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by: Fatoumata sall
Fatoumata sall

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Lecture 8
Introduction to Anthropology
professor Niezel
Class Notes
25 ?




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This 4 page Class Notes was uploaded by Fatoumata sall on Friday April 1, 2016. The Class Notes belongs to ANTH 101 001 at University of Delaware taught by professor Niezel in Spring 2016. Since its upload, it has received 11 views. For similar materials see Introduction to Anthropology in anthropology, evolution, sphr at University of Delaware.

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Date Created: 04/01/16
Lecture 8 Nominal- is the base, foundation, naming, set of characteristics and we give them a common name, there is no higher or less then EX: man and women, democratic r republicans Ordinal- given them name, but we are ranking them, high, medium, low, big, low but we do not say how much then Interval- everything in nominal and ordinal, but now we have units so we can say how much or how much less then. 0 does not mean none, therefore we can have negative number. EX: Temperature Ratio: the only difference with the others is that 0 means none, no negative numbers Ex: counts and amounts. All measurement falls under one of these categories PRINCIPALES OF MEASUREMENT LIKERT SCALE Horizontal and vertical but don’t do the vertical because it takes up too much space Each point scale is not explicitly labeled, they are interval measurement RELIABITY OF MEASUREMENT We want our measurement to be accurate BASIC PREMISE We are never going to have perfect measurement; the error of measurement is random It is important because we are interested in discovering relationships, our relationships is systematic and random cannot be systemic. We are always striving for better measurement but we are never get there. So therefore we can never find the perfect relationships. TWO MEANINGS OF RELIABLE STABLE: measure change or lack of change Accurate: measurement that contains little error, if we are accurate we will get stability Ex: the bathroom scale fluctuates because it is old Degree of error: The equation assumes X total= score we measure, obtain, it is the combination of two pieces X true or real + X error We can only see total score and we can’t separate an individual score but we can make them at the level of the sample Difference among people and variable= variance We can only do so in terms of percentage Page 56 . Reliability is going to be expressed as a percent VARAtrue/VARtotal We want the percentage to be large The percentage is always large (.7) 70% of true variance is minimum acceptable (not in the packet) ( PAGE 57) we need two independent people and then see that every unit starts same place in a stream and ends in the same place stream if these two are coterms they would have the exact same number of thought unit if they didn’t stop the same place, they wont be coterms GUETZKOW’S U U= 01 - 02/01 + 02 If they were cotermenus the denominator of the equation would be o The numerator is the error of unitizing We want smaller U, the smaller the U the closer we are to the terminus When we talk reliability of measurement the people are doing it Reliability unitizing the scale is doing it Reliability of observations ( are people too ) INTRA= within INTRA= within INTER= between (is defined as agreement) (we rely on c) Observation Is about consistency (did they see the same thing at the same point in time), are the scale consistent as well too. Reliability of observation is different from the one in the scale -none of us are consistency, if there is a lack of consistency its about the observer, we only know about the object through the observer. - anything off diagonal is a disagreement, we do adjust percentage of agreement for chance PAGE 58 How do we know we are measuring what we say we are measuring? U question the validity of of not the reliability There is no numeric index of validity, validity is not represented by one number Validity is theory driven - Content validity (sometimes called face validity) Are measure valid on the face of it. Predictive validity Can I measurement predict what’s external to themselves. EX: lsat,act, education, or manifuctury, so we don’t predict Construct validity Do I theories behave like we said they would We would say that are measure are invalid Measure can’t be valid if they are not reliable Reliability is necessary but not sufficient for validity Establishing validity is about argument Construct validity is the most important LECTURE 9 DATA DESCRIPTION First thing describes the data We are dealing with a data from a larger population WE WANT TO KNOW TWO THINGS -u’nivarian measurement where is the center of the data, the central tendencies? how dispersed are the data, are they clustered or spread? We turn the data into ranks, The mean is expressed in the same metric as the original score, it can be distorted by an extreme score Metric how we measure stuff How do we prevent the mean from being disorted? Frequency histogram The bars represent frequency of occurrence; the tails of the distribution are symmetrical The spread of the data How does the score disperse around the mean? Page 66 We want a single number that represents average number 1.Raw deviation is untransformed Synonyms for deviation is difference N~ REPRESENTS THE NUMBER OF SAMPLE Absolute values to get rid of negative signs and get a picture sum and sum it up to get 86 Large deviation from the mean contributes more than small deviation Step 1 Subtract mean from each and every individual y (inside ()) 2.Square each and every one of N deviation 3.sum them all ( it is in the unit square unit) sum square=442 this is why Y vary N: IS The number of the score Y: is how many they are In the standard deviation the most important word in the definition is the original units The degree of free represent number of scores that are free to take on any value along the continuum of measurement Standard deviation is the descriptive index spread. The variance iws the standard deviation squared Both the mean and the standard deviation are Constance Page 74 Every sample of Z score is 0 or centering the data at the mean of 0] If the variance is 1 what us the sum of c square Lecture 10 BIVARIATE RELATIONSHIPS To what extent do the two variables of interest exhibit a concomitant change in their values. Measurement Error Is a random variation Substantive reasons Page 80 As gets larger so does y Inverse relationship goes down to the right We want the sampe to be gentle 1. Does the relationship exist 2. Is the relationship positive or negatice 3. Is the large an small The numerator is called sum of product We are calculating the index of covariance Index of covariance always I cannot and will not tell us magnitude. - How large is the relationship? - Substantive relations: it is the degrees of freedom - Page 90 N-1 is a constant and they all have that in common 4258284523688696 01/20 874 $206


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