1 1 7 15 24 2 [...]

  • Página 46

    46 Now click Options, and enter 3 for Number of Sigmas . Then click Continue followed by OK to get the control chart, which is again pretty much self-explanatory . W e see that the process is out of control on August 24 and 25, although it is hard to call too few defectives out of control.[...]