Alberto Lusoli
March 16, 2022
180

# CMNS201 - Lab 6. SPSS Bivariate Analysis, Crosstab

March 16, 2022

## Transcript

2. ### 1. Calendar 2. Bivariate analysis: quick theory refresher 3. Bivariate

analysis in SPSS 4. Exercise INDEX
3. ### Photo: Startup Weekend Hackathon. Nov.2014 SURVEY Do not forget to

take the survey by Friday, 18 at midnight. Canvas > Quiz > CMNS201 Survey 2022
4. ### Photo: Startup Weekend Hackathon. Nov.2014 BIVARIATE ANALYSIS: DEFINITION Bivariate analyses

are conducted to determine whether a statistical association exists between two variables. For example, bivariate analyses could be used to answer the question of whether there is an association between income and quality of life. Source: Sandilands D.. (2014) Bivariate Analysis. In: Michalos A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_222
5. ### Photo: Startup Weekend Hackathon. Nov.2014 BIVARIATE ANALYSIS: DEFINITION More speciﬁcally,

bivariate analysis explores how the dependent variable depends by the independent variable. There are 2 types of relationship between the dependent and independent variable: ▪ A direct relationship (also called positive correlation) – that means if the independent variable increases, then the dependent variable would also increase and vice versa. ▪ A inverse relationship (negative correlation) – when the independent variable increases and the dependent variable decrease and vice versa.
6. ### Photo: Startup Weekend Hackathon. Nov.2014 BIVARIATE ANALYSIS: EXAMPLE Average daily

temperature Ice cream sales Example of a direct relation. If average daily temperature increases, ice cream sales also increase
7. ### Photo: Startup Weekend Hackathon. Nov.2014 BIVARIATE ANALYSIS: EXAMPLE Average daily

temperature Ramen sales Example of an inverse relation. If average daily temperature increases, ramen sales decrease
8. ### Photo: Startup Weekend Hackathon. Nov.2014 HOW TO CONDUCT BIVARIATE ANALYSIS

IN SPSS ▪ Go to Canvas ◦ Assignments > SPSS Lab 6 - Bivariate analysis ◦ Download the Week-6.sav ﬁle ▪ Open the ﬁle on SPSS ◦ Launch SPP ▪ File > Open > Data… ▪ Find and open the Week-6.sav ﬁle
9. ### Photo: Startup Weekend Hackathon. Nov.2014 LET’S BEGIN BY FORMULATING A

HYPOTHESIS SFU Students with Canadian citizenship will tend to have higher GPA because they grew up within the Canadian educational system. On the contrary, SFU students on a study permit will have lower GPA because in addition to entering a new educational system, they also experience disconnection from their families and communities. Null hypothesis: there is no relation between Immigration Status and GPA
10. ### Photo: Startup Weekend Hackathon. Nov.2014 LET’S BEGIN BY FORMULATING A

HYPOTHESIS SFU Students with Canadian citizenship will tend to have higher GPA because they grew up within the Canadian teaching system. On the contrary, SFU students on a study permit will have lower GPA because in addition to entering a new educational system, they also experience disconnection from their families and communities. Immigration status: Independent variable GPA: dependent variable Null hypothesis: there is no relation between Immigration Status and GPA

12. ### Photo: Startup Weekend Hackathon. Nov.2014 SETTING UP YOUR CROSSTAB Independent

variable in the Rows Dependent variable in the Columns
13. ### Photo: Startup Weekend Hackathon. Nov.2014 SETTING UP YOUR CROSSTAB 1.

Put Residence_status in the Rows 2. Put GPA in the Columns 3. Click Cells
14. ### Select Observed and Expected counts Photo: Startup Weekend Hackathon. Nov.2014

SETTING UP YOUR CROSSTAB Select Row Click Continue

Click Ok
16. ### Photo: Startup Weekend Hackathon. Nov.2014 HOW TO READ A CROSSTABULATION

Independent variable Dependent variable
17. ### Photo: Startup Weekend Hackathon. Nov.2014 HOW TO READ A CROSSTABULATION

Count: is the observed frequency in a particular cell of the crosstabs table. For example, In this case, 18 students with Canadian Citizenship have a GPA of 2.99 or lower. Expected Count: The expected count is the predicted frequency for a cell under the assumption that the null hypothesis is true. In our case, the null hypothesis is that there is no association between Immigration Status and GPA. If the Null hypothesis was true, then we expected 17 students with a GPA of 2.99 or lower.
18. ### Photo: Startup Weekend Hackathon. Nov.2014 HOW TO READ A CROSSTABULATION

*: “signiﬁcant” is a very broad qualiﬁer. In general, if you see a diﬀerence equal or greater of 10%, you can assume there is a relation between the two variables (which can conﬁrm or not your hypothesis). If the diﬀerence is small (around 5%), you can assume there is no relation between the two variables. To understand if our initial hypothesis is conﬁrmed or not, we need to compare % by column. To conﬁrm our hypothesis, the % of Canadian students with a GPA greater than 3 should have been signiﬁcantly* above the % of study permit students with a GPA above 3. This results do not support the hypothesis. Actually show the opposite: there is a relation between Residence_Status and GPA but a relatively larger share of Study permit students (4.4%) compared to Canadian Citizen appear to have a GPA of 3 or higher.
19. ### Photo: Startup Weekend Hackathon. Nov.2014 CROSSTABULATION AND VARIABLE RECODING Hypothesis:

People who sleep less tend to achieve a higher GPA. Null hypothesis: there is no relation between Sleep_hrs and GPA

21. ### Photo: Startup Weekend Hackathon. Nov.2014 CROSSTABULATION AND VARIABLE RECODING 8

is usually indicated as the ideal number of hours of sleep per night. Based on this information, let’s create 2 segments. In formula: ▪ If Sleep_hrs <= 7.5, then Sleep_new = “Restless” ▪ If Sleep_hrs => 8, then Sleep_new = “Sleepy”
22. ### Photo: Startup Weekend Hackathon. Nov.2014 CROSSTABULATION AND VARIABLE RECODING If

you do not remember how to recode a variable, check Week 3 slides.
23. ### Photo: Startup Weekend Hackathon. Nov.2014 1: select the variable to

recode 2: type the new variable name and add descriptive label 3: click change 4: click Old and New Values 5: set your range 6: tick the checkbox and deﬁne width 15 7: deﬁne the new variable value for the range 8: click add and repeat steps 5-7-8 for the second range. 10: click OK 9: once deﬁned all ranges, click Continue

25. ### Photo: Startup Weekend Hackathon. Nov.2014 CROSSTABULATION AND VARIABLE RECODING The

ﬁndings do not support the hypothesis. The % of students with a GPA of 3 and above is nearly identical for both Sleepy and Restless students. The diﬀerence is just 1.5%!
26. ### Photo: Startup Weekend Hackathon. Nov.2014 EXERCISE Run a Crosstab for

the variables Gender (independent Variable) and GPA (dependent variable). Is there a relation between the two variables? Upload a screenshot of the crosstab on Canvas alongside a one sentence comment about the relation (or lack thereof) between the two variables.