Crosstab Report and Chi Square Test using SPSS

crosstab and chi-square statistics using spss now what is crosstab report if you want to analyze the frequency of response between two variables you can use crosstab reports now what it does is that crosstab help produce summary results in the form of a table where data is grouped at an intersection point the data in crosstab reports is summarized in the form of rows and column and at the intersection of rows and column you've got a data value providing a summary of the two variables or the frequency of the two variables the crosstab procedure forms two-way and multi-way tables if you want to found multi-way tables you will have to use layers now what does layers do they actually help you categorize the output for example if you specify a row a column and a layer factor which is termed as a control variable the cross tabs procedures forms one panel of associated statistics and measures for each value of the layer factor we'll be looking at this through an example as well for example if gender is a layer factor for a table of merit there are two variables okay one is married with two values yes and no and the other variable is life with values is life exciting routine or dull now since gender is our layer factor the results show separate association between merit and life for each gender for instance male and female an important test associated with crosstab reports is chi square test of association now chi-square test of association is used when you want to check association between two categorical variables on nominal scale however it is important to note that in the case of two variables being compared the test can also be interpreted as determining if there is a difference between the two variables so you can use your chi-square test of association as a test of difference if your variables are on nominal scale now the test is also referred to as chi square test of independence also pearson chi square test when you can use chi square test so following are the few scenarios in which chi square test is appropriate a business research teacher would like to know whether gender male or female is associated with preferred type of learning methods now in both cases your both variables are on nominal scale there is no order between the values so there are on nominal scale a group of students who are classified in terms of their personality introvert or extrovert one variable personality and in terms of color preference red yellow green or blue with the purpose of seeing whether there is an association relationship between personality and color preference both of them on nominal scale a car manufacturing company would like to know if there is an association between different makes of the car and the gender whether a particular gender prefers a particular make of the car so in each of these scenarios both the variables are on nominal scale and you want to test the association between these variables in this case we will use chi square test of association and finally a market researcher would like to know if a particular brand of watches is associated with a particular gender and in this case you can use chi square test of association as well now let's go for our example in order to run crosstab analysis we will go to analyze descriptive statistics cross tabs and i've already got that here so let's uh change it okay this is how you will see let's put personality as row and preference as column you can change the order of row and columns as well um it's it's depend it depends on your own readability do not do anything else for now just press ok and here are your results and is 150 the percentage valid none missing so here are your results personality into preference cross tabulation now introverts and extrovert these are two personality types and for red color there are 13 people with introvert personalities their preference is red nine people with extra word personality their preference is red and same is for yellow green blue so you've got you've divided your categories that is preference and personality into cross tabulation and at the intersection of these data values you've got a summary statistic a frequency for instance let's say 13 people who've got an uh an introvert personality have got color preference for blue same is for extrovert as well now let's use the layer variable so how do you use the layer variable same go to analyze descriptive statistics cross tab and what we'll do is we'll put city as a layer and we'll press ok and here are your results now the preference and the personality is now divided into cities so for in islamabad if the personality or in our data set the personality is introvert there are four people with the choice of color red yellow 5 green 14 blue 7 and similarly for lahore and then there is our total total is similar to this one so layer variable can help you divide your output by categories for a particular crosstab report now let's run our test before i run our test let's see so i've got this output a sample output as to how to report the tests as well so one while this opens let's run our test we can press ctrl a and press delete to delete all the output and now chi square test of association we want to test the association between the personality and the color preference in order to do this what we'll do is we'll go to analyze descriptive statistics cross tab and let's remove the layer variable for now go to statistics and select chi square statistics press continue and press ok now we've discussed these two and this is your chi square result pearson chi square value is 4.535 the degrees of freedom 3 and your significance or your p value is 0.209 which is greater than 0.05 this means that there is no association between personality and color preference now how do you report these results here is a sample output the problem is to identify the association between personality and color preference based on this problem the hypothesis is there is a significant association between personality and color preference now in order to report chi-square test or chi-square test of association what we do is we write chi square statistics were used to examine association between categorical variables personality and color preference the results actually showed there is an insignificant association at five percent significance level between the two variables which are personality and correct color preference of the respondents here is your chi square 4.535 the degree of freedom 3 and p value 0.209 which is greater than 0.05 so your h1 was not supported let's say i want to test this hypothesis in each of the cities so what i'll do is i'll go to analyze descriptive statistics cross tabs and add the layer variable and just press ok and if you look here obviously the hype the significance level obviously in islamabad greater than 0.05 in lahore greater than 0.05 overall 0.209 again there is no association between these two variables so this is how you can use chi square test of association and crosstab reports to assess the relationship between two nominal scale variables one more thing if you go to analyze descriptive statistics and cross tabs and go to statistics sorry go to cells you can take percentages as well so let's say we want the percentages in rows and columns just press continue press ok and here you can see within personality and within preference this shows your percentage score as well so there are 13 people whose personality is introvert and their preference is red and within personality this count or this amounts to 18.6 percent but within preference this amounts for 59.1 percent so this is how you can use percentage with crosstab reports as well thank you very much

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Keywords: Chi-Square Test, SPSS, The Chi-Square test for independence, Crosstabulation, Pearson's Chi-square test., Crosstab Analysis, Chi Square, Reporting Chi-Square Test, Interpret Chi-Square Test, Chi-Square Test of Association, SPSS Tutorial, SPSS Data Analysis

Id: 7oq6snUiXb0

Length: 10min 3sec (603 seconds)

Published: Tue Dec 29 2020