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CMNS201 - Lab 6. SPSS Bivariate Analysis, Crosstab

CMNS201 - Lab 6. SPSS Bivariate Analysis, Crosstab

Alberto Lusoli

March 16, 2022
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  1. Bivariate analysis in SPSS
    SPSS LABORATORY 6

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  2. 1. Calendar
    2. Bivariate analysis: quick theory refresher
    3. Bivariate analysis in SPSS
    4. Exercise
    INDEX

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  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

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  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

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  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.

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  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

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  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

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  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

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  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

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  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

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  11. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS IN SPSS

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  12. Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    Independent variable in the Rows
    Dependent variable in the Columns

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  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

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  14. Select Observed and Expected
    counts
    Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    Select Row
    Click Continue

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  15. Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    4. Click Ok

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

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  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.

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  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.

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  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

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  20. Photo: Startup Weekend Hackathon. Nov.2014
    CROSSTABULATION AND VARIABLE RECODING

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  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”

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  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.

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  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

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  24. Photo: Startup Weekend Hackathon. Nov.2014
    CROSSTABULATION AND VARIABLE RECODING

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  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%!

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  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.

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  27. THANK YOU
    Alberto Lusoli
    [email protected]
    Office hour: Thursday, 12.30pm - 1.20pm (please book an appointment in advance via email).

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