Making Graphs with Excel - Bowling Green State …Making Graphs with Excel Summer 2014 Workshop...

CENTER FOR FAMILY & DEMOGRAPHIC RESEARCH

Making Graphs with Excel Summer 2014 Workshop Series

CHARTS?

WHY-

Picture Superiority Effect Information is better remembered in tests of recall and item recognition when presented as pictures rather than words

Fruit <

SOCIOLOGISTS?

Why is it so difficult for-

How do you process? • Analytical

• Logical

• Precise

• Repetitive

• Organized

• Details

• Scientific

• Detached

• Literal

• Sequential

• Creative

• Imaginative

• General

• Intuitive

• Conceptual

• Big picture

• Heuristic

• Empathetic

• Figurative

• Irregular

I Propose we Marry the Two The pun is intended!

Organization of Presentation

• Structure of an Excel Chart

• Different Types of Excel Charts

• Basic Principles of Chart Design

• Graphing Interaction Effects

• Creating a Chart with a Double Axis

THE STRUCTURE OF AN EXCEL CHART?

What makes up-

Let’s Dissect…

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$0 $5 $10 $15 $20 $25 $30 $35 $40

Rate

s per

1,0

00 a

t Ris

k Po

pula

tion

HMI Spending Per Population at Risk of Marriage or Divorce

Predicted Marriage & Divorce Rates Upper Bounds (UB) & Lower Bounds (LB)

UB Mar Rt LB UB Div Rt LB

Chart Title

Legend

Axis Titles

Axis

Gridlines

Data Labels

Sources: U.S. Census Bureau, American Community Survey, 2008-2011; HMI spending data– Hawkins et al., 2013. Source

THE DIFFERENT TYPES OF CHARTS?

What are-

Histograms A vertical bar chart that depicts the distribution of a set of data

Histograms, example

Pie Charts Generally used to show percentage or proportional data classified into nominal or ordinal categories

Pie Charts, examples Simple Pie Pie-of-Pie

Childcare 15%

School/ Training

20%

Layoff 19%

Other 46%

Top Reasons for Fathers Leaving the Workforce in 2008

Single 18%

Cohabiting 25%

Married 57% Unmarried

43%

Percent of births by informal marital status of mother, 2005-

2010

Source: Survey of Income and Program Participation, 2008 March Supplement

Source: NSFG 2006-2010

Pie Charts, examples Simple Pie Doughnut

Didn't enroll 40%

No degree 33%

Associate's degree

6%

Bachelor's degree

21%

College experiences of young adults (by age 25)

Did not finish 44%

Associate's degree

7%

Bachelor's degree

49%

Percent of young adults who enroll in a 4-year program by

degree earned by age 25

Source: National Longitudinal Survey of Youth 1997, Rounds 1-13: 1997-2009 weighted. U.S. Department of Labor, Bureau of Labor Statistics, NCFMR analyses of valid cases.

Bar Chart, example

19%

20%

12%

8%

2%

33%

13%

7%

9%

11%

None

GED

H.S.

Assoc. Deg.

B.A.+

Blacks

Hispanics

Whites

Men

Women

Pre-

unio

n Fi

rst B

irth

Prevalence of Pre-union First Birth across Demographic Characteristics

Yes 33%

No 67%

Blacks

Yes 13%

No 87%

Hispanics

Yes 7%

No 93%

Prevalence of Pre-union First Birth by Race/Ethnicity: Whites

Source: National Longitudinal Survey of Youth 1997 (NLSY97), Rounds 1-13: 1997-2009 (weighted). U.S. Department of Labor, Bureau of Labor Statistics, NCFMR analyses of valid cases.

Column & Bar Charts Useful for showing data changes over a period of time or for illustrating comparisons among items

Column Charts, examples Simple

74% 80%

49%

62%

70%

All fathers White Black NB Hispanic FB Hispanic

Fathers Living with All of Their Children Race, Ethnicity & Nativity

Side-by-Side

11%

25% 20% 22%

25%

44% 38% 37%

0%

10%

20%

30%

40%

50%

60%

70%

80%

White Black Asian Hispanic

Percentage of Same-Sex Couple Households with Minor Children by Sex of Couple and Race/Ethnicity of Household

Head

Male-Male Female-Female

Source: NSFG 2006-2010 Source: U.S. Census Bureau, American Community Survey, 1-Year Estimates, 2012

Column Charts, examples

-25%

-18%

-12%

-8%

16%

Percent Change in Share of Aggregate Income from 1970-2009

Lowest fifth Second fifth Third fifth Fourth fifth Highest fifth

Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplements

Column Charts, examples

55% 65%

31%

52% 53%

76%

6% 11%

1% 6%

11% 19%

Married CoupleHouseholds

Different SexCouples

MaleSame SexCouples

FemaleSame SexCouples

FatherOnly

MotherOnly

Cohabiting Households Single Parent Households

Public Assistance Participation among U.S. Children in Poverty by Family Structure, 2010

SNAP TANF

Source: U.S. Census Bureau, American Community Survey, 1-Year Estimates, 2010

Column Charts, examples

15% 16% 17% 18%

6% 11%

18% 24% 21%

27% 35%

42%

0%

20%

40%

60%

80%

100%

1980-1984 1990-1994 1997-2001 2005-2009

Changes in the Shares of Births to Single and Cohabiting Mothers Under Age 40

Single Cohabiting Total Non-Marital

Sources: 1980-1984 data, Bumpass & Lu (2000) using NSFH, 1987/1988; 1990-1994 & 1997-2001 data, Kennedy & Bumpass (2008) using NSFG 1995 & NSFG 2002; 2005-2009, NCFMR analyses using NSFG 2006-2010.

Line Charts Ideal for showing trends over time

Line Charts, examples

4.4

30.9

2.4

20.4 22.1

57.1

0

10

20

30

40

50

60

70

1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09

Share of Married Mothers Experiencing a Premarital Birth, by Race and Marriage Cohort

Overall

White

Black

Source: The Integrated Fertility Survey Series (IFSS) is a project of the Population Studies Center and the Inter-university Consortium for Political and Social Research at the University of Michigan, with funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD, grant 5R01 HD053533; Pamela J. Smock, PI).

Line Charts, examples

67%

42%

51%

51%

32%

47%

24%

10%

19%

17%

7% 15%

0%

20%

40%

60%

80%

100%

1940 1950 1960 1970 1980 1990 2000 2010

Young Adults Living in a Parent's Household and Economic Recession Years by Sex and Ages, 1940-2010

Recessions

18-24 Men

18-24 Women

25-34 Men

25-34 Women

2 per. Mov. Avg.(18-24 Women )2 per. Mov. Avg.(25-34 Men)

gf

Source: U.S. Census Bureau, Decennial Census, 1940-2000 (IPUMS); U.S. Census Bureau, American Community Survey, 1-year estimates 2010 (IPUMS)

Line Charts, examples

46.1

33.9

18.4 18.4

$5

$122

$0

$20

$40

$60

$80

$100

$120

$140

$160

0

5

10

15

20

25

30

35

40

45

50

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Dolla

r Am

ount

s in

Mill

iom

s

Rate

s per

1,0

00 a

t Ris

k

Annual HMI Spending and Marriage & Divorce Rates, 2000 - 2010 Marriage Rate Divorce Rate HMI Spending

Sources: CDC/NCHS, National Vital Statistics System, 2000; Glass & Levchak, 2010, NCFMR County-Level Marriage & Divorce Data, 2000; U.S. Census Bureau, Decennial Census, 2000; U.S. Census Bureau, American Community Survey, 1-Year Estimates, 2008 – 2010; HMI Spending data – Hawkins et al., 2013.

Line Charts, examples

0

10

20

30

40

50

60

70

80

90

100

20

21

22

23

24

25

26

27

28

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Perc

ent

Age

in y

ears

Crossover in median age at first marriage and first birth: Rising proportion of births to unmarried women, 1980-Present

Proportion of births to unmarried womenWomen's median age at first marriageWomen's median age at first birth

Sources: 1. U.S. Census Bureau, Current Population Survey, March and Annual Social and Economic Supplements, 2012 and earlier. 2. Centers for Disease Control and Prevention. National Center for Health Statistics. Vital Stats. http://www.cdc.gov/nchs/vitalstats.htm. [March 2013]. 3. Martin JA, Hamilton BE, Ventura SJ, et al. Births: Final data for 2009. National vital statistics reports; vol 60 no 1. Hyattsville, MD: National Center for Health Statistics.

2011. 4. Hamilton BE, Martin JA, Ventura SJ. Births: Preliminary data for 2010. National vital statistics reports web release; vol 60 no 2. Hyattsville, MD: National Center for

Health Statistics. 2011.

Scatter Plots Commonly used to show the relationship between two variables e.g. correlation

Scatter Plots, example

AL

AZ

AR CA

CO

CT

DE

DC

FL GA HI

ID IN

IA

KY

LA

ME

MD

MA MI

MN

MS

MO

NE NH

NJ

NM

NY NC

ND

OH OK

PA

RI

SC TN

TX

UT

VA

WV

WI

WY

230

240

250

260

270

280

290

0 5 10 15 20 25 30

1992

Sta

te 8

th-g

rade

NAE

P M

ath

Scor

e

% Students watching TV 6 hrs+ per day

State Math Scores and Students' TV Viewing Habits

Source: National Center for Educational Statistics, 1994

Area Charts Show percentage or proportional data classified into nominal or ordinal categories over time

Area Charts, example

0%

20%

40%

60%

80%

100%

1970 1980 1990 2000 2008 2012

Marital Status of U.S. Population Aged 15 and Older, 1970-2012

Married

Never Married

Divorced

Widowed

Source: 1970-2000 data, U.S. Census Bureau, Current Population Survey, March and Annual Social and Economic Supplements. 2008 and 2012 data, U.S. Census Bureau, American Community Survey, (IPUMS)

BASIC PRINCIPLES OF CHART DESIGN?

What are some-

1. Simplify • Minimize ink-to-data ratio • Remove unneeded chart

elements – Gridlines – Chart borders – Axis titles – Legends – Markers & data labels – Decimal points (in axis &

data labels) – Trend lines – NO 3D charts!!!!!!!!!!!!!!!!!!!

• Sort data in a meaningful way

Example of a 3D Chart:

Allfathers

White Black NBHispanic

FBHispanic

74% 80%

49%

62% 70%

Fathers Living with All of Their Children Race, Ethnicity & Nativity

2. Color vs. Black & White

• When in doubt black & white

• Color can help tell a story

• Color = branding (e.g. CFDR, NCFMR, BGSU) – Use a cohesive and consistent color palette

– Be mindful of how audience will view • Excel vs. Word vs. PDF

• Color vs. B&W print copy

3. Do NOT Use Distorted Charts

• Do NOT misrepresent your data!

• Use appropriate and consistent axis and scales

4. Present Related Charts Simultaneously • One-after-another or side-by-side if

possible

• Emphasizes importance of appropriate axis and scales

5. Know Your Audience

• Academics vs. lay folks

• Undergraduate students vs. graduate students

• Graduate students vs. professors

• PAA presentation vs. job talk

6. TMC = TMI

• Too many charts (TMC) is as bad as too much information (TMI) Do NOT overload your audience!

Let’s apply some principles: Which is easier to understand?

35.97

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$0.00 $10.00 $20.00 $30.00 $40.00Rate

s per

1,0

00 a

t Ris

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pula

tion

HMI Spending Per Population at Risk of Marriage or Divorce

Predicted Marriage & Divorce Rates Upper Bounds (UB) & Lower Bounds (LB)

UB Mar Rt LB

UB Div Rt LB

39 36

19

24***

0

5

10

15

20

25

30

35

40

45

$0 $8 $16 $24 $32

Predicted Marriage & Divorce Rates Given State-level HMI Spending

Mar Rt Div Rt

Sources: U.S. Census Bureau, American Community Survey, 2008-2011; HMI spending data– Hawkins et al., 2013.

7. Do you need a chart?

$117 mill

ion The amount annual

HMI spending in the U.S. increased from 2000 – 2010

Sources: U.S. Census Bureau, American Community Survey, 2008-2011; HMI spending data– Hawkins et al., 2013.

CHART INTERACTION EFFECTS?

How do I-

Logistic Regression Predicting Ever Marrying • An interaction between a categorical and

continuous predictor (DeMaris 2004, p 143):

E(Y) = β0 + δ1Black + β1Parity + ϒ1Black*Parity – The subpop consists of only White and Black women

– Black is a dummy variable

– Parity indicates number of live births, range 0-15

– Analyses is weighted

Logistic Regression Predicting Ever Marrying, cont. • Stata Output for Full Model: . svy, subpop(blkwht): logistic evermar black PARITY PARITYblk, coef (running logistic on estimation sample) Survey: Logistic regression Number of strata = 56 Number of obs = 12279 Number of PSUs = 152 Population size = 61754741 Subpop. no. of obs = 8568 Subpop. size = 45835139 Design df = 96 F( 3, 94) = 186.25 Prob > F = 0.0000 ------------------------------------------------------------------------------ | Linearized evermar | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- black | -.4698438 .1172022 -4.01 0.000 -.7024885 -.2371992 PARITY | 1.458909 .0707637 20.62 0.000 1.318444 1.599374 PARITYblk | -.9253343 .0978554 -9.46 0.000 -1.119576 -.7310928 _cons | -.8652098 .0616793 -14.03 0.000 -.9876423 -.7427772 ------------------------------------------------------------------------------

Logistic Regression Predicting Ever Marrying, cont. • Table of Results

Logistic Regression Predicting Ever Marrying Model 1

(Zero-Order) Model 2 Model 3

(Full)

Coef. SE Coef. SE Coef. SE Black -0.854 0.325 *** -1.589 0.113 *** -0.470 0.117 *** Parity 1.040 0.054 *** 1.150 0.053 *** 1.459 0.071 *** Black X Parity -0.925 0.098 *** Constant -0.679 0.06 *** -0.865 0.062 ***

• Equation for Full Model E(Y) = β0 + δ1Black + β1Parity + ϒ1Black*Parity

• Equation for Black Women E(Y) = β0 + δ1 + β1Parity + ϒ1*Parity

• Equation for White Women E(Y) = β0 + β1Parity

• Now, Plug and Play in Excel!

Logistic Regression Predicting Ever Marrying, cont.

Logistic Regression Predicting Ever Marrying, cont. Unformatted Formatted

-5.000

0.000

5.000

10.000

15.000

20.000

25.000

1 2 3 4 5 6 7 8 9 10111213141516

Black Women

White Women

-10

-5

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Parity

Effect of Parity on Ever Marrying for Black and White Women

Black Women White Women