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# Topic 13 con pattern spss

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### Topic 13 con pattern spss

1. 1. Srinivasulu Rajendran Centre for the Study of Regional Development (CSRD)Jawaharlal Nehru University (JNU) New Delhi India r.srinivasulu@gmail.com
2. 2. Objective of the session To understand consumption pattern through software packages
3. 3. 1. How to Analyze consumptionpattern?2. What are procedure availablefor estimating consumptionpattern and how to do withEconometric software
4. 4. Two-way ANOVA using SPSS The two-way ANOVA compares the mean differences between groups that have been split on two independent variables (called factors). You need two independent, categorical variables and one continuous, dependent variable .
5. 5. Objective We are interested in whether an monthly per capita food expenditure was influenced by their level of education and their gender head. Monthly per capita food expenditure with higher value meaning a better off. The researcher then divided the participants by gender head of HHs i.e Male head & Female head HHs and then again by level of education.
6. 6.  In SPSS we separated the HHs into their appropriate groups by using two columns representing the two independent variables and labelled them “Head_Sex" and “Head_Edu". For “head_sex", we coded males as "1" and females as “0", and for “Head_Edu", we coded illiterate as "1", can sign only as "2" and can read only as "3“ and can read & write as “4”. Monthly per capita food expenditure was entered under the variable name, “pcmfx".
7. 7. How to correctly enter your data into SPSS in order torun a two-way ANOVA
8. 8. Testing of Assumptions In SPSS, homogeneity of variances is tested using Levenes Test for Equality of Variances. This is included in the main procedure for running the two- way ANOVA, so we get to evaluate whether there is homogeneity of variances at the same time as we get the results from the two-way ANOVA.
9. 9. Perform the two-anova test procedure which is explained in the previous session.
11. 11. Tests of Between-Subjects Effects Dependent Variable:Per capita monthly food expenditure (taka) Type III Sum of Source Squares df Mean Square F Sig.Corrected Model 10669432 6 1778239 6.773 .000 Intercept 279013110 1 279013110 1062.753 .000 head_sex 46145 1 46145 .176 .675 head_edu 5527869 3 1842623 7.019 .000 head_sex * 197900 2 98950 .377 .686 head_edu Error 322396593 1228 262538 Total 1708644528 1235Corrected Total 333066026 1234
12. 12. Multiple Comparisons Table
13. 13. Multiple Comparisons Per capita monthly food expenditure (taka) Tukey HSD 95% Confidence Interval (J) (sum) MeanWe can see from the table that (I) (sum) head_ed Difference (I- Lower Upperthere is some repetition of the head_edu 1 u 2 J) -50.5163 Std. Error 42.12953 Sig. .628 Bound Bound -158.8968 57.8641results but, regardless of 3 85.0395 118.47081 .890 -219.7329 389.8118which row we choose to read *from, we are interested in the 4 -200.2444 36.46704 .000 -294.0578 -106.4310differences between (1) 2 1 50.5163 42.12953 .628 -57.8641 158.8968illiterate, (2) can sign, (3) can 3 135.5558 118.29353 .661 -168.7605 439.8721read, (4) can read & write. 4 -149.7281 * 35.88692 .000 -242.0491 -57.4071From the results we can see 3 1 -85.0395 118.47081 .890 -389.8118 219.7329that there is a significant 2 -135.5558 118.29353 .661 -439.8721 168.7605difference between selecteddifferent combinations of 4 -285.2839 116.39719 .068 -584.7218 14.1540educational level (P < .0005). 4 1 200.2444 * 36.46704 .000 106.4310 294.0578 * 2 149.7281 35.88692 .000 57.4071 242.0491 3 285.2839 116.39719 .068 -14.1540 584.7218
14. 14. Homogeneous Subsets Per capita monthly food expenditure (taka) Tukey HSDa,,b,,c (sum) Subset N head_edu 1 2 3 20 858.3107 1 289 943.3501 943.3501 2 303 993.8665 993.8665 4 623 1143.5946 Sig. .409 .101Overall, both subset shows insignificant, there was no homogeneous among subsets
15. 15. Plot of the Results
16. 16.  The following plot is not of sufficient quality to present in your reports but provides a good graphical illustration of your results. In addition, we can get an idea of whether there is an interaction effect by inspecting whether the lines are parallel or not.
17. 17. From this plot wecan see how ourresults from theprevious tablemight makesense. Rememberthat if the linesare not parallelthen there is thepossibility of aninteraction takingplace.
18. 18. Procedure for Simple Main Effectsin SPSS You can follow up the results of a significant interaction effect by running tests for simple main effects - that is, the mean difference in monthly per capita food expenditure between head of gender HHs at each education level. SPSS does not allow you to do this using the graphical interface you will be familiar with, but requires you to use syntax.
19. 19. Step 1
20. 20. Click File > New > Syntax from the main menu as shown below
21. 21. You will be presented with the Syntax Editor as shown below:  Type text into the syntax editor so that you end up with the following (the colours are automatically added):  [Depending on the version of SPSS you are using you might have suggestion boxes appear when you type in SPSS- recognised commands, such as, UNIANOVA. If you are familiar with using this type of auto-prediction then please feel free to do so, but otherwise simply ignore the pop-up suggestions and keep typing normally