Upgrade to Pro — share decks privately, control downloads, hide ads and more …

CMNS201 - Lab 7. SPSS Multivariate Analysis

CMNS201 - Lab 7. SPSS Multivariate Analysis

Alberto Lusoli

March 23, 2022
Tweet

More Decks by Alberto Lusoli

Other Decks in Education

Transcript

  1. Multivariate analysis in SPSS
    SPSS LABORATORY 7

    View full-size slide

  2. 1. Calendar
    2. Multivariate analysis and control variables: theory
    3. Adding a control variable to your analysis in SPSS
    4. Exercise
    INDEX

    View full-size slide

  3. Photo: Startup Weekend Hackathon. Nov.2014
    SIMPLE BIVARIATE ANALYSIS
    The important element to point out in this crosstab was the difference between female students with GPA above
    3.0 and male students with GPA above 3.0. Being this difference above 10%, we can assume there is a significant
    relation between grade and gender.
    65.1% - 50% = 15.1%

    View full-size slide

  4. Photo: Startup Weekend Hackathon. Nov.2014
    SIMPLE BIVARIATE ANALYSIS
    A simple association between two variables that does not control for the possible influence of other variable
    ZERO ORDER RELATIONSHIP

    View full-size slide

  5. Photo: Startup Weekend Hackathon. Nov.2014
    CONTROL VARIABLE
    A control variable is a variable which is held constant throughout a research in order to assess the relationship
    between dependent and independent variables. Since it remains constant, it enables researchers to test and better
    understand the relationship between dependent and independent variables.

    View full-size slide

  6. Photo: Startup Weekend Hackathon. Nov.2014
    HOW TO CONDUCT BIVARIATE ANALYSIS WITH
    CONTROL VARIABLE IN SPSS
    ■ Go to Canvas
    ○ Assignments > SPSS Lab 7 - Bivariate with control variable and selecting cases
    ○ Download the Week-7.sav file
    ■ Open the file on SPSS
    ○ Launch SPP
    ■ File > Open > Data…
    ■ Find and open the Week-7.sav file

    View full-size slide

  7. Photo: Startup Weekend Hackathon. Nov.2014
    AS USUAL, LET’S BEGIN WITH A HYPOTHESIS
    People who spend more time on Instagram will tend to have more followers than people who spend less time on it.
    Null hypothesis: there is no relationship between time spent on Instagram and number of followers

    View full-size slide

  8. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS: EXAMPLE
    Amount of time spent on
    Instagram
    (independent variable)
    Number of followers
    (dependent variable)
    Example of a direct relation. If average time on IG increases, followers also increase.

    View full-size slide

  9. Photo: Startup Weekend Hackathon. Nov.2014
    PREPARING THE VARIABLES
    Amount of time spent on
    Instagram
    Number of followers
    Simplified, 2-value, nominal
    variable
    Simplified, 2-value, nominal
    variable

    View full-size slide

  10. Photo: Startup Weekend Hackathon. Nov.2014
    PREPARING THE VARIABLES
    Where to “cut” scale variables in order to identify two ranges to use to define the values of the new variable?
    A good idea is to use a measure of centrality (mean, median or mode) as a threshold value to identify the two
    ranges. For example:
    1. Right click on the variable name. Select :Descriptive
    statistics
    2. Mean or Median can be used as threshold values.

    View full-size slide

  11. Photo: Startup Weekend Hackathon. Nov.2014
    PREPARING THE VARIABLES
    Another option for identifying a threshold is to refer to arbitrary values that are meaningful in the context of the
    analysis. For example, in the case of this hypothesis, we can define people who use Instagram less than 1 hour a
    day as casual users, and people who use it for more than one hour a day as intense users. All arbitrary values are
    good threshold values as long as they are consistent with your hypothesis.

    View full-size slide

  12. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS WITH RECODED VARIABLES

    View full-size slide

  13. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS WITH RECODED VARIABLES
    Missing values

    View full-size slide

  14. Photo: Startup Weekend Hackathon. Nov.2014
    CLEANING UP DATA: SELECTING CASES
    Click on
    Select Cases

    View full-size slide

  15. Photo: Startup Weekend Hackathon. Nov.2014
    CLEANING UP DATA: SELECTING CASES
    1. Click on
    If condition is
    satisfied
    2. Click
    If…
    button

    View full-size slide

  16. Photo: Startup Weekend Hackathon. Nov.2014
    CLEANING UP DATA: SELECTING CASES
    1. Add the
    variable you
    want to use as a
    filter
    2. Specify the
    conditions for
    inclusion.
    Separate
    multiple
    conditions with
    the command
    “or” or “And”.
    Pay attention to
    quotes, spaces,
    capitalizations.
    IG_followers_new = 'More followers' or IG_followers_new = 'Less followers'

    View full-size slide

  17. Photo: Startup Weekend Hackathon. Nov.2014
    CLEANING UP DATA: SELECTING CASES

    View full-size slide

  18. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS ON CLEANED VARIABLES
    The missing
    values column is
    gone
    % are different
    compared to the
    initial bivariate
    analysis
    Important: Make sure to reintroduce all your cases before performing any other
    analysis on different variables. To do so go to Data > Select Cases and click on “All cases”

    View full-size slide

  19. Photo: Startup Weekend Hackathon. Nov.2014
    CROSSTAB INTERPRETATION
    Since the difference between % is less than 10%, we can conclude that the data do not support our the hypothesis.
    The Null hypothesis is true: there is no relation between independent and dependent variable.

    View full-size slide

  20. Photo: Startup Weekend Hackathon. Nov.2014
    ADDING A CONTROL VARIABLE
    What happens when we add a control variable?
    Let’s try to add “Gender” as a control variable. To do so, go to Analyze > Descriptive statistics > Crosstab
    Independent variable in the Rows
    Dependent variable in the Columns
    Control variable in the Layer

    View full-size slide

  21. Photo: Startup Weekend Hackathon. Nov.2014
    READING A CROSSTAB WITH CONTROL VARIABLE
    Zero order relation.
    it’s the bivariate analysis
    without control variable.

    View full-size slide

  22. Photo: Startup Weekend Hackathon. Nov.2014
    READING A CROSSTAB WITH CONTROL VARIABLE
    Partial
    relations
    Zero order relation.
    it’s the bivariate analysis
    without control variable.

    View full-size slide

  23. Photo: Startup Weekend Hackathon. Nov.2014
    READING A CROSSTAB WITH CONTROL VARIABLE
    Partial
    relations
    Zero order relation.
    No significant relation
    between time on
    instagram and number of
    followers

    View full-size slide

  24. Photo: Startup Weekend Hackathon. Nov.2014
    READING A CROSSTAB WITH CONTROL VARIABLE
    Male students who
    spend less time on
    Instagram tend to
    have more followers.
    Inverse relation
    between time on
    Instagram and
    followers
    Zero order relation.
    No significant relation
    between time on
    instagram and number of
    followers

    View full-size slide

  25. Photo: Startup Weekend Hackathon. Nov.2014
    READING A CROSSTAB WITH CONTROL VARIABLE
    Zero order relation.
    No significant relation
    between time on
    instagram and number of
    followers
    Female students who
    spend more time on
    Instagram tend to
    have more followers.
    Direct relation
    between time on
    Instagram and
    followers

    View full-size slide

  26. Photo: Startup Weekend Hackathon. Nov.2014
    INTERPRETING THE RESULTS
    The control variable revealed inverse and direct significant relations in the partials, while the zero order relation
    confirmed the null hypothesis (no relation between time on Instagram and number of followers).
    Therefore, we can conclude that the Gender is a suppressor variable (see Prof.Al-Rawi Week 8 videos for the kind
    of relations between independent, dependent and control variables).

    View full-size slide

  27. Photo: Startup Weekend Hackathon. Nov.2014
    INTERPRETING THE RESULTS
    In general, begin by commenting the zero order relation. Is there a significant (more than 10%) relation? Is it direct
    or inverse?
    Then analyze each partial.
    1. Is the relation’s direction (direct or inverse) in each partial the same as the zero sum relation?
    2. Is it more or less intense than the zero sum relation? (meaning, is the difference in % greater or lower).
    3. Is it significant? (more than than 10%)
    Lastly, try to map the variables using the models described in Prof.Al-Rawi Week 8 lecture? (specification,
    interpretation, explanation, replication, suppressor variable, distorter variable).

    View full-size slide

  28. Photo: Startup Weekend Hackathon. Nov.2014
    EXERCISE
    Repeat the same crosstab done in class: independent variable: time on Instagram. Dependent variable: number
    of followers.
    Instead of using Gender as a control variable, try to use Canadian_birth. Does the result change? Upload the
    crosstab with the control variable and comment on the partial relations.
    Upload a screenshot of the crosstab on Canvas alongside a one sentence comment about the relation (or lack
    thereof) between the two variables.

    View full-size slide

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

    View full-size slide