Standardized Test Scores

Examples of Standardized Test Scores in the following topics:

• Staking the Desk: Unequal Funding

  • What's more, because colleges want to maintain their rankings in various college ranking systems, colleges favor students with higher standardized test scores and aggressively recruit them using "merit" scholarships.
  • In other words, affluent students who can pay for college often do not have to because the advantages they received attending better elementary, middle, and high schools translated into higher standardized test scores, which are attractive to universities when it comes to recruiting.

• 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.
  • One of the main advantages of standardized testing is that the results can be empirically documented; the test scores can be shown to have a relative degree of validity and reliability, being generalizable and replicable.
  • Finally, critics have expressed concern that standardized tests may create testing bias.
  • Some standardized testing uses multiple-choice tests, which are relatively inexpensive to score, but any form of assessment can be used.

• Institutional Prejudice or Discrimination

  • This disparity include standardized test scores, grade point average, dropout rates and college enrollment and/or completion rates.

• 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.
  • The report showed that, in general, white students scored higher than black students, but it also showed significant overlap in scores: 15 percent of black students fell within the same range of academic accomplishment as the upper 50 percent of white students.
  • This same group of blacks, however, scored higher than the other 50 percent of whites.

• 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

• Introduction to describing one network

  • We learn about the mean of a set of scores on the variable "income."
  • 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").
  • This is because the "observations" or scores in network data are not "independent" samplings from populations.
  • 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.

• 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.
  • Different schools may set different cut-offs for defining giftedness, but a common standard is the top 2% of students with an IQ score of about 140 or above.
  • 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.

• 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.
  • The differences in group means account for 78% of the total variance in eigenvector centrality scores among the donors.
  • One-way ANOVA of eigenvector centrality of California political donors, with permutation-based standard errors and tests

• 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.
  • POSCOAL is the mean number of times that each donor participates on the same side of issues with other donors (a negative score indicates opposition to other donors).
  • As before, the coefficients are generated by standard OLS linear modeling techniques, and are based on comparing scores on independent and dependent attributes of individual actors.
  • 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

• Correlation between two networks with the same actors

  • Tools>Testing Hypotheses>Dyadic (QAP)>QAP Correlation calculates measures of nominal, ordinal, and interval association between the relations in two matrices, and uses quadratic assignment procedures to develop standard errors to test for the significance of association.
  • The Pearson correlation is a standard measure when both matrices have valued relations measured at the interval level.
  • Simple matching and the Jaccard coefficient are reasonable measures when both relations are binary; the Hamming distance is a measure of dissimilarity or distance between the scores in one matrix and the scores in the other (it is the number of values that differ, element-wise, from one matrix to the other).
  • This value, as you can see, is not necessarily zero -- because different measures of association will have limited ranges of values based on the distributions of scores in the two matrices.
  • The appropriate one of these values to test the null hypothesis of no association is shown in the column "Signif."