Greetings geniuses! Can you help me out with a basic issue? (On analyzing my data) - Page 2 - boards.ie

17-04-2012, 10:44   #16
Ostrom
Moderator

Join Date: Jul 2006
Location: Kildare
Posts: 3,415
Quote:
 Originally Posted by chris85 No grey area though, If they are dependent and you test assuming independence your results are completely meaningless. Needs to be sorted by either changing the test or test for independence to be sure. Otherwise worthless to be honest. At least some one else things the same as me here.
Try it another way (copied from Andy Field's Discovering Statistics);

"Independent-means t-test: This test is used when there are two experimental conditions and different participants were assigned to each condition (this is sometimes called the independent-measures or independent-samples t-test).

Dependent-means t-test: This test is used when there are two experimental conditions and the same participants took part in both conditions of the experiment (this test is sometimes referred to as the matched-pairs or paired-samples t-test)"

The OP's problem, as he describes, does not involve subjecting the same participants to two experimental conditions. An appropriate use of a dependent samples test in the above problem might test for equality of means between male recall on low attractiveness and male recall on high attractiveness. In this case, the samples are dependent, as this is now a case of repeat meaures. Comparing gender is not, because both groups are independent of each other.

17-04-2012, 11:30   #17
opinion guy
Closed Account

Join Date: Aug 2009
Posts: 10,561
efla you are confused on what are the samples in this study.

The samples in this case are not the male and female volunteers - those are the measuring apparatus - equivalent to a manual blood pressure cuff vs an automatic cuff.

The samples are the pictures of 50 faces being looked at.
The quality being measured is attractiveness - equivalent to any other property of an individual such as blood pressure.

Therefore the measurements are not independent and a paired analysis should be used.

Quote:
 It is completely different from the blood pressure example, in which variation is attributable to the intervention, rather than characteristics of the sample.
What intervention ? No where anywhere in this thread has anyone mentioned any interventions, neither in the OP's query nor in my BP example. I think you need to clarify your understanding of the terminology to be honest.

17-04-2012, 15:07   #18
Ostrom
Moderator

Join Date: Jul 2006
Location: Kildare
Posts: 3,415
Quote:
 Originally Posted by opinion guy efla you are confused on what are the samples in this study. The samples in this case are not the male and female volunteers - those are the measuring apparatus - equivalent to a manual blood pressure cuff vs an automatic cuff. The samples are the pictures of 50 faces being looked at. The quality being measured is attractiveness - equivalent to any other property of an individual such as blood pressure. Therefore the measurements are not independent and a paired analysis should be used.
This is not the correct meaning of the term sample. From first principles, based on the OP's initial outline;

"I have 2 groups: men and women"

Before we get to distinctions based on gender, the above contitutes the sample. A sample is a sub-set of a given population selected for study. The reason inferential tests such as the t-test are used is to infer the liklihood (probability) that an observed difference may be present in the parent population.

It makes no logical sense to state that the samples are the pictures; to what population are the inferences directed in this case?

A 'measuing apparatus' as you put it, is the instrument used to collect the data - a questionnaire, blood pressure cuff, or in this instance, a set of recall exercises. Within any given instrument, a number of measures may be employed; in this case, the primary variables the OP has measured are gender, and each respondents ability to recall a range of images ranked in terms of attractiveness.

Before we get to questions of data collection and analysis, the sample comprises two independent groups; males and female.

Quote:
 Originally Posted by opinion guy The samples are the pictures of 50 faces being looked at. The quality being measured is attractiveness - equivalent to any other property of an individual such as blood pressure. Therefore the measurements are not independent and a paired analysis should be used.
The quality being measured is not attractiveness, it is rate of recall according to levels of attractiveness, as per the OP. In any event, this does not matter, as the measure, or instrument used in this case to measure recall is the set of images. This is no different to how one might conceptualise a questionnaire; it is a set instrument administered to all participants.

The OP's initial question concerned inference for gender differences in recall ability;

H1: Mean recall differs between genders at a given level of attractiveness (remember this is bivariate)
H0: Mean recall is identical

As per my above post, independence is reckoned on sample independence (hence the term independent samples t-test); your confusion is due to your misapplication of the term sample. In either case, Im not sure how gender could function as a measuing apparatus; it features here merely as a sample strata, although, as the hypothesis implies, it is also used as an independent variable to account for differences in recall ability.

Quote:
 Originally Posted by opinion guy What intervention ? No where anywhere in this thread has anyone mentioned any interventions, neither in the OP's query nor in my BP example. I think you need to clarify your understanding of the terminology to be honest.
The term 'intervention' is used in repeat-measures experimental design to denote a difference due to manipulation, or application of some experimental condition to the sample group. A study of new teaching methods might measure standardised test scores on a sample of students before and after new methods were applied. In this case, the samples (students) are dependent (matched, paired), because this is a repeat measures design, and each subject appears in both test groups.

In the OP's example, this is not the case. Were he to collapse over gender and test for mean recall (for the whole sample) at low and high levels of attractiveness, this would call for a dependent test, as subjects in both groups (low and high) are now paired (i.e. the same individuals answered recall at low and high). To divide on gender, as the OP has, is to create two unique, independent groups to compare.

 (2) thanks from:
 27-04-2012, 03:00 #19 MonkeyBalls Registered User   Join Date: Aug 2007 Location: A big rock spinning through a void Posts: 435 Efla, that makes 100% sense. Thank you. Last edited by 440Hz; 21-05-2012 at 17:10.
27-04-2012, 11:29   #20
opinion guy
Closed Account

Join Date: Aug 2009
Posts: 10,561
Quote:
 Originally Posted by MonkeyBalls Efla, that makes 100% sense. Thank you.
A shame its not 100% valid.

<mod.snip>

Last edited by 440Hz; 21-05-2012 at 17:10.