T-Tests and Chi2

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					    T-Tests and Chi2

Does your sample data reflect the
population from which it is drawn
              from?
     Single Group Z and T-Tests
• The basic goal of these simple tests is to
  show that the distribution of the given data
  under examination are not produced by
  chance and that there is some systematic
  pattern therein.
• Main point is to show the mean of a
  sample is reflective of the population.

• Salkind’s text skips a discussion of single
  group/sample T-Tests.
           Review of Z-Tests
• Recall that a Z-score can measure the location of
  a given value on a normal distribution, which can
  be expressed as a probability.

• A Z-Test uses the normal distribution to obtain a
  test statistic based on some data that can be
  compared with a sampling distribution of chance,
  which is an abstract construction drawn from the
  data.

• This is a parameter estimation, which is an
  inference of a sample based on a population of
  data.
       Problem with Z Tests
• But because we do not often know the
  population variance, σ2, we estimate a
  single “point estimate” or value
  (sample mean).

• However, this sample mean may vary
  greatly from the real population mean,
  μ. This error is called “sampling error.”
        Problem with Z Tests
• A confidence interval is set up to estimate μ.
  This is a range of values that is likely to
  include the value of the population mean (at
  the center of the interval). The larger the
  sample, the more the sample mean should
  equal the population mean, but there may be
  some error within the confidence interval.
  How far is the X from μ ?
          Student’s T-Test
• Problem: We may not know the mean and
  variance of some populations, which
  means we cannot do a Z-Test. In this
  case, we use a T-test, Student’s T to be
  specific, for use with a single group or
  sample of data.

• Again, this is when we are not looking
  at different groups but a sample of data
  as an entirety. We will next examine
  differences in groups.
           Student’s T-Test
• One uses this test when the population
  variance is unknown, as is usually the
  case in the social sciences.
• The standard error of the sampling
  distribution of the sample mean is
  estimated.
• A t distribution (not normal curve, more
  platykurtic but mean=0) is used to create
  confidence intervals, like critical values.
             T Distribution
• Very similar to the Z distribution by
  assuming normality.

• Normality is obtained after about 100 data
  observations.

• Basic rule of parameter estimation: the
  higher the obs (N) of sample the more
  reflective of overall population.
 The t formula
       y  y
    t
         Sy
           N 1

CI  Y  t / 2 (S y / N 1)

For α =.05 and N=30 , t =2.045
95% CI using t-test
      • Mean= 20
        • Sy = 5
        • N= 20


20± 2.086 (5/19) =
   20.55 upper
   19.45 lower
    T-Tests

Independent Samples
     T-Tests of Independence
• Used to test whether there is a significant
  difference between the means of two
  samples.

• We are testing for independence, meaning
  the two samples are related or not.

• This is a one-time test, not over time with
  multiple observations.
      T-Test of Independence
• Useful in experiments where people are
  assigned to two groups, when there should
  be no differences, and then introduce
  Independent variables (treatment) to see if
  groups have real differences, which would
  be attributable to introduced X variable.
  This implies the samples are from different
  populations (with different μ).

• This is the Completely Randomized Two-
  Group Design.
For example, we can take a random set of
independent voters who have not made up their
minds about who to vote for in the 2004 election.
But we have another suspicion:

H1: watching campaign commercials increases
consumption of Twinkies (snackie cakes), or μ1≠ μ2

Null is μ1= μ2
After one group watches the commercials, but not
the other, we measure Twinkie in-take. We find that
indeed the group exposed to political commercials
indeed ate more Twinkies. We thus conclude that
political advertising leads to obesity.
          Two Sample Difference of Means T-Test


                              X1  X 2
   t
                                   2            
          (n1  1) s1  (n2  1) s 2  n1  n2  
                     2
                                             
                n1  n2  2           n1n2  
        

                                    2
        (n1  1) s1  (n2  1) s2       Pooled variance of the two groups
                  2

Sp2 =
               n1  n2  2

          n1  n2     = common standard deviation of two groups
                  
           n1n2 
Two Sample Difference of Means T-Test

• The nominator of the equation captures
  difference in means, while the
  denominator captures the variation within
  and between each group.

• Important point: of interest is the difference
  between the sample means, not sample
  and population means. However, rejecting
  the null means that the two groups under
  analysis have different population means.
                    An example
• Test on GRE verbal test scores by gender:
Females: mean = 50.9, variance = 47.553, n=6
Males: mean=41.5, variance= 49.544, n=10
                     50.9  41.5
t
      (6  1)47.553  (10  1)49.544  6  10  
                                      6(10)  
                 6  10  2                  
               9 .4
t
        48 .826 (. 26667 )   
         9.4
t
         13 .02

    9.4                  Now what do we do with this
t        2.605         obtained value?
   3.608
Steps of Testing and Significance
1. Statement of null hypothesis: if there is
   not one then how can you be wrong?
2. Set Alpha Level of Risk: .10, .05, .01
3. Selection of appropriate test statistic:
   T-test, chi2, regression, etc.
4. Computation of statistical value: get
   obtained value.
5. Compare obtained value to critical
   value: done for you for most methods
   in most statistical packages.
Steps of Testing and Significance

6. Comparison of the obtained and
   critical values.
7. If obtained value is more extreme than
   critical value, you may reject the null
   hypothesis. In other words, you have
   significant results.
8. If point seven above is not true,
   obtained is lower than critical, then
   null is not rejected.
                           The critical values
                           are set by moving
                           toward the tails of the
                           distribution. The
                           higher the
                           significance
                           threshold, the more
                           space under the tail.



Also, hypothesis testing can entail a one or two-
tailed test, depending on if a hypothesis is
directional (increase/decrease) in nature.
Steps of Testing and Significance
• The curve represents all of the possible
  outcomes for a given hypothesis.

• In this manner we move from talking
  about a distribution of data to a
  distribution of potential values for a
  sample of data.
        GRE Verbal Example
Obtained Value: 2.605
Critical Value?
Degrees of Freedom: number of cases left after
  subtracting 1 for each sample.

Is the null hypothesis supported?

Answer: Indeed, women have higher verbal
 skills and this is statistically significant. This
 means that the mean scores of each gender
 as a population are different.
     Let’s try another sample
• D:\POLS 5300 FA04\Comparing Means
  examples.xls

• Type in the data in SPSS
           Paired T-Tests
• We use Paired T-Tests, test of
  dependence, to examine a single sample
  subjects/units under two conditions,
  such as pretest - posttest experiment.

• For example, we can examine whether a
  group of students improves if they retake
  the GRE exam. The T-test examines if
  there is any significant difference between
  the two studies. If so, then possibly
  something like studying more made a
  difference.
    D
n D  ( D )
    2         2

                  ΣD = sum differences
    (n  1)       between groups, plus it is
                  squared.

                  n = number of paired
                  groups
            Paired T-Tests
• Unlike a test for independence, this test
  requires that the two groups/samples being
  evaluated are dependent upon each other.

• For example, we can use a paired t-test to
  examine two sets of scores across time as
  long as they come from the same students.

• If you are doing more than two groups, use
  ANOVA.
          Let’s Go to SPSS
• Using the data from last time, we will now
  analyze the Pre-test/Post-test data for
  GRE exams.

• D:\POLS 5300 FA04\Comparing Means
  examples.xls
                                 Paired Samples Statistics

                                                                                               Std. Error
                                   Mean                    N            Std. Deviation           Mean
     Pair         TESTSCR1         409.69                      16             200.459            50.115
     1            TESTSCR2         448.88                      16             152.679            38.170



                                Paired Samples Correlations

                                                           N             Correlation               Sig.
         Pair 1   TESTSCR1 & TESTSCR2                           16             .959                  .000



                                                     Paired Samples Test

                                                    Paired Differences
                                                                            95% Confidence
                                                                             Interval of the
                                                           Std. Error          Difference
                                Mean      Std. Deviation     Mean          Lower        Upper         t       df        Sig. (2-tailed)
Pair 1    TESTSCR1 - TESTSCR2    -39.19          69.155      17.289         -76.04         -2.34     -2.267        15             .039




   H0: μ scr1 = μscr2             whereas research hypothesis H1:                                  H1 :  scr 2   scr1
    Nonparametric Test of Chi2
• Used when too many assumptions are violated in
  T-Tests:
  – Sample size to small to reflect population
  – Data are not continuous and thus appropriate for
    parametric tests based on normal distributions.


• Chi2 is another way of showing that some pattern
  in data is not created randomly by chance.

• Chi2 can be one or two dimensional.
  Nonparametric Test of Chi2
• Again, the basic question is what you are
  observing in some given data created by
  chance or through some systematic
  process?
               (O  E )  2
        
         2

                   E
       O  observedfrequency
       E  exp ectedfrequecy
 Nonparametric Test of Chi2
• The null hypothesis we are testing here
  is that the proportion of occurrences in
  each category are equal to each other.
  Our research hypothesis is that they are
  not equal.

Given the sample size, how many cases
 could we expect in each category
 (n/#categories)? The obtained/critical
 value estimation will provide a coefficient
 and a Pr. that the results are random.
       Cross-Tabs and Chi2
• One often encounters chi2 with cross-
  tabulations, which are usually used
  descriptively but can be used to test
  hypotheses.
Party Affiliation * gender (past, present) Crosstabulation

Count
                         gender (past, present)
                           Fem          Male           Total
Party Affiliation Dem           5            6              11
                  GOP           5            8              13
Total                          10           14              24


                                    Chi-Square Tests

                                                 Asymp. Sig.     Exact Sig.   Exact Sig.
                        Value           df        (2-sided)       (2-sided)    (1-sided)
Pearson Chi-Square         .120 b            1          .729
                    a
Continuity Correction      .000              1         1.000
Likelihood Ratio           .120              1          .729
Fisher's Exact Test                                                  1.000         .527
Linear-by-Linear
                           .115              1          .735
Association
N of Valid Cases             24
   a. Computed only for a 2x2 table
   b. 1 cells (25.0%) have expected count less than 5. The minimum expected count is
      4.58.

				
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posted:11/24/2011
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