While agreeing in part with Stephen, it sounds like you do not have a
random sample. Significance testing pertains to random samples, asking
about the probability of getting a result as strong as you observe if
another random sample were taken. If your data are the entire universe
to which you are generalizing (ex., all your clients), then barring
measurement error, even very small results are true results and
significance levels do not apply. If you have a non-random sample, you
would like to know the probability of a result of given strength if you
took another sample, but this is unknowable. Significance levels for
non-random data will be in error to an unknown degree.
In an experimental setting, if subjects are randomly assigned to groups,
statistical inference may be made but, as Knapp (2009) notes, "the
inference is to all possible randomizations for the given sample, not to
the population from which the sample was [non-randomly] drawn. " That
is, if one uses significance testing for non-random subjects who are
randomly assigned, one is asking about the probability of getting a
result of the given strength if one randomizes again.
Dave
On 12/9/2011 12:20 PM, Statisticsdoc Consulting wrote:
> Ariel,
> I would start by computing the internal consistency of the computed
> scores, and then examining the distribution of scores (o determine the
> degree to which they follow a normal distribution. The first will give
> a conservative estimate of how reliable the scores are in your sample.
> The second will provide guidance about whether the assumptions for
> parametric or non parametric analysis should be used.
> Best,
> Stephen Brand
> www.StatisticsDoc.com
> ------------------------------------------------------------------------
> *From: * Ariel Barak <
[hidden email]>
> *Sender: * "SPSSX(r) Discussion" <
[hidden email]>
> *Date: *Fri, 9 Dec 2011 10:58:55 -0600
> *To: *<
[hidden email]>
> *ReplyTo: * Ariel Barak <
[hidden email]>
> *Subject: *Survey Analysis Questions
>
> Hello All,
>
>
> Background: I have 86 satisfaction surveys from families who have
> children in a program that provides case management services. The 20
> likert questions (Strongly Disagree, Disagree, Undecided, Agree,
> Strongly Agree) are scored 1 through 5 and are broken into 5 domains.
> Per the instructions from one of the survey creators, I have only
> calculated domain scores (averages) if 2/3 or more of the questions
> for a given domain have been answered and most respondents answered
> all of the questions.
>
>
> I would like to test whether there are statistically significant
> differences in the domain scores based on the following factors: Race,
> Sex, Length of Program Service (less than 365 days, 365+ days),
> program provider (there are two).
>
> Second, I also have data related to whether parent(s) attended team
> meetings via a database, regardless of whether or not they responded
> to the survey. For those who completed the survey, I would like to
> test for whether there are correlations between the domains mentioned
> above and the percent of team meetings attended by a family member. I
> would also like to know if it is possible to state which domains have
> the strongest correlations to team meeting attendance by the different
> factors above: race, sex, etc.
>
>
> Could someone provide me with some help on what the appropriate
> statistical test(s) are and what the syntax would look like?
>
>
> Any assistance would be greatly appreciated.
>
>
> Thanks,
>
> Ariel
>
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