standardized tests

Examples of standardized tests in the following topics:

• Standardized Tests

  • A standardized test is a test that is administered and scored in a consistent manner.
  • A standardized test is a test that is administered and scored in a consistent manner.
  • Finally, critics have expressed concern that standardized tests may create testing bias.
  • Students must pass a standardized test in order to graduate from high school.
  • The most common standardized tests for applying to college are the SAT and ACT.

• Coleman's Study of Between-School Effects in American Education

  • In 1966, the Coleman Report launched a debate about "school effects," desegregation and busing, and cultural bias in standardized tests.
  • It also helped define debates over desegregation, busing, and cultural bias in standardized tests.
  • The Coleman Report also fed the debate over the validity of standardized testing.
  • Importantly, though, the report pointed out that the tests administered in these schools were not measuring intelligence, but rather an ability to learn and perform in the American environment.
  • The report states: "These tests do not measure intelligence, nor attitudes, nor qualities of character.

• Introduction to describing one network

  • Second, many of tools of standard inferential statistics that we learned from the study of the distributions of attributes do not apply directly to network data.
  • Most of the standard formulas for calculating estimated standard errors, computing test statistics, and assessing the probability of null hypotheses that we learned in basic statistics don't work with network data (and, if used, can give us "false positive" answers more often than "false negative").
  • The standard formulas for computing standard errors and inferential tests on attributes generally assume independent observations.
  • Instead, alternative numerical approaches to estimating standard errors for network statistics are used.

• Hypotheses about two paired means or densities

  • Network>Compare densities>Paired (same node) compares the densities of two relations for the same actors, and calculates estimated standard errors to test differences by bootstrap methods.
  • When both relations are valued, this is a test for a difference in the mean tie strengths of the two relations.
  • Results for both the standard approach and the bootstrap approach (this time, we ran 10,000 sub-samples) are reported in the output.
  • The standard error of the difference by the classical method is .0697; the standard error by bootstrap estimate is .1237.
  • Test for the difference of density in the Knoke information and money exchange relations

• Hypotheses about the means of multiple groups

  • The procedure Tools>Testing Hypotheses>Node-level>Anova provides the regular OLS approach to estimating differences in group means.
  • Because our observations are not independent, the procedure of estimating standard errors by random replications is also applied.
  • The dialog for Tools>Testing Hypotheses>Node-level>Anova looks very much like Tools>Testing Hypotheses>Node-level>T-test, so we won't display it.
  • One-way ANOVA of eigenvector centrality of California political donors, with permutation-based standard errors and tests

• Hypotheses about one mean or density

  • The standard deviation of the sampling distribution of a statistic (how much variation we would expect to see from sample to sample just by random chance) is called the standard error.
  • If we used this for our test, the test statistic would be -.4556//.0528 = 8.6 which would be highly significant as a t-test with N-1 degrees of freedom.
  • Using this alternative standard error based on random draws from the observed sample, our test statistic is -3.7943.
  • This test is also significant (p = .0002).
  • In general, the standard inferential formulas for computing expected sampling variability (i.e. standard errors) give unrealistically small values for network data.

• Hypotheses about the means of two groups

  • Figure 18.10 shows the dialog for Tools>Testing Hypotheses>Node-level>T-Test to set up this test.
  • For each of these trials, the scores on normed Freeman degree centralization are randomly permuted (that is, randomly assigned to government or non-government, proportional to the number of each type. ) The standard deviation of this distribution based on random trials becomes the estimated standard error for our test.
  • This would seem to support our hypothesis; but tests of statistical significance urge considerable caution.
  • UCINET does not print the estimated standard error, or the values of the conventional two-group t-test.
  • Test for difference in mean normed degree centrality of Knoke government and non-government organizations

• Regressing position on attributes

  • Tools>Testing Hypotheses>Node-level>Regression will compute basic linear multiple regression statistics by OLS, and estimate standard errors and significance using the random permutations method for constructing sampling distributions of R-squared and slope coefficients.
  • The R-squared is very high for this simple model (.987), and highly significant using permutation tests ( p = .014).
  • What differs here is the recognition that the actors are not independent, so that estimation of standard errors by simulation, rather than by standard formula, is necessary.
  • Multiple regression of eigenvector centrality with permutation based significance tests
  • Dialog for Tools>Testing Hypotheses>Node-level>Regression for California donor's eigenvector centrality

• Introduction to explaining attributes of networked actors

  • In the previous section we examined methods for testing differences and association among whole networks.
  • In most cases, standard statistical tools for the analysis of variables can be applied to describe differences and associations.
  • But, standard statistical tools for the analysis of variables cannot be applied to inferential questions -- hypothesis or significance tests, because the individuals we are examining are not independent observations drawn at random from some large population.
  • Instead of applying the normal formulas (i.e. those built into statistical software packages and discussed in most basic statistics texts), we need to use other methods to get more correct estimates of the reliability and stability of estimates (i.e. standard errors).
  • The "boot-strapping" approach (estimating the variation of estimates of the parameter of interest from large numbers of random sub-samples of actors) can be applied in some cases; in other cases, the idea of random permutation can be applied to generate correct standard errors.

• The Gifted

  • There is no standard definition of "gifted," nor a standard way of implementing gifted education.
  • Though gifted education programs are widespread, there is no standard definition of "gifted," nor a standard way of implementing gifted education.
  • In compacting, students are pre-tested to determine which skills or content they have already mastered, thus allowing students to skip repetitive practice.
  • Early IQ tests were notorious for producing higher IQ scores for privileged races and classes and lower scores for disadvantaged subgroups.
  • Although IQ tests have changed substantially over the past half century, and many objections to the early tests have been addressed by "culture neutral," IQ testing remains controversial.