Beall Hofer Schaller Study 1 Month Date Nationwide

Beall Hofer Schaller Study 1

Month	Date	Nationwide.Republican.Support	Nationwide.Democratic.Support	Voter.Intention.Index	Ebola.Search.Volume.Index	ISIS.Search.Volume.Index	DJIA	LexisNexisNewsVolume
9	1				2.86			23
9	2				3.14		17067.56	38
9	3				3.57		17078.28	52
9	4				3.71		17069.58	50
9	5				3.86		17137.36	51
9	6				3.57			31
9	7	43.7	42.3	1.4	4	30		23
9	8				4	31.28571429	17111.42	30
9	9	43.7	42.5	1.2	3.86	28.71428571	17013.87	74
9	10				3.43	25.71428571	17068.71	42
9	11				3.29	34.71428571	17049	32
9	12				3.14	36	16987.51	52
9	13				3.14	36.28571429		29
9	14	43.9	42.9	1	3	40.28571429		29
9	15				3	41	17031.14	40
9	16				3.14	41.57142857	17131.97	89
9	17				3.57	40.42857143	17156.85	128
9	18	43.8	43.2	0.6	3.57	29.28571429	17265.99	140
9	19				3.71	26.71428571	17279.74	71
9	20				3.57	25		57
9	21	43.7	43.3	0.4	3.57	19.85714286		36
9	22				3.57	18.14285714	17172.68	85
9	23				3.43	19.57142857	17055.87	74
9	24				3.14	20.28571429	17210.06	90
9	25	43.1	43.5	-0.4	3.29	20.28571429	16945.8	92
9	26				3.29	20.85714286	17113.15	113
9	27				3.43	21.14285714		69
9	28	43.3	43.6	-0.3	4	20.85714286		57
9	29	43.4	43.6	-0.2	4.29	20.42857143	17071.22	55
9	30	43.5	43.6	-0.1	6.86	17.28571429	17042.9	57
10	1				13.43	15.28571429	16804.71	197
10	2	43.8	43.7	0.1	22.57	13.28571429	16801.05	298
10	3				31.43	11.28571429	17009.69	237
10	4				38.43	10.71428571		160
10	5	44.3	43.6	0.7	43.43	10		125
10	6	44.4	43.6	0.8	48.86	9.428571429	16991.91	118
10	7	44.5	43.5	1	50.71	9.142857143	16719.39	186
10	8				51	9	16994.22	208
10	9	44.6	43.5	1.1	49.43	9.142857143	16695.25	314
10	10				48.86	9	16544.1	250
10	11				46.71	8.714285714		175
10	12	44.7	43.4	1.3	48.14	8.857142857		157
10	13				50.14	8.714285714	16321.07	315
10	14	44.9	43.5	1.4	53.29	8.428571429	16315.19	322
10	15				59	8.285714286	16141.74	353
10	16	45.1	43.5	1.6	65.14	7.857142857	16117.24	512
10	17				68.14	7.857142857	16380.41	583
10	18				70.86	7.428571429		402
10	19				69	6.714285714		269
10	20	45.4	43.5	1.9	65.86	6.285714286	16399.67	384
10	21	45.5	43.5	2	63.43	6	16614.81	359
10	22				54.29	5.428571429	16461.32	327
10	23	45.6	43.5	2.1	43.14	5.428571429	16677.9	273
10	24				38.14	5.142857143	16805.41	388
10	25				33.71	4.857142857		302
10	26	45.7	43.5	2.2	31.14	4.714285714		197
10	27	45.8	43.5	2.3	29.43	4.571428571	16817.94	294
10	28				27.14	4.285714286	17005.75	327
10	29				26.29	4.285714286	16974.31	259
10	30	45.9	43.6	2.3	25.71	4	17195.42	293
10	31				21.14	3.857142857	17390.52	281
11	1	46	43.6	2.4	19.14	3.714285714		218
11	2				17.86	3.714285714		150
11	3				19.71	3.714285714	17366.24	214
11	4				18.43	3.857142857	17383.84	118

Page 1 of 4

Lab 4 ? Infections and elections

ANSWER THE FOLLOWING QUESTIONS & DO THESE PROCEDURES. ALL ANSWERS SHOULD BE INSERTED INTO THIS WORKSHEET. THIS IS ALL THAT WILL BE TURNED IN. YOU WILL NOT TURN IN YOUR SPSS OR EXCEL FILES!

STUDY DESCRIPTION

Choices that individuals make in the voting booth, such as whether to support a more conservative or

liberal candidate, may be affected by a number of factors. Individual voting histories, the policy positions

of the candidates, and current events may each shape voters? preferences. In their research, Beall, Hofer,

and Schaller (2016) sought to examine the role of outbreaks of infectious diseases on voting behavior.

The authors hypothesized that an outbreak of a disease, such as Ebola, may increase support for more

conservative political candidates.

To test this hypothesis, the authors examined the frequency of google searches for ?Ebola? during the

weeks prior to and after the outbreak of Ebola that occurred in 2014. The authors also examined support

for the two major political parties in the United States (the conservative Republicans and the liberal

Democrats) by collecting and aggregating polls of voters into a single score called the voter intention

index.

(Kevin P. McIntyre, 2021. Open Science Lab)

ANALYSIS

Question #1.

? How many total observations are in this sample? ____________________
[LIST NUMBER HERE]

? Does each measured variable have the same number of observations? If not, which variable has the greatest number of observations, and which has the least?
[INSERT YOUR ANSWER HERE]

? This data set has multiple variables. Based on the description above, which two variables will we focus on in this lab?
[INSERT YOUR ANSWER HERE]

? BONUS: why do some variables not have observations for every day?
[INSERT YOUR ANSWER HERE]

Question #2.
We have several dependent variables in this data set. A description for each is listed below. Let?s determine if any of the dependent variables have a ?normal? distribution.

? Month = the month of the observation

? Date = the date of the observation

? Nationwide.Republican.Support = support for republican party based on nationwide poll

? Nationwide.Democratic.Support = support for the democratic party based on nationwide poll

? Voter.Intention.Index = voters? intention leaning more conservative (positive) or more liberal (negative)

? Ebola.Search.Volume.Index = frequency of Ebola searches on google

? ISIS.Search.Volume.Index = frequency of ISIS searches on google

? DJIA = Dow Jones Industrial Average

? LexisNexisNewsVolume = number of Ebola related news articles

Click on Analyze -> Descriptive Statistics -> Descriptives. Put all variables aside from Month and Date in the ?Variables? box. Click on ?Options? and check ?kurtosis? and ?skewness.? Then hit ?continue? and ?ok?. The closer the skewness and kurtosis values are to 0 the more ?normal? they are. If they are each approximately between -2.0 and +2.0, then the distribution is probably ok (rule of thumb).

Are any of the variables not normally distributed? If so which ones? [INSERT YOUR ANSWER HERE]

Question #3.
We will test the relationship between infection scare (Ebola.Search.Volume.Index) and voting intentions (Voter.Intention.Index). We will use bivariate correlation to test this association.

Analyze -> Correlate -> Bivariate. Put both variables in the variables box. Under the ?Correlation coefficients? box check ?Pearson? IF the data are normal and check ?Spearman? IF the data are not normal.

Describe your results in words and a statistical summary that includes the correlation coefficient, degrees of freedom, and p-value (e.g., r(df) = #, p = #).

[INSERT YOUR ANSWER HERE]

Question #4.
Next, we will determine if any of the other variables are related to voter intentions.

Analyze -> Correlate -> Bivariate. Put all variables of interest (NOT Month or Date) in the variables box. Under the ?Correlation coefficients? box check ?Pearson? IF the data are normal and check ?Spearman? IF the data are not normal.

Describe your results in words and a statistical summary that includes the correlation coefficient, degrees of freedom, and p-value (e.g., r(df) = #, p = #). Tip: this can be described in one sentence (e.g., height is related to weight (stats) and age (stats), but not the school attended (stats)).

[INSERT YOUR ANSWER HERE]

Question #5.
The authors were interested in how the breakout of Ebola announced on September 30, 2014 influenced voter intention. Let?s analyze the data both before and after the breakout to see if this event changed voter intentions. We need to filter the data to only include data that occurred before the announcement. We will use a linear regression to answer this question.

Filtering: Data -> Select cases -> in the ?Select? box chose the ?If condition is satisfied? option. Click on the IF box below. Type ?Month = 9? into the box, then press ?continue? and ?ok.? You should now see new message in the Output window indicating a new filter has been applied. You will also see rows crossed out in the Data view window that will not be included in the analysis.

Analysis: Analyze -> Regression -> Linear. Put
Voter.Intention.Index
in the ?Dependent? box and Date in the ?Independent? box. Click ?ok.?

Describe your results in words and a statistical summary that includes the standardized beta, degrees of freedom, t-statistic, and p-value (e.g., ? = #, t(df) = #, p = #).

[INSERT YOUR ANSWER HERE]

Question #6.
Next, let?s analyze the data after the breakout to see if this event changed voter intentions. We need to filter the data to only include data that occurred after the announcement.

Filtering: Data -> Select cases -> in the ?Select? box chose the ?If condition is satisfied? option. Click on the IF box below. Type ?Month = 10? into the box, then press ?continue? and ?ok.? You should now see new message in the Output window indicating a new filter has been applied. You will also see rows crossed out in the Data view window that will not be included in the analysis.

Analysis: Analyze -> Regression -> Linear. Put
Voter.Intention.Index
in the ?Dependent? box and Date in the ?Independent? box. Click ?ok.?

Describe your results in words and a statistical summary that includes the standardized beta, degrees of freedom, t-statistic, and p-value (e.g., ? = #, t(df) = #, p = #).

[INSERT YOUR ANSWER HERE]

Question #7.
Use SPSS to create a scatter plot of the Voter.Intention.Index before and after the breakout. Make sure to include lines of best fit.

Graphs -> Legacy Dialogues -> Scatter/Dot? -> Simple Scatter -> Put
Voter.Intention.Index
in the Y-axis variable and Date in the X-axis variable and hit ?ok.? To add the line of best fit, right click on the image -> Edit content -> In separate window -> click the ?Add fit line at total? button and uncheck the ?Attach label to line.? Hit ?Apply.?

[INSERT BOTH YOUR GRAPHS HERE]

Beall Hofer Schaller Study 1 Month Date Nationwide

Beall Hofer Schaller Study 1

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