SPSS data entry & which test?!?

Discussion in 'Code Forum' started by lilysdaisys, Aug 24, 2008.

  1. lilysdaisys

    lilysdaisys New Member

    Hi all,<br />
    <br />
    I'm studying moral development (SRMS SCORE) and play opportunities (TIME SCORE) and the differences between urban and rural areas.<br />
    My variables are as follows age, gender, region, SRMS moral dev score, and time playing with social interaction, time for play without social interaction.<br />
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    I have the data for each participant (n=292) entered in SPSS, except for the time variables... these I only have the average for the 2 regions; urban and rural.<br />
    <br />
    I was thinking that I might run a t test? or would ANOVA be better?<br />
    <br />
    Can you also clarify which are my IV's and DV's?... it's so confusing having so many variables... gosh, am I doing this correctly at all?<br />
    <br />
    I don't know how to enter the data for 'time' variables as I only have the average for each region (urban/rural), and not individual like all my other variables... can anyone advise me on how to rectify this or work with this?<br />
    <br />
    I really appreciate any help that you can give... I'm at my wits end!!<br />
    <br />
    Thank you<br />
    <br />
    <br />
    <br />
     
  2. Blah

    Blah New Member

    If I understand properly, you can do an independent two-sample t-test for difference in means for the urban vs rural times (as long as you have a standard deviation) to determine if the time spent is significantly different, but that doesn't tell you if or how the effect varies with time spent.

    If there is no significant difference, presumably the difference in time probably doesn't matter. However, urban vs rural might still make a difference for other (unknown) reasons.

    The dependent variable is moral development (I don't know what SRMS might mean). The (possible) independent variables are the others -- age, gender, and region. Assuming region means urban vs rural, then it is a dummy variable whose value is 0 or 1. Likewise, gender is a dummy variable whose value is 0/1.

    You can then do regressions which will describe moral development as a function (largely) of age, and whether or not gender or region make any difference. Differences in gender will show up (or not) as different y-intercepts in the regression lines.
     

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