To compose this draft, I combined my introduction draft and my methods draft. Add in the analysis

which you turned in for SM and add the draft of the discussion I outlined in class and the references

from JA 4 + any more I used in the correct order.

Compassion Fade

Compassion fade is a type of cognitive bias that makes people inclined towards

behaving more sympathetically towards smaller groups that are suffering rather than to

a large group with the same needs (Västfjäll et al., 2014).The amount donated to charity

has increased from $390 billion in 2016 to $410 billion in 2017 (Butts et al.,

2019).However, there is a feel of sympathy towards one rather than the group who

suffered as a whole. Research shows a decreasing intent to assist with an increase in

the number of individuals in a group. Although, the instances of compassion fade

challenge the ethics of economic valuation at risk; however further research is required

to acquire a better understanding of how and why compassion fade effect occurs.

There is a long grieving among researchers and practitioners as to why more

sympathy for help or assistance is developed for one as compared to a large group with

the same needs. Empirical data suggest that helping diminish as the size of the

individuals in need increase (Fetherstonhaugh, 1997). However, sympathy for helping

others do exist. In qualitative research conducted by Croker et.al (2017), it was found

that individuals are psychology constructed in a way that they believe that supporting

others can be rewarding while selfishness can be a costly affair despite its material

benefit. However, a negative relationship between helping intent and group size was

further supported by research conducted by Butts et al. (2019). The relationship

between the size of the group size and tendency of people to help those in need seems

trues; however, a causal relationship cannot be determined based on the above

research studies.

The study used correlation analysis to determine if there exists any significant

relationship between the group’s size and helping intent. This correlation design relied

on observations being taken over different periods.

Method

For determining the effect of group size on the amount of donation, a total of 33

observations were recorded. The study is qualitative as the objective is to determine if any

significant relationship exists between group size and helping intent. The research design method

includes using an independent two-sample t-test to determine if any significant difference exists

between the two groups (Kenna child and St Jude’s Hospital).The t-test is appropriate in this case

as both dependent and independent variables are available. The independent variable is

qualitative and measured on a nominal scale. The dependent variable i.e. amount of donation is

quantitative and measured on a ratio/interval scale.

The data included collecting information about the amount of donation over several years

for both Kenna child and St Jude’s hospital. The data were obtained using the already available

data on the amount of donation received by the two groups over different years. The amount of

donation received for St Jude’s hospital was obtained from the hospital’s previous financial

sheets. Similarly, data on the amount of donation of Kenna was obtained from the medical bill of

the child. Thus, the procedure involved using existing data to test the hypothesis. The amount of

donations received by Kenna child (n=16) and St Jude’s hospitals (n=17) were recorded

individually. The data were included in the analysis only if had been fully verified from different

sources. All the missing observations were removed from the study. A comparison between the

two groups with missing and observed values is shown in the below table. The donation amount

was recorded under the same environ

Table 1.Descriptive Statistics of Observations with complete data

Variable

N(missing observation)

Mean (SD)

Kenna child

16(0)

7.06(4.48)

St Jude’s Hospital

17(0)

6.41(2.09)

The potential limitation of the dataset includes the presence of less observation which may have

an impact on the result. The probability of Type II error generally increases with low sample size

selection (Hayslett & Murphy, 2017).

References

Butts, M., Lunt, D., Freling, T., & Gabriel, A. (2019). Helping one or helping many? A

theoretical integration and meta-analytic review of the compassion fade

literature. Organizational Behavior and Human Decision Processes, 151, 16-33.

doi: 10.1016/j.obhdp.2018.12.006

Crocker, J., Canevello, A., & Brown, A. (2017). Social Motivation: Costs and Benefits of

Selfishness and Otherishness. Annual Review Of Psychology, 68(1), 299-325.

https://doi.org/10.1146/annurev-psych-010416-044145

Fetherstonhaugh, D. (1997). Insensitivity to the Value of Human Life: A Study of

Psychophysical Numbing. Journal of Risk And Uncertainty, 14.

Västfjäll, D., Slovic, P., Mayorga, M., & Peters, E. (2014). Compassion Fade: Affect and

Charity Are Greatest for a Single Child in Need. Plos ONE, 9(6), e100115.

https://doi.org/10.1371/journal.pone.0100115

Running head: More Hypothesis Testing

1

Module 5b: More Hypothesis Testing

Lisl Dye

Boise State University

PSYC 321- Research Methods

Cindy McCrea

February 15, 2020

1.

2.

3.

4.

5.

Hypothesis 1

State the hypothesis (1pt)

Individuals who have experienced any childhood emotional abuse have high self-harm rates

State the independent/predictor variable (1pt) and if it is scale, ordinal or nominal (1pt)

The independent variable experienced emotional abuse in childhood. The scale of measurement

for this variable is the nominal scale

State the dependent/outcome variable (1pt) and if it is scale, ordinal or nominal (1pt)

The dependent variable is self-harm. The scale of measurement for this variable is the nominal

scale of measurement

Choose and state the correct statistical test (1pt)

The statistical test that will be used is the Chi-square test

Run the correct test using SPSS – prove by copy and pasting your output table (1pt)

Chi-Square Tests

Asymptotic

Significance Exact Sig. (2- Exact Sig. (1Value

df

(2-sided)

sided)

sided)

a

14.182

1

.000

11.098

1

.001

17.662

1

.000

.000

.000

Pearson Chi-Square

Continuity Correctionb

Likelihood Ratio

Fisher’s Exact Test

Linear-by-Linear

13.591

1

.000

Association

N of Valid Cases

24

a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.67.

b. Computed only for a 2×2 table

6. Write 1-2 sentences explaining your results

A chi-square test of independence was used to test if there was any difference between the selfharm rates of individuals who had experienced childhood emotional abuse and those who did not

experience any childhood emotional abuse. The test of independence was significant (X2(1, N=

24) = 14.182, p < .05). The proportion of individuals who had experienced childhood emotional
abuse (81.25 %) and had purposefully hurt themselves was significantly different than that of
individuals who had not experienced childhood emotional abuse (0%). There were no other
significant differences.
More Hypothesis Testing
7. Make the correct figure to display your results(1pt)
Figure 1. Predicted likelihood to purposely hurt your body having experienced emotional abuse
in childhood
2
More Hypothesis Testing
1.
2.
3.
4.
5.
3
Hypothesis 2
State the hypothesis (1pt)
Men are more likely than women to engage in self-harm behaviors.
State the independent/predictor variable (1pt) and if it is scale, ordinal or nominal (1pt)
The independent variable is Biological sex and its scale of measurement is the nominal scale of
measurement
State the dependent/outcome variable (1pt) and if it is scale, ordinal or nominal (1pt)
The dependent variable is self-harm and its scale of measurement is the nominal scale of
measurement
Choose and state the correct statistical test (1pt)
The statistical test that will be used is the correlation test
Run the correct test using SPSS - prove by copy and pasting your output table (1pt)
Chi-Square Tests
Asymptotic
Significance Exact Sig. (2- Exact Sig. (1Value
df
(2-sided)
sided)
sided)
a
.621
1
.431
.142
1
.706
.623
1
.430
.682
.353
Pearson Chi-Square
Continuity Correctionb
Likelihood Ratio
Fisher's Exact Test
Linear-by-Linear
.595
1
.440
Association
N of Valid Cases
24
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.04.
b. Computed only for a 2x2 table
6. Write 1-2 sentences explaining your results. This sentences needs to
A chi-square test of independence was used to test if there was any difference between men and
women. The test of independence was not significant (X2(1, N= 24) = 0.621, p > .05). The

proportion of men (45.5% %) who had purposefully hurt themselves was not significantly

different than the proportion of women (61.5%). There were no other significant differences

7. Make the correct figure to display your results(1pt)

More Hypothesis Testing

Figure 2. Predicted likelihood to purposely hurt your body as a function of gender

4

Running head: SPSS EXPERIMENT DATASET

Module 7: SPSS Experiment Dataset

Lisl Dye

Boise State University

PSYC 321- Research Methods

Cindy McCrea

February 29, 2020

2)

1

SPSS EXPERIMENT DATASET

5.

2

SPSS EXPERIMENT DATASET

3

a.

The average amount of donation for donation of child (M=7.06, SD=4.48) was found to be

higher than the average donation of St. Jude Children’s Hospital (M=6.41, SD=2.09).

b.

The test is the independent sample t-test which was used to check whether the average amount of

donations across two different groups (Kenna and St. Jude) differs significantly or not.

Both variables are independent and not related to one another. Thus, the best test will be

independent sample t-test.

c.

There appears to be no significant difference in the mean amount of donation of Kenna and St.

Jude.

d.

t(20.97)= 0.529,p =0.602

e.

There does not appear to exist significant difference in the scores between Kenna and St. Jude

(t(30)=0.54,p>0.05).

SPSS EXPERIMENT DATASET

1.

Figure 1. Box plot showing a comparison for the scores of Kenna and St. Jude.

4

Running head: STATISTICAL ANALYSIS FOR OUR ECPERIENCE

1

Module 6: Stat for Experiment

Lisl Dye

Boise State University

PSYC 321- Research Methods

Cindy McCrea

February 21, 2020

1. According to the compassion fade effect, we hypothesize that compassion and empathy

decrease as the number of those in need increases

2. The independent variable for this research study is number of those in need because it

determines the level of compassion and empathy that one will depict through their

donation. This variable is a scale variable because it has order and differences between

values can be determined. The variable will be manipulated through the use of two

settings with varying number of people who are in need of help acquired from donations.

The two settings are St. Jude Children’s Hospital, and a homeless child living on the

streets. The univariate test that will be used to come up with descriptive statistics for the

variable is the frequency distribution test.

3. The dependent variable is the amount of money one donates to either St. Jude Children’s

Hospital, or a homeless child living on the streets. This variable is also a scale variable

because it has order and differences between values can be determined. The variable will

be measured by determining the amount of money one is willing to donate to St. Jude

Children’s Hospital, and the amount they are to give a homeless child living on the

streets. The univariate test that will be used to come up with descriptive statistics for the

variable is the frequency distribution test

4. The statistical test that will be used to test the hypothesis that compassion and empathy

decrease as the number of those in need increases is the paired samples t-test because

there are two groups i.e., the amount of money one is willing to donate to St. Jude

Children’s Hospital and the amount of money the same individual will be willing to give

to a homeless child living on the streets, whose means will be compared to each other.

Running head: JOURNAL 3

1

Journal 3- Compassion Fade Effect

Lisl Dye

Boise State University

PSYC 321- Research Methods

Cindy McCrea

February 11, 2020

Article Analysis

Phenomena: Compassion Fade Effect

Compassion fade can be defined as a type of cognitive bias that makes people inclined

towards behaving more sympathetically towards smaller groups that are suffering rather than

to a large group with the same needs. For example, we may feel sympathy for the killing of a

small group of people; however, mass killing is simply given the term “death.”

Article Title

Helping one or helping many? A theoretical integration and meta-analytic review of

the compassion fade literature

Abstract Copy (As written in the Article)

“Researchers and practitioners in the area of charitable giving have long lamented the

tendency to offer greater aid to one person who is suffering rather than to a large group with

the same needs. Demonstrations of such compassion fade are common in the literature,

although different explanations for these findings exist. To reconcile both past theory and

empirical research, we utilized a dual concern framing in conducting a meta-analysis of 41

studies (95 independent samples; 13,259 total sample size) on compassion fade. Results

suggest that victim group size negatively affects both helping intent and helping behavior, as

well as our proposed mediating mechanisms of anticipated positive affect (self-oriented

motivation) and perceived impact (hybrid other-/self-oriented motivation). However,

significant effects were not found for empathetic concern (other-oriented motivation). Results

COMPASSION FADE EFFECT

2

also showed that the indirect effects of victim group size on helping are stronger through

anticipated positive affect and perceived impact than through empathetic concern. Further, as

indicated by supplemental analyses, anticipated positive affect and perceived impact likely

operate as predictors of empathetic concern in a serial mediation process through which

victim groups size affects helping. Finally, we examined calamity scope (number of victims)

and event features (certainty, chronicity, and threat severity) as moderators of the observed

relationships between victim group size and helping. Theoretical implications and directions

for future compassion fade research emanating from these findings are discussed” (Butts,

Lunt, Freling & Gabriel, 2019).

Introduction

The recent data suggests the amount of donations in the US has increased over the

years. For example, the donation amount for the year 2016 was $390 billion, which increased

to $410 billion in 2017 (Giving USA, 2018).However, research studies have shown the

amount of donation is not positively related to the total number of individuals in need. In fact,

helping decreases with the increase in the number of victims. The research question for the

study is;

“Is there any significant relationship between group size and helping?”

The purpose of the study is to test the claim that the amount of donations decreases with the

increase in the group size or the number of people that requires help.

Type of Study

The study is a type of observational as it does not involve any treatment to the

experimental units. The authors collected data from different available works of literature to

determine whether any association exists between the amount of donation and group size.

COMPASSION FADE EFFECT

3

Method Section

The independent variable for the study was the group size, and the dependent variable

for the group size was the amount of donation received. The information about the

independent variable was collected from different electronic databases such as

ABI/INFORM, Google Scholar, ProQuest, Business Source Complete etc. Also, information

about the compassion fade was obtained through different Journal articles by searching for

keywords related to compassion fade. Based on certain criteria, 41 studies were selected

which included a sample size of 13,259.For each of the selected studies, the group size was

then taken as a measurement.

Measuring the Dependent Variable

In order to measure the independent variable “helping (intent and behavior),”studies

considered had to measure the willingness to help or actual help. The helping intent measures

were those that examined an individual’s intent to help. Helping behavior calculation

involved any willingness to help which involves giving donations, goods (e.g., candy), or

giving advice. These helping intent were given scores based on coding schemes.

Main result of the Study

A negative and significant bivariate relationship was found between the helping intent

and behavior and the group size; p=-0.09, 95% CI=-0.06,-0.13 and helping behavior; p=-0.13,

95% CI=-0.08,-0.17

Manipulation of Dependent and Independent Variable

I would also carry out a correlation analysis to check whether any significant

relationship exists between the amount of donations and group size. For the study, I would

consider only the quantitative data of the amount of donation and not any helping behavior

intent. For the independent variable, I would collect data on the number of people who were

victims in a particular event.

COMPASSION FADE EFFECT

Using Reference

In a study by Butts et al. (2018), it was found that the group size of the victim

negatively affects the helping intent and helping behavior.

4

COMPASSION FADE EFFECT

5

References

Butts, M., Lunt, D., Freling, T., & Gabriel, A. (2019). Helping one or helping many? A

theoretical integration and meta-analytic review of the compassion fade

literature. Organizational Behavior and Human Decision Processes, 151, 16-33. doi:

10.1016/j.obhdp.2018.12.006

Giving USA. (2018). Americans gave $410.02 billion to charity in 2017, crossing the $400

billion mark for the first time. Retrieved from: https://givingusa.org/giving-usa2018-americans-gave-410-02-billion-to-charity-in-2017-crossing-the-400billionmark-for-the-first-time/

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