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- 1. POLI 399 – Research Methods Week Four Causal Modelling
- 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. 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” + – +
- 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 +
- 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)
- 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. 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.
Notas del editor
- 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.