Inheriting (Im)Possibility

Inheriting (Im)Possibility

Marisa Bohlmann Lisa DeMusis Jackie Williams

PROJECT INTRODUCTION

Inheriting (Im)Possibility is Ezekiel Dixon-Roman’s study on quantitative and qualitative data and how they affect an individual’s knowledge of an engagement with the world. His study involves multiple models that focus on specific relationships between different variables. The first model presented to us focused on education, wealth, and ethnicity and how they directly and indirectly affect each other.

The first group of data consists of quantitative elements, such as wealth and education. Quantitative data represents variables that can be measured numerically. Alternatively, the qualitative data consists of parent/child relationship and ethnic background. Qualitative data is described as information that can be observed but not measured. In the case of both qualitative and quantitative variables, each can affect other variables that are within or outside of their own group.

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FIRST TABLES

The first set of data we received was intensive quantitative charts displaying all of the variables being studied. The next set of five figures within the first model was processed into vertical bar graphs by Microsoft Excel. Our challenge was to fully interpret these graphs that were missing key relationships and pieces of data that refer back to the original charts.

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ENGLISH

FOREIGN LANGUAGE

HS Achievement

MATH

SOCIAL SCIENCE LIFE/PHYSICAL SCIENCE

VERBAL

SAT MATH

FLOW CHARTS Aside from the processed graphs, we were given two flow chart models that

Mother/Child Relationship

Mother/Child Relationship

Father/Child Relationship

Father/Child Relationship

break down the direct and indirect relationships between all variables of the study. This includes all assets, maternal/

M

paternal education, SAT year, parent/ child relationships, environmental factors, and ACH and SAT performance. The flow charts along with the previously mentioned graphs all needed to be redesigned in a way that would make them more comprehensible to readers.

F

Father/Child Relationship

Mother/Child Relationship

ENG

Maternal GP EDU

FGN LAN

Paternal GP EDU ACH

MATH

Mother EDU SOC SCI Father EDU LP SCI

Log Permanent Outcome

Log Illiquid Asset

Verbal Log Liquid Asset

SAT Math

Log Debt

Control Including Race

SAT Year

Private/Public School

Physical Environment Risk

Enriching Environment

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FIgure 3: Racial/ethnic differences in wealth holdings

FIGURE 3

Figure 3 is used to express the differences in median wealth holdings between ethnicities. These holdings display net worth, total illiquid assests, total liquid assets, and total debt that are measured in monetary values.

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FIgure 4: Racial/ethnic differences in SAT performance

FIGURE 4

Figure 4 represents the ethnic differences in SAT performances through unconditioned and full structural models. Unconditioned focuses exclusively on the child’s education while the full structural model includes all other factors such as assets.

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FIgure 5: SAT wealth gap SAT effect differences

FIGURE 5

Figure 5 displays how the SAT score differences affect the median wealth holdings of racial groups. The graph shows that when there is a decrease in wealth within the groups, the SAT scores decrease as well.

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FIgure 6: Paternal grandparent education SAT ffects

FIGURE 5

Figure 6 is the focal point of the model. Since figure 6 shows the direct and indirect effects of paternal grandparent education on SAT performance, it embodies the overall concept of the model while the other graphs support it. The direct effect is the effect using only the grandparent’s level of education while the total effect accounts for the grandparent’s education along with all other assets (wealth and race).

PROJECT INTRODUCTION

When beginning the redesign process of the graphs, as a group we decided to edit the information so that it would be easily relatable throughout the model. Changing graph titles and variable wording created relationships between variables within the model. Adding definitions for variables also helps the reader thoroughly understand the concept of each graph individually and the model as a whole.

Towards the end of the process, after understanding and working with the model, we rearranged and renumberd the order of the graphs so that the connections would build on each other. Another design choice we made was to add negative values to graphs that dealt with integers below zero and condensing or separating graphs so that each figure would be displayed with its contextual content. Each change we made to the model was to make the concept more easily interpretable.

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ENGLISH

FOREIGN LANGUAGE

HS Achievement

F

MATH

SOCIAL SCIENCE LIFE/PHYSICAL SCIENCE

VERBAL

FLOW CHART REVISED

SAT MATH

On the given flow charts, there were many overlapping connections that needed to be made

Mother/Child Relationship

Mother/Child Relationship

Father/Child Relationship

Father/Child Relationship

clearer. We began grouping similar variables that had the same connections. In doing so, line clutter significantly decreased and made for a less overwhelming visual. The two flow charts have been kept separate. The first flow chart simply

M

F

shows the variables that make up SAT and ACH complex connections between developed groups.

Father/Child Relationship

Mother/Child Relationship

while the second flow chart displays the more

ENG

Maternal GP EDU

FGN LAN

Paternal GP EDU ACH

MATH

Mother EDU SOC SCI Father EDU LP SCI

Log Permanent Outcome

Log Illiquid Asset

Verbal Log Liquid Asset

SAT Math

Log Debt

Control Including Race

SAT Year

Private/Public School

Physical Environment Risk

Enriching Environment

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Figure 1: Algorithm of influence

Accounts for error margin Error margin states that the subject being measured can never be 100% accurate.

Figure 1: Algorithm of influence

ACH

(Standardized test)

Mother education Father education Maternal and paternal Grandparent education

Public/private school

Control including race

Mother/child relationship

Physical environment risk

Mother

Permanent income

Father/child relationship

Enriching environment

Father

Illiquid asset Liquid asset Debt

SAT

ACH

(Standardized test)

SAT year

English Foreign language Math

SAT Verbal Math

Social science Life/physical science

Structural composition of both ACH and SAT tests.

- ACH affects SAT.

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FIGURE 2 REVISED

Through the design process, we found that Figure 2 would be most successful if the wealth holdings could be represented with positive and negative values. Our solution was to create a vertical graph that contained bars rising above and below the baseline of zero. Total liquid assets, plus total illiquid assets, minus debt results in net worth. In organizing the variables this way the reader can understand the connections between wealth assets. Grouping ethnicities with their own wealth numbers, makes the differences in wealth more obvious.

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Figure 2: Racial Racialdifferences differencesininwealth wealth holdings Figure 2: holdings Total illiquid assests

+

Total liquid assets

-

Total debt

=

Net worth

160 140 120 100 80 60

Value in dollars (1,000’s)

40 20 0 -20 -40 -60 -80

Hispanic

Black

Sample median

White

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FIGURE 3 REVISED

Figure 3 portrays the effect of wealth on SAT scores amongst the different racial groups. When we reexamined the data, we found that the numbers were too close together to emphasize the resulting difference in a bar graph. We decided that the best way to proceed would be to translate the data into percentages. By using percentages and reorganizing the data, we could present the differences in numbers with greater effect.

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Figure Grandparent wealth Figure 33:: Grandparent wealth holdings holdingseffect effectdifferences differenceson onSAT SATscores scores 1050

Bar numerals show increases in scores compared to the baseline of 0 assets.

+108

1025

+96

+92 +85

+84

1000 SAT scores

975

950

929 0 Assets ($0)

Black median

Hispanic median

Sample median

White median

($37,500)

($43,475)

($102,500)

($154,500)

Wealth medians (Total illiquid assets)

$750,000

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FIGURE 4 REVISED

The SAT scores for Figure 4 proved to be difficult to design. Being so close together, many graphs and charts did not emphasize the difference in scores between Black and Hispanic ethnicities within the unconditioned and full structural models. After working with a point and line graph for a period of time, we came to the conclusion that separating the unconditioned and full structural models would be the best option. The bars, from left to right, measure a quantity. Using true SAT scores that start at 400 (the lowest SAT score possible), each bar extends to the achieved SAT score on a scale that caps at 1600.

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Figure 4: SAT performance differences between racial groups

Figure 4: SAT performance differences between racial groups Unconditioned Model 851

Black

879

Hispanic

1062

White 400

1600 SAT scores

Full Structural Model Black Hispanic

935 941 1021

White 400

1600

SAT scores

--Unconditioned includes just just SAT year Unconditioned includes SAT and yearrace andvariables. race variables.

- Full structrual inclueds all of the variables and their connections in figure 1.

- Full structural includes all of the variables and their connections in figure 1.

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FIGURE 5 REVISED

In Figure 5, direct and total effect needed to be displayed in a way that showed the positive correlation between paternal grandparent education and grandchildren’s SAT scores. Instead of using a bar graph that is static and evenly spaced, we chose to design a line graph that showed the rise in education and SAT scores as a steady incline. We also chose to represent the scores in true SAT score form. Doing so helps the reader to fully understand the context of the numbers. Data for Figure 5 is still being tested, and therefore may redetermine the state of the final graph.

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Figure 5: education effects on SAT performance Figure 5:Paternal Paternalgrandparent grandparent education effects on SAT performance 1600

1550

1500

SAT scores

1450

1400

1350

1300 0

4th grade

8th grade

Direct effect Total effect

- Direct effect includes just grandparents’ influence on SAT scores.

- Total effect includes grandparents’ connection to all other variables in figure 1.

HS diploma or equivalent

Bachelor’s degree

Graduate or professional degree

CONCLUSION In order to complete designing model one, we will need to rework and fine tune some aspects of each figure. We would also like to consider creating color versions, to give the publisher variety. As we finish up with the current model, we will begin to discuss continuing onto other models after the course ends. We will be able to take the skills and techniques we have learned from designing the first model and apply them to the upcoming models in a concise, relative way.