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CMNS201 - Lab 8. Assignment 3

CMNS201 - Lab 8. Assignment 3

Alberto Lusoli

April 06, 2022
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  1. Assignment 3
    SPSS LABORATORY 8

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  2. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    Empty column
    Variable contains
    empty/missing
    values

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  3. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    Filtered out all
    values
    due to errors in
    the Select Cases
    formula

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  4. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    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.
    Variable_Name = 'Value to include' OR Variable_Name = 'Second value to include'

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  5. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    Variable_Name = 'Value to include' OR Variable_Name = 'Second value to include'

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  6. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    “The relationship between Instagram
    followers and Canadian born status is
    significant. The relationship between
    Instagram time and Canadian born status
    is not significant. As well as the
    relationship between Instagram followers
    and Instagram time is not significant. “
    A crosstab shows the relation (or lack
    thereof) between independent and
    dependent variable.

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  7. Photo: Startup Weekend Hackathon. Nov.2014
    LAST WEEK LAB ASSIGNMENT: COMMON PROBLEMS
    “The relationship between Instagram
    followers and time on instagram is
    spurious“
    Why? Always mention the significance (is
    it above or below 10%) for both zero
    order relationship and partials.

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  8. 1. Introduction
    2. Univariate Statistics
    3. Bivariate Statistics A
    4. Multivariate Statistics B
    5. Sampling
    6. Critique of the survey
    7. Future research
    ASSIGNMENT 3 STRUCTURE

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  9. 1. CHOOSING VARIABLES

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  10. Photo: Startup Weekend Hackathon. Nov.2014
    AS USUAL, LET’S BEGIN WITH A HYPOTHESIS
    Hypothesis: People who have a political affiliation will tend to read news more carefully and to investigate more to
    understand is something they read online is a legit news or misinformation. On the contrary, people without a
    political affiliation will be less likely to spend time investigating news sources.
    Control variable: We will control the relation between political affiliation and tendency to investigate news sources
    through a variable measuring how much of an impact social media have on media consumption.
    Null hypothesis: there is no relationship between political affiliation and tendency to investigate news sources.

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  11. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 1: CHOOSE YOUR VARIABLES
    ● Independent variable:
    ○ @2718068 (Which Canadian federal political party do you think best represents your personal political
    orientation?)
    ● Dependent variable:
    ○ @2864544 (When presented with news media on social media platforms, how often do you further
    analyze or investigate the news media in question in order to discern misinformation?)
    ● Control variable:
    ○ @2864568 (How much of an impact does social media have on your media consumption?)

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  12. 2. RECODING VARIABLES

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  13. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    All variables (independent, dependent and control) must be binary. Which means, must include only 2 values (for
    example, younger, older, more followers, less followers, high GPA, low GPA,etc.).
    Therefore, it is very likely you will have to recode your variables in order to convert them from their original
    format to a binary form (for how to recode variables, see week 3 lab).

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  14. Photo: Startup Weekend Hackathon. Nov.2014
    Right click on the independent variable name and
    select Descriptive Statistics.
    STEP 2: RECODING YOUR VARIABLES
    2.2 RECODING THE INDEPENDENT VARIABLE

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  15. The univariate analysis shows that the variable
    @2718068 is not binary. It has 7 values. We need to
    find a logic for creating 2 groups only:
    1. People with political affiliation
    2. People without political affiliation
    In your assignment, explain the login you followed to
    group your variable’s values.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    2.2 RECODING THE INDEPENDENT VARIABLE

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  16. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    Group 1
    People with
    political
    affiliation
    2.2 RECODING THE INDEPENDENT VARIABLE

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  17. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    Group 2
    People without
    political
    affiliation
    2.2 RECODING THE INDEPENDENT VARIABLE

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  18. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    In the “Prefer not to say” value
    there might be people with political
    affiliation and people without
    political affiliation. For this reason,
    we do not recode this value
    intentionally. We do not recode this
    value so that we can remove these
    answers later
    2.2 RECODING THE INDEPENDENT VARIABLE

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  19. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    2.2 RECODING THE INDEPENDENT VARIABLE
    Notice that the “Prefer not to say” is absent from the recoding rules

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  20. 2.2 CHECKING IF THE VARIABLE WAS PROPERLY RECODED
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    1. Once recoded, drag and drop the new variable next to the original
    variable.
    2. Sort the dataset Ascending using the original variable (right click on the
    original variable name > Sort Ascending).
    3. In this way you can easily check if the old variable was correctly recoded
    into the new one.

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  21. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    Then, run a quick Descriptive Statistics on the new variable to check
    once again that the independent variable was properly recoded.
    2.2 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  22. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    OK
    OK
    Notice how the table has 3 rows. This
    means that the variable contains 3
    values. “Does no have political opinion”,
    “Has political opinion” and blank. The
    Blank value includes missing responses
    and the ‘Prefer not to say” responses
    that we decided not to recode. Blank
    responses (the first row) must be
    removed. We’ll clean the data later, in
    step 3.
    2.2 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  23. Photo: Startup Weekend Hackathon. Nov.2014
    Now it’s time to analyze the Dependent variable. Right
    click on the dependent variable name and select
    Descriptive Statistics.
    STEP 2: RECODING YOUR VARIABLES
    2.3 RECODING THE DEPENDENT VARIABLE

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  24. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    The univariate analysis shows that the variable
    @2864544 is not binary. It has 5 values. We need to
    find a logic for creating 2 groups only:
    1. People who are more likely to investigate news
    sources in order to discern misinformation
    2. People who are less likely to investigate news
    sources in order to discern misinformation
    In your assignment, explain the login you followed to
    group your variable’s values.
    2.3 RECODING THE DEPENDENT VARIABLE

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  25. Photo: Startup Weekend Hackathon. Nov.2014
    Group 1
    People who answered Always,
    Often or Sometimes will be
    grouped into the “More likely to
    investigate news” value of the new
    recoded variable.
    STEP 2: RECODING YOUR VARIABLES
    2.3 RECODING THE DEPENDENT VARIABLE

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  26. Photo: Startup Weekend Hackathon. Nov.2014
    Group 2
    While people who answered Not
    often or Never will be grouped into
    the “Less likely to investigate news”
    value of the new recoded variable.
    STEP 2: RECODING YOUR VARIABLES
    2.3 RECODING THE DEPENDENT VARIABLE

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  27. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    2.3 RECODING THE DEPENDENT VARIABLE

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  28. 2.2 CHECKING IF THE VARIABLE WAS PROPERLY RECODED
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    1. Once recoded, drag and drop the new variable next to the original
    variable.
    2. Sort the dataset Ascending using the original variable (right click on the
    original variable name > Sort Ascending).
    3. In this way you can easily check if the old variable was correctly recoded
    into the new one.

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  29. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    Then, run a quick Descriptive Statistics on the new variable to check
    once again that the dependent variable was properly recoded.
    2.3 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  30. Notice how the table has 3 rows. This
    means that the variable contains 3
    values. “Less likely to investigate news”,
    “More likely to investigate news” and
    blank. The Blank value includes
    missing responses. Blank responses
    (the first row) must be removed. We’ll
    clean the data later, in step 3.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    OK
    OK
    2.3 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  31. Photo: Startup Weekend Hackathon. Nov.2014
    Lastly, analyze the Control variable. Right click on the
    control variable name and select Descriptive Statistics.
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE

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  32. Photo: Startup Weekend Hackathon. Nov.2014
    The univariate analysis shows that the variable
    @2864568 is not binary. It has 5 values. We need to
    find a logic for creating 2 groups only:
    1. People whose media consumption choices are
    impacted by social media
    2. People whose media consumption choices are
    not impacted by social media
    In your assignment, explain the login you followed to
    group your variable’s values.
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE

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  33. Photo: Startup Weekend Hackathon. Nov.2014
    Group 1
    People who answered Above
    Average and Average will be
    grouped into the “Social media
    have impact” value of the new
    recoded variable.
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE

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  34. Photo: Startup Weekend Hackathon. Nov.2014
    Group 2
    While people who answered Very
    Rarely and Not at all will be
    grouped into the “Social media do
    not have impact” value of the new
    recoded variable.
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE

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  35. What about people who answered don’t
    know/do not use social media? Based on
    their response, we can’t really tell whether
    they are impacted or not by social media.
    Therefore, we will not recode their
    responses. In this way, all cases with “I don’t
    know/ I don’t use social media” in the old
    variable will will have empty values in the
    new recoded variable. We will filter out
    these empty values later.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE

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  36. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    2.4 RECODING THE CONTROL VARIABLE
    Notice that the “I don’t know/ I don’t use social media” is absent from the recoding rules

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  37. 2.4 CHECKING IF THE VARIABLE WAS PROPERLY RECODED
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    1. Once recoded, drag and drop the new variable next to the original
    variable.
    2. Sort the dataset Ascending using the original variable (right click
    on the original variable name > Sort Ascending).
    3. In this way you can easily check if the old variable was correctly
    recoded into the new one.

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  38. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    Then, run a quick Descriptive Statistics on the new variable to check
    once again that the control variable was properly recoded.
    2.4 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  39. Notice how the table has 3 rows. This
    means that the variable contains 3
    values. “Social media have impact”,
    “Social media do not have impact” and
    blank. The Blank value includes
    missing responses and the “I don’t
    know/ I don’t have social media”
    responses that we have not recoded.
    Blank responses (the first row) must be
    removed. We’ll clean the data later, in
    step 3.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    OK
    OK
    2.2 CHECKING IF THE VARIABLE WAS PROPERLY RECODED

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  40. 1. All your variables (independent, dependent, control) must be binary (two values only).
    2. If you work with numeric variables, rely on one of the two strategies discussed in Lab 7 to decide how to
    divide the values of the old variable into 2 values in the new variable. These strategies are:
    a. Using a measure of centrality as threshold value (median or mean)
    b. Relying on an arbitrary rule (as long as it is consistent with your hypothesis)
    3. Include at least 1 variable from student submitted questions (variable #52 - #138).
    4. If one of your variables is already binary, there is no need to recode.
    5. Do not use the same combination of variables of this example.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 2: RECODING YOUR VARIABLES
    NOTES

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  41. 3. CLEANING VARIABLES

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  42. Now it’s time to clean the data. As shown in the previous slides, all recoded variables have missing or empty
    values.
    We now need to remove empty values from all variables.
    To do so, click on Data in the top bar and then Select Cases.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES

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  43. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES
    1. Click on
    If condition is
    satisfied
    2. Click
    If…
    button

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  44. In the week 7 Lab, we learned how to remove undesired values from a variable. This is achieved by writing a
    simple logical statement in the Select cases window. This statement determines which cases SPSS will use in all
    future calculations.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES

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  45. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES
    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.
    @2718068_New = 'Has political opinion' OR @2718068_New = 'Does not have political opinio'

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  46. The formula showed in the previous slide is appropriate if you have to clean only one variable.
    However, if more than one of your variables include unwanted values (in other words, if more than one of your
    variable is not in binary form), then you need to use a more complex formula to remove all unwanted values
    from all variables at once. The formula is the following:
    (VARIABLE1 = 'VALUE1' OR VARIABLE1 = 'VALUE2’) AND
    (VARIABLE2 = 'VALUE1' OR VARIABLE2 = 'VALUE2') AND
    (VARIABLE3 = 'VALUE1' OR VARIABLE3 = 'VALUE2')
    Where VARIABLE1 is your independent variable, VARIABLE2 is your dependent variable and VARIABLE3 is your
    control variable, and VALUE1 and VALUE2 are the respective values you want to include for each variable.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES

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  47. In the case of the variables used in this example, the inclusion formula is the following:
    (@2718068_New = 'Has political opinion' OR @2718068_New = 'Does not have political opinio') AND
    (@2864544_New = 'More likely to investigate new' OR @2864544_New = 'Less likely to investigate new') AND
    (@2864568_New = 'Social media do not have impact' OR @2864568_New = 'Social media have impact')
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES

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  48. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 3: CLEANING YOUR VARIABLES
    Write the logical
    statements for
    inclusion
    Click OK

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  49. Photo: Startup Weekend Hackathon. Nov.2014
    Good job! The most tedious part of Assignment 3 is done. If you successfully recoded and cleaned your variables,
    you are 80% done. What is left is the fun part: running the analyses.
    STEP 3: CLEANING YOUR VARIABLES

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  50. 4. UNIVARIATE ANALYSIS

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  51. Now, let’s do a univariate analysis for all three variables.
    Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS

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  52. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS

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  53. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS
    Drag and drop your three variables
    (recoded and cleaned) in the box on the
    right.
    Select Statistics and choose the main
    measures of centrality and dispersion
    (mean, mode, median, std deviation,
    range, min and max).
    Repeat this step for all your 3 variables.

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  54. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS
    Your Frequency tables should look like
    these.
    If some of your tables have more than 2
    rows (plus the total row), then it means
    you have not cleaned the data. Go back to
    step 3 and control the logical statements
    for inclusion.

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  55. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS
    If your variables are strings (text), your
    statics table should look like this
    (remember, you cannot calculate centrality
    and dispersion for textual variables):
    If your variables are numeric, your statics
    table should look like this:

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  56. Photo: Startup Weekend Hackathon. Nov.2014
    STEP 4: UNIVARIATE ANALYSIS
    If you really want to go the extra mile, add
    charts to your analysis. If you do, keep in
    mind the data visualization best practices
    described in SPSS Lab 5

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  57. 4. BIVARIATE ANALYSIS

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  58. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS

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  59. Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    1. Independent variable (recoded
    and cleaned) in the Rows
    2. Dependent variable (recoded and
    cleaned) in the Columns
    3. Click Cells

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

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  61. Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    Your crosstab should look like this.
    If you have more than 2 rows for the
    independent variable or more than 2
    columns for the dependent variable
    (excluding the Total), then it means
    you have not properly cleaned your
    variables.
    In other words, you have more than 2
    values in the independent or
    dependent variable. Go back to Step 3
    and repeat the cleaning procedure.

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  62. Photo: Startup Weekend Hackathon. Nov.2014
    SETTING UP YOUR CROSSTAB
    Briefly discuss your findings in words.
    Describe the patterns you found and
    discuss whether or not these patterns
    support or do not support your
    hypothesis. To learn how to interpret a
    crosstab, see SPSS Lab 6 slides on
    Canvas.

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  63. Photo: Startup Weekend Hackathon. Nov.2014
    CROSSTAB INTERPRETATION
    81.3% of people with a political affiliation declared to investigate news sources, while only 64.7% of people without
    political affiliation declared doing it. Since the difference is greater than 10% (16.6%), we can conclude that there
    is a significant relationship between the independent variable and the dependent variable. People with
    political affiliations are more likely to check news sources more often than people without political affiliation.

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  64. 4. MULTIVARIATE ANALYSIS

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  65. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS

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  66. Photo: Startup Weekend Hackathon. Nov.2014
    ADDING A CONTROL VARIABLE
    1. Independent variable (recoded
    and cleaned) in the Rows
    2. Dependent variable (recoded and
    cleaned) in the Columns
    3. Control variable (recoded and
    cleaned) in the Layer box
    4. Click Cells

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

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  68. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS ON CLEANED VARIABLES
    Control
    Variable (2
    values only)

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  69. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS ON CLEANED VARIABLES
    Independent
    Variable
    (2 values only)

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  70. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS ON CLEANED VARIABLES
    Dependent
    Variable
    (2 values only)

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  71. Photo: Startup Weekend Hackathon. Nov.2014
    IF YOU HAVE MORE THAN 2 VALUES IN ANY OF
    THE 3 VARIABLES, GO BACK TO DATA CLEANING
    AND MAKE SURE TO REMOVE ALL UNWANTED
    VALUES

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  72. Photo: Startup Weekend Hackathon. Nov.2014
    BIVARIATE ANALYSIS ON CLEANED VARIABLES
    Controlling for social media impact reveals a complex
    scenario. As already seen in the previous bivariate analysis,
    the zero order relationship shows a significant relation
    between political views and tendency to investigate news
    sources.
    This relation is even stronger in the case of people who
    declared that social media have a significant impact on
    their media consumption. This can be seen in the %
    difference between people with political affiliation and
    people without political affiliation (20.6%).
    While in the case of people who declared that social media
    do not play a role in their media consumption, there is no
    relation between dependent variable and independent
    variable. People with political views and without political
    views are equally likely to investigate news sources further
    (75% of them declare doing it)

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  73. Photo: Startup Weekend Hackathon. Nov.2014
    INTERPRETING THE RESULTS
    See Week 7 slides for instructions on how to read a multivariate analysis crosstable.
    If you want to go the extra mile, and if possible, try to describe the relation between variables using the models
    described in Prof.Al-Rawi Week 8 lecture (specification, interpretation, explanation, replication, suppressor
    variable, distorter variable).

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  74. Photo: Startup Weekend Hackathon. Nov.2014
    Congratulations, you have successfully completed the SPSS part of Assignment 3

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  75. Photo: Startup Weekend Hackathon. Nov.2014
    Q&A

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  76. 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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