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    Is this approach permissible?




    Hello everyone,

    I am not so familiar with statistics but I have a question concerning the computation of a variable.

    I have collected data on certain crowdfunding offerings. I scanned the initiaitves and checked it for the following (usage of twitter, facebook, youtube and updates) and coded it the following:

    Twitter (0=No, 1=Yes)
    Facebook (0=No, 1=Yes)
    Youtube (0=No, 1=Yes)
    Updates (0=No, 1=Yes)

    But I don't want to test, how they are using one particular channel. I would like to compute a Social Media index. How? Simply aggregating the data on these channels.

    Campaign A uses twitter and facebook scores a 2 then.

    Is such a thing permissible?

    Thank you very much. I highly appreciate it!

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    Re: Is this approach permissible?

    You haven't described what you're trying to accomplish or test.

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    Re: Is this approach permissible?

    Quote Originally Posted by derksheng View Post
    You haven't described what you're trying to accomplish or test.
    Well, here's the deal:

    I would like to investigate, whether successfully funded initiatives use more social media.

    The funded variable is in the same coding:

    Not funded = 0
    Funded = 1

    Got it?

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    Re: Is this approach permissible?

    You could create a new variable in the following way: Number of social medias (0, 1, 2, 3, 4), and then use a binary logistic regression with the funded variable as the dependent variable. Or you can run a logit regression with the four variables you now got to see which contribute most to the funded variable.

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    Re: Is this approach permissible?

    Sounds like a probit would be good with the binary Funded variable as the dependent variable.

    I'm not knowledgeable enough to know whether it'd be better to construct an index or to include the 4 social media variables as separate regressors. Multicollinearity would be one of the necessary considerations.

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    Re: Is this approach permissible?

    Well, I don't need to go into any detail concering which social media channel contributes most and so on. That's why I thought a computation like this is enough.

    What do you think?

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    Re: Is this approach permissible?

    In that case make the index like you originally proposed, and use either a probit or logit regression with Funded as the dependent variable.

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    Re: Is this approach permissible?

    hey derksheng,

    thanks for coming back so quick. Why can't I use the Social Media index as the dependent and the funded as the independent and compute a independent t-test?

    Cheers

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    Re: Is this approach permissible?

    Probit/Logit are nice and can only be done with binary dependent variables. It also has the interpretation of "given X social media usage, we expect them to be funded Y % of the time".

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    Re: Is this approach permissible?

    @derksheng
    I don't get it. When I compute the Social Media Index, I will receive a variable that won't be binary (0;1) any more. It'll be 0-4. How does this then look like?!

    Thank you!

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    Re: Is this approach permissible?

    The model you should estimate is:

    (1) Funded_i = a + b*Index_i + error.

    Funded is binary, Index_i is \in {0,1,2,3,4}.

    Estimate it with Probit or Logit, which is automated with any econometrics or statistics computer program.

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    Re: Is this approach permissible?

    Quote Originally Posted by derksheng View Post
    (1) Funded_i = a + b*Index_i + error.
    Isn't it like this

    Funded_i = 1/(1+e^-(a+b*Index_i+error))?
    Quote Originally Posted by derksheng
    Probit/Logit are nice and can only be done with binary dependent variables.
    It's possible to use multinomial logistic regression as well. Then the DV can take the form of other than binary.

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    Re: Is this approach permissible?

    If you think you've come up with a betting plan that turns a negative expected turnover into a positive, then drop that thought!

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    Re: Is this approach permissible?

    Quote Originally Posted by Englund View Post
    Isn't it like this

    Funded_i = 1/(1+e^-(a+b*Index_i+error))?
    That's closer but still not quite right (the error isn't inside there).

    Typically you say something like

    Y_i \sim \mbox{Binomial}(n = 1, p = p_i)

    \log(\frac{p_i}{1 - p_i}) = \beta_0 + \beta_1X_i

    (which is equivalent to)
    p_i = \frac{1}{1+\exp(-(\beta_0+\beta_1X_i))}

    In this case Y is Funded and X is Index. N doesn't have to be 1 (we could replace 1 with n_i but typically logistic regression is done on bernoulli trials).

    The fun thing about generalized linear models (which logistic regression and probit regression are a part of) is that you get to get out of the "signal + noise" type of mindset where you specify an expected value and then some error on top of that. Generalized linear models make you think in terms of a response distribution conditional on the covariates.
    I don't have emotions and sometimes that makes me very sad.

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    Re: Is this approach permissible?


    Quote Originally Posted by Englund View Post
    If you think you've come up with a betting plan that turns a negative expected turnover into a positive, then drop that thought!
    Was this meant for this thread?
    I don't have emotions and sometimes that makes me very sad.

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