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  • Spuriousness: Ice Cream is linked with drowning, but this relationship with linked by a third variable. The relationship is not logical! Must justify what you are doing, and how you are measuring things.
  • Must be able to justify relationships!
  • Best researchers are those who know the literature, and come up with really interesting ways to measure. In your paper, must have clear links between what you are measuring, and what you have developed.
  • This is a linear relationship. As you move right, you are moving forward in time. There is always some underlying idea of geography in Canadian Election studies. Where you are reflects what you want from the government. Concentrated areas have a lot of power! There are different attitudes between men and women. Different experiences create different attitudes toward the government. R egion should be X1, and support X2. X3 should also before X1. You cannot control where you live, nor can you control your gender. You are born this way.
  • This model starts with how long you’ve been a Canadian Citizen. Not all educated people support private health care, w hich is why ‘Canadian Values’ is also considered in the model. Would have a very tough time operationalizing ‘Canadian Values’. (Not obviously ‘kickable’ as a concept.) Would need an index to measure this.
  • This will be very useful for doing our research project! Keep this close. Use all the obvious variables. Build it knowing that certain variables happen before other Variables. May want to include intervening variables. Can’t measure everything! Surveys are not exhaustive, there are limits to time and money.
  • Age, can mean 3 things. Actual age. As we age, we have more of a stake in what the government does. More set. Also, the generation: values and expectations come with the generation you grew up in. Certain things have changed dramatically over time. Can be a period affect, something dramatic have happened while you were growing up. E.g. 911 is ours. When people are more educated, they better they translate their values into actions. The type of education might bring a set of knowledge, and tolerance. Urbanization is a big part of this. (This is one of the theorized ways that education affects tolerance.) This is an argument of a Liberal Science education. Not all Education is the same!
  • Can try to measure this by measuring how often they actually attend services. There is a measure of this in the Canadian Election Study.
  • Must talk about how you operationalize such a concept.
  • This is when you have to be tough on yourself when you are making a causal model. Is there something you are not considering? This is one of the most damning criticisms of research. You probably will not have to draw this, but keep this in mind! Can be latent, and hard to see. Can be found by being critical of yourself. If you think the relationship is wrong, and you can prove it, this is really good research.
  • This will be important when you are working with WORD and drawing your causal models. These instructions also work on the new word.
  • POLI_399_tutorial_4

    1. 1. POLI 399 – Research Methods Week Four Causal Modelling
    2. 2. Today’s Agenda <ul><li>What is a causal model? </li></ul><ul><li>Elements of the causal model </li></ul><ul><li>In-class Example </li></ul><ul><ul><li>Intervening Variable Model </li></ul></ul><ul><ul><li>Antecedent Variable Model </li></ul></ul><ul><ul><li>Source of Spuriousness Model </li></ul></ul>
    3. 3. Establishing a Causal Relationship <ul><li>Three conditions to establish causal relationship: (Jackson and Verberg, 442) </li></ul><ul><li>The variables must be associated (They vary together). </li></ul><ul><li>The variables must be in a plausible causal sequence (Believable ordering of variables). </li></ul><ul><li>The variables must not be spuriously connected (The relationship is not due to some third variable). </li></ul>
    4. 4. What is a Causal Model? <ul><li>A causal model helps to clarify complex multivariate relationships by providing a visual representation of the relationships between variables. It also sets the stage for operationalization and the testing of hypotheses. </li></ul>
    5. 5. What is a Causal Model? <ul><li>Your causal model should be built from your literature review. Existing research should be your guide. </li></ul><ul><li>You have to be able to defend the design of your causal model. </li></ul><ul><li>There should be a clear link between the literature review and the model you develop -- This should be made explicit in your research report. </li></ul>
    6. 6. Modelling Conservative Support in Canada X 1 Support for less government intervention Y 1 Support for Steven Harper + X 3 gender X 2 Region +/- +/-
    7. 7. Modelling Support for Private Health Care X 1 X 2 Duration of Canadian citizenship Education Y 1 Support for private health care + X 3 Income + X 4 Possession of “Canadian values” + – +
    8. 8. Things to include in your model <ul><li>Identify the primary dependent and independent variables </li></ul><ul><li>Label the variables (dependent=Y, independent=X) </li></ul><ul><li>Draw causal arrows to indicate the direction of the relationship </li></ul><ul><li>Insert symbols above the causal arrows: + for positive and – for negative </li></ul><ul><ul><li>If either variable is measured at the nominal level, insert +/ – because the relationship does not have a direction </li></ul></ul><ul><ul><li>If the literature has contradictory findings, label the relationship with a ? </li></ul></ul><ul><li>Include all variables that could helps explain the relationship between the independent and dependent variables (intervening variables) or that come before the it (antecedent) </li></ul><ul><li>Circle the variables that will be the focus of your research </li></ul>
    9. 9. In-class Example <ul><li>Research question: What are the relationships between age, education and support for same sex marriage? </li></ul><ul><li>Background: We are conducting a research study on support for same sex marriage in Canada. Specifically, we are interested in whether one’s age and level of education affects one’s support for same sex marriage. </li></ul>
    10. 10. Modelling Support for Same Sex Marriage Y 1 Support for Same Sex Marriage X 1 Age - Initial Relationships A negative sign can be interpreted like this: As a person’s age increases their support for same sex marriage decreases. (Positive X Negative = Negative) Education Level X 2 +
    11. 11. Modelling Support for Same Sex Marriage <ul><li>Other research: However, as a result of our literature review we know that religiosity (the importance of religion in one’s life) also has an impact on support for same sex marriage. </li></ul><ul><li>The more importance that is placed on religion, the more likely that one is to believe that gays and lesbians should not be allowed to get married. </li></ul>
    12. 12. Modelling Support for Same Sex Marriage Y 1 Support for Same Sex Marriage X 3 Religiosity Age - X 1 - Education Level X 2 + A positive sign can be interpreted like this: The higher one’s level of education, the greater their support for same sex marriage. (Positive X Positive= Positive)
    13. 13. Modelling Support for Same Sex Marriage <ul><li>The literature also says that there is a relationship between religiosity, age and education. </li></ul><ul><li>We have to edit our model to take this into account. In this case, religiosity acts as an intervening variable . </li></ul>
    14. 14. Modeling Support for Same Sex Marriage Y 1 Support for Same Sex Marriage X 3 Religiosity Age - X 1 + Education Level X 2 - Religiosity acts as an intervening variable
    15. 15. Intervening Variable Model: Support for Same Sex Marriage Y 1 Support for Same Sex Marriage X 3 Religiosity Age - X 1 + Education Level X 2 - By including religiosity as an intervening variable, I am hypothesizing that religiosity is most important because young people, who are very religious are still less likely to support same-sex marriage than then young people who are not religious.
    16. 16. Source of Spuriousness Model <ul><li>While there is a relationship between X and Y, this relationship is non-causal because another variable influences both X and Y. </li></ul><ul><li>X and Y vary together because a third variable is influencing both of them. If we control for the third variable, there should no longer be a relationship between X and Y. </li></ul>Y X S/S
    17. 17. How to Create Models in MS Word <ul><li>You can create your causal model directly in Microsoft Word. </li></ul><ul><li>On the menu bar, click INSERT, PICTURE and AUTO SHAPES. Here you will find the arrows we use in the model. Click lines and then the arrow. Right click to start and drag to where you want the arrow to end. </li></ul><ul><li>Use text boxes to add labels and the +/ ─ symbols to your models. Click INSERT, TEXT BOX and Horizontal . Enter your text. If you right click on the box, clicking FORMAT TEXT BOX allows you to change the width and colour of the box (selecting the colour ‘white’ removes the box) </li></ul><ul><li>Arrange the text boxes and the arrows appropriately on your page. </li></ul><ul><li>How did I get the numbers beside the variables to look smaller (e.g. X 3 ). Highlight the number and right click on it. Select FONT and click the box beside SUBSCRIPT . </li></ul>
    18. 18. In-class Example <ul><li>Use Word to create a causal model. </li></ul><ul><li>Explain party support with two independent variables. </li></ul><ul><li>What might affect support for a political party? </li></ul><ul><li>Is the relationship positive or negative? </li></ul>
    19. 19. For Next Time... <ul><li>Assignment #2 is available this Friday, October 10 on Blackboard. </li></ul><ul><li>The assignment is due October 17 before midnight. </li></ul><ul><li>Next week we will cover descriptive statistics and diagrams. Read chapter 6 of the SPSS guide and sections B5, B7, B8 in in Appendix A of Jackson and Verberg </li></ul>