An observation and analysis of the

How to Write a Summary of an Article? Ayo a second grade teacher, in addition to Mr. Which educational theories were employed?

An observation and analysis of the

BOX Statistical Decompositions over Time One way to analyze the sources of change over time in the outcomes of various groups is to differentiate between periods. Blau and Beller and many other studies do this. However, the change in each of the two components combines the effects of changes in the race gap in characteristics and in the race gap in coefficients.

A more detailed decomposition of change over time can be obtained as follows. For concreteness, let the outcome Y denote the wage rate. The second term is the effect of changes over time in the coefficients for group 1, holding differences in observed characteristics fixed.

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The third term is the effect of changes over time in the gap in the coefficients between the two groups. The fourth term accounts for the fact that changes over time in the characteristics of group 2 alter the consequences of differences in Page Share Cite Suggested Citation: The National Academies Press.

A limitation of this decomposition is that it does not provide much insight into how the wage gap is affected by changes in the overall wage distribution, such An observation and analysis of the occurred over the s when the returns to skill rose rapidly.

Increases in the dispersion of wages will increase the gap between the mean wages of whites and blacks given that whites are above the mean and blacks beloweven if there is no change in the skill distributions of whites relative to blacks or in the level of discrimination.

Altonji and Blank provide a detailed discussion of the methods used in these papers. A brief summary of Juhn et al. Using data from the Current Population Survey, they find that, between andchanges in levels of education and experience reduced the black—white wage gap in logs for men by 0.

They find that 0. In short, they find that relative wages of blacks declined because black men were disproportionately located at the lower end of an increasingly unequal wage distribution. An alternative approach, used by Murnane et al. They find a smaller race gap in the s that is less sensitive to inclusion of test scores, particularly for males.

This result is broadly consistent with the analysis in Juhn et al.

An observation and analysis of the

The most informative studies use explanatory variables that both measure the most important determinants of the outcome under study and are likely to have different distributions by race. But the residual race differential may include not only any effect of discrimination but also the effect of other omitted factors that would generate different outcomes by race even in the absence of discrimination.

Hence the unexplained gap may overestimate or underestimate the effects of discrimination. Except in very limited or special circumstances e. Again, the crucial problem that must be addressed to draw a causal inference from observa- 2 See also DiNardo et al. One of the key controversies in audit studies is the extent to which the designs can approach the classic random assignment paradigm Heckman, See the discussion in Chapter 6.

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Page Share Cite Suggested Citation: Essentially, the inference that race has a causal effect on an outcome because of racial discrimination is drawn by shaping a set of statistical correlations using other information and assumptions formalized in a model of how the process under study is determined.

This approach is typical of statistical analysis of observational data and is not unique to the problem of discrimination. Sometimes, the most we can claim is that the evidence is consistent with a certain explanation, with the caveat that other plausible explanations cannot be excluded.

Below we identify and discuss common obstacles to causal inference and some of the solutions proposed in the literature. We begin with a brief introduction to the essential role of theoretically informed models and adequate data in drawing causal inferences from observational data.

We then illustrate this point with an extended example involving hiring decisions in the labor market. Finally, we discuss two of the most important sources of bias in observation studies of discrimination—omitted variables bias and sample selection bias. Depending on the particular process and context, one may have more or less information on which to base a theoretical model and then translate it into a statistical model.

Laboratory experiments see Chapter 6 are designed precisely to test the plausibility of various detailed theoretical frameworks. As discussed in detail in Chapter 5there is a growing literature that formalizes the assumptions and the deductive process needed to draw cause-and-effect inferences from statistical data.

The key idea underlying this literature is the hypothetical counterfactual introduced in Chapter 5: What would have happened if the applicant for a job or rental housing had been white rather than nonwhite but nothing else had changed?

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Obviously, the counterfactual situation cannot be observed and compared with what actually occurred. Therefore, to draw a causal inference from experimental or observational data, it is necessary to specify assumptions and conditions 4 Page Share Cite Suggested Citation: Assumptions from the causal literature are particularly important for justifying the use of regression methods for drawing causal inferences.

To draw inferences from running regressions on observational data, substantial prior knowledge about the mechanisms that generated the data must be used to support the necessary assumptions.

Studies vary substantially in the degree to which the necessary assumptions are adequately justified. Below we discuss some of the specific issues that must be addressed in such models and their assumptions to draw causal inferences.The perfect model is a mental image of the correct technique.

Sometimes a video or photographic image can be used to compare with a live performance. However, good coaches will know what they are. Values made meaningful by quantifying into specific units. Measurements act as labels which make those values more useful in terms of details. For example, instead of saying that someone is tall, we can specify a measurement and specify that the individual is 6 feet tall.

Jennifer Isaacs is a prominent Australian writer, art consultant and independent curator. A pioneer in moves to gain respect for Aboriginal culture, she is the author of seminal books on Aboriginal art, religion, plant use, food, medicine and oral history.

In she was made a Member of the Order of Australia in recognition of her work promoting Aboriginal culture and assisting Aboriginal. Classroom Observation Analysis. Abstract Many instructional approaches exist that have been developed to reach more students.

Teachers have to select the instructional approaches that work best for students. These approaches have been tested and researched from various theoretical perspectives. ANALYSIS OF OBSERVATION(S) **Please use the example of an Analysis of Observation provided as a template for all analysis papers.** Step 1: Summarize what was observed.

The Center for Medicare Advocacy, is a national nonprofit, nonpartisan law organization that provides education, advocacy and legal assistance to help older people and people with disabilities obtain fair access to Medicare and quality health care.

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