xuebaunion@vip.163.com

3551 Trousdale Rkwy, University Park, Los Angeles, CA

留学生论文指导和课程辅导

无忧GPA：https://www.essaygpa.com

工作时间：全年无休-早上8点到凌晨3点

扫码添加客服微信

扫描添加客服微信

ATHK1001-英语代写

时间：2021-04-22

ATHK1001 ANALYTIC THINKING: ASSIGNMENT 1, 2021

Due date: 11:59pm Friday, April 23rd (Week 7). Late penalty of 5% per calendar day applies.

Online submission: All submissions are to be made online on the ATHK1001 Canvas website.

Submissions will be checked for plagiarism.

Incorrect submissions: If you discover before the closing date that the file you submitted on

Turnitin was incorrect, and let us know, you may be given the option to resubmit a corrected

version which will incur a 50% penalty or the relevant lateness penalty, whichever is greater.

Word length: 750 words across all questions (excluding references in Question 12). A penalty of

10% will apply to papers that exceed this limit by more than 10%, a 20% penalty if you exceed

20% of the limit, and 30% if you exceed the limit by 30%.

Total marks: 60 (17.5% of total grade for class)

Background and Aims

Sometimes people do not have access to the data they need, so they have to make informed

estimates. However, there can be biases in people’s estimates. Tversky and Kahneman (1974) identified

one such bias they called “anchoring” (amongst other biases in decision making). They showed that when

people tried to estimate the numerical answers to a question they do not know, they can be influenced by

a number they have just seen. For example, if people estimate the proportion of African countries in the

United Nations, then they give higher estimate if they first had to say if the answer was higher or lower

than 65 rather than 10. This was true even though they were told that the number was randomly

generated. The first number appeared to anchor their estimate and drag the estimate towards the anchor.

In the experiment you did during Week 2 tutorials explored the anchoring bias to estimation.

Although participants in Tversky and Kahneman’s (1974) study was told that the number was

randomly generated, perhaps they did not believe the experimenter and instead thought the number they

were given was actually related to the true answer, so rather than the number they were given being an

anchor it may have been regarded as useful information. To test this possibility, in our experiment for one

of our tasks we had participants generate a number based on their own phone number, so they knew it

was unrelated to the question. Participants then said whether or not the Attila the Hun was defeated at the

Battle of Chalons on a date before or after the year equal to their phone number (plus 100). They then

estimated the true answer. If a knowingly random number can anchor an estimate, then the higher a

participant’s phone number the higher the estimate should be.

The anchoring bias seems to imply that any random number we are exposed to could influence us

whenever we have to try to estimate a numerical answer. So an important question is how wide is the

scope of the anchoring effect? We tested this using a task based on that used by Strack and Mussweiler

(1997). We gave participants a set of pairs of questions. The first question in the pair asked them whether

the answer to the question was higher or lower than a given answer (i.e., the anchor), the second question

in the pair asked them to either give a numerical answer to the same question or to a different question.

The anchor was either substantially higher than the true answer or substantially lower than it. If to be

influential an anchor must be directly related to the number being estimated, then when the anchor is high

estimates should be higher when the second question in the pair is the same than when it is different, and

when the anchor is low estimates should be lower when the second question in the pair is the same than

when it is different.

In the class experiment we examined hypotheses about both of these tasks.

Method

Participants

A total of 294 students from analytic thinking course (ATHK1001) participated as part of a class

experiment. Additional students participated but either did not complete the experiment or did not consent

2

to having their data analysed. Of these 177 were female, 117 were male and they had a mean age 19.5

years).

Materials

For the Phone task participants answered three questions:

“Think of the last 3 digits of your telephone number, now add 100 to its value, then write the

answer in the box below” (adding 100 was a way to make participants focus on the number).

“Looking at the number you created above did the following event occur before or after this date

AD: Attila the Hun was defeated at the Battle of Chalons”

“Provide your estimate of what year AD the event occurred” (true answer is 451)

An example of the questions asked in the Paired task was the following: “Please indicate whether

you think the true answer for the quantity is higher or lower than the random number in blue. The

population of New York City in 2019 was [anchor number in blue] ”. They then answered on the

following two questions:

“What is the population of New York City in 2019?” [Same question condition]

“What was the number of babies born in the USA in 2018?” [Different question condition]

The eight other questions in the Same question condition were:

What was the total livestock population of ducks in France in 2008?

In 2014, what was the Gross National Debt of the Republic of Congo (US$)?

What is the length of time an American person spends eating dinners per year in minutes?

What was the weight of King Henry 8th of England (in pounds)?

What is the total area (square kilometres) of Chile?

What is the height of the mountain K2 (in feet)?

What is the annual consumption of dairy products per person (in pounds)?

What was the total worldwide gross of the film 'How to Train Your Dragon II' (US$)?

The eight other questions in the Different question condition were:

In 2012, what was the annual consumption of electricity (megawatt hours) in Kazakhstan?

What was the lowest daily value of shares traded on New York Stock Exchange in Year 2003

(US$)?

What is the average time spent by a person per year on social networking online (minutes)?

How much does a typical refrigerator weight (in pounds)?

What is the population of Suriname?

What is the average distance a car travels per year (in km)?

What is the average annual water consumption per household (in Liters)?

What were the global iPhone sales during 2018 (units)?

The high anchors were randomly generated but always had a magnitude one greater than the true

answer. The low anchors were randomly generated always with a magnitude one less than the true

answer.

Design and Procedure

The experiment had two independent variables: Question condition, either same or different;

Anchor condition, either high or low. These independent variables were varied between participants, each

participant received all their paired questions in either the same or different condition, and they received

either all the high anchor high or all the low anchors.

During tutorials for the class Analytic Thinking at the University of Sydney participants

completed the experiment individually on computers in class or online. They then completed the

experiment in a set of steps. First participants answered some demographic questions, then they did the

Paired task, and then the Phone task. After completing the experiment participants indicated whether or

not they consented to having their data included in the data set.

3

Hypotheses

We proposed four hypotheses related to anchoring effects. First, we will test whether there is an

anchoring effect when participants can know for certain that the anchor is random and completely

unrelated to the question being asked. We predict that participants with relatively high phone numbers

will produce higher estimates than those with relatively low phone number.

Hypothesis 1: Participants with phone numbers above the median produced higher Battle of

Chalons estimates than those with phone number below the median.

Another way to test whether people’s phone numbers influence their estimates is to calculate the

correlation between them.

Hypothesis 2: Participants’ phone numbers and their estimates for the Battle of Chalons will have a

positive correlation.

For the Paired task, if the anchoring effect depends on the similarity of the question introducing the

anchor and the question asking for the estimate, then would expect a bigger impact of the anchor for

participants in the same question condition than the different question condition.

Hypothesis 3: In the Paired task, participants in the High anchor condition will produce higher

aggregated estimates if they were also in the same question condition than if they were in the

different question condition.

For the Paired task, we would expect a result consist with the result of Hypothesis 3 for participants in the

low anchor condition.

Hypothesis 4: In the Paired task, participants in the Low anchor condition will produce lower

aggregated estimates if they were also in the same question condition than if they were in the

different question condition.

Results

The data set for our class can be found on the Canvas site for ATHK1001 under “Assignment 1”.

This assignment description can be found there as well as an Excel file called “Assignment1_dataset.xls”.

This Excel file contains all the data for the assignment and has 294 data lines, one for each participant.

Each participant has values for 6 variables, and the values of each variable are in a single column of the

file.

The first variable is an id number. There are two variables related to the Phone task, the variable

“phone_number” which is the number participants entered for their phone number (plus 100), and the

variable “Chalons_estimate” which shows participants’ estimates for the year of the Battle of Chalons.

There are only 261 participants with values for these variables because this task came at the end of the

experiment, so some participants ran out of time. Excel formula can sometimes act nonintuitively when

presented with blank cells, so be aware of this.

The need to make this data public for the assignment created a privacy issue due to the

“phone_number” variable because it potentially includes part of each participant’s phone number. Some

of the values for this variable are unique, so if a student has friends in the class who know their phone

number, then these friends may be able to identify the student’s data. This could not be done with

complete confidence because only about one-third of the class has data for this variable and participants

could have deliberately or accidently entered incorrect numbers. However, participants were told that

their data would be kept private so we decided we needed to make identifying a participant much more

difficult. We did this by randomly adding a number between 0 and 100 to each value for the

phone_number variable. The data file you have access to contains this modified data. We checked all the

analysis impacted by this variable that you are requested to carry out and found that there was no material

impact on the analysis, so you can ignore this change to the data when answering the questions below.

There are three variables related to the Paired task. “Anchor_condition” says whether the

participant was given high or low anchors, “Question_condition” says whether the participant received

4

the same question or a different question to that used to present the anchor. “Aggregated_estimates”

which represent how close overall a participant’s estimate was to the mean estimate, with negative

numbers indicating they tended to be below the mean of everyone’s estimates and positive numbers

indicating they tended to be above.

The calculation of the Aggregated estimates variable is somewhat complicated and to interpret it

you do not need to understand how it was calculated, just what it represents. If you are interested though,

this is what we did. We could not just take the average of participants estimates because the questions had

very different answers meaning they were on different scales. To put them onto to the same scale we

converted them into standard scores (standard scores are calculated by first calculating the sample’s mean

and standard deviation for a variable, then calculating the difference between each participant’s estimate

for that variable and the variable’s mean, and finally dividing this difference by the sample’s standard

deviation). We then calculated each participant’s mean standard score across their estimates, which is

what Aggregated_estimates are.

WHAT YOU WILL WRITE

Your task is to analyse the data in order to test the four hypotheses proposed above. You will do this by

addressing each of the following twelve questions. Answer all questions with complete sentences, not

with just numbers, notes or tables. Do not include the text of the questions in your assignment (this

will trigger a plagiarism warning), but you should include the number of the question being addressed.

1) For the Phone task the median phone number (modified) was 611. For participants with phone

numbers below or equal to the median calculate and state the mean and standard deviation of their

estimate for the Battle of Chalons. Do the same for participants with phone numbers above the median. (4

marks)

2) Based on the means you calculated in Question 1 use a t-test to test Hypothesis 1, that participants with

phone numbers above the median produced higher Battle of Chalons estimates than those with phone

number below the median. Report the p-level for the t-test and state clearly whether or not Hypothesis 1

was supported, and why. (Note that we will be discussing hypothesis testing in lectures in Week 4 and

practicing using Excel to test hypotheses in tutorials in Week 5. So you may need to wait to answer this

question until we have covered the relevant material in class.)

(4 marks)

3) Test Hypothesis 2 by calculating the correlation between participants’ phone numbers and their

estimates for the Battle of Chalons. To test the statistical significance of the correlation you can use the

fact that for a sample n=261 any correlation with greater magnitude than .122 is statistically significant at

the p<.05 level. State whether Hypothesis 2 was supported, and why. (4 marks)

4) Present a scatter graph to show the relationship between phone numbers and their estimates for the

Battle of Chalons. Based on your analysis in Questions 1-4, how strong does the influence of the anchor

on the estimated answers appear to be? Is testing Hypothesis 1 or testing Hypothesis 2 better convey the

nature of this influence? (6 marks)

5) For the Paired task calculate four means and standard deviations for aggregated estimates: for

participants in the high anchor condition and the same question condition, for participants in the high

anchor condition and the different question condition, for participants in the low anchor condition and the

same question condition, for participants in the low anchor condition and the different question condition.

(8 marks)

6) Based on the means calculated in Question 5, test Hypothesis 3 that participants in the High anchor

condition will produce higher aggregated estimates if they were also in the same question condition than

5

if they were in the different question condition. Report the p-level for the t-test and state clearly whether

or not Hypothesis 3 was supported, and why. (4 marks)

7) Based on the means calculated in Question 5, test Hypothesis 4 that participants in the Low anchor

condition will produce lower aggregated estimates if they were also in the same question condition than if

they were in the different question condition. Report the p-level for the t-test and state clearly whether or

not Hypothesis 4 was supported, and why. (4 marks)

8) Are your conclusions for Hypothesis 3 and Hypothesis 4 consistent? Explain your answer and any

implications (3 marks)

9) Identify three different issues with the way we collected data which could limit our ability to draw

conclusions from it. These issues could relate to one or more of the hypotheses. Clearly differentiate the

three issues as “Issue 1”, “Issue 2” and “Issue 3” and explain how each of these issues relates to a data

collection consideration raised in ATHK1001 lectures. For each issue suggest a way it might be resolved

in future research on this topic or if it cannot be resolved then explain why. (12 marks)

10) Summarize what YOUR data analysis tells us about anchoring effects. What do our results add to

Tversky and Kahneman’s (1974) discussion of anchoring, particularly on page 1128 of their article?

Explain your answers with reference to the results of your testing of the hypotheses and possibly the

issues you raised in Question 9. (6 marks)

11) What do you think is the single most important finding from our experiment? Why? (3 marks)

12) Include a reference section which lists the full reference for any paper you have cited when

addressing these questions. You must cite Tversky and Kahneman (1974) in Question 10, and include

citations where ever appropriate. You should use APA style for citations and references, but we will

accept other standard journal article referencing formats. (2 marks)

References

Judgement under uncertainty. Author(s): Amos Tversky and Daniel Kahneman. Science, volume 185,

pages 1124-1131

THIS IS NOT IN A STANDARD REFERENCING FORMAT, YOU WILL NEED TO REFORMAT

THIS FOR Q.12.

Strack, F., & Mussweiler. T. (1997). Explaining the Enigmatic Anchoring Effect: Mechanisms of

Selective Accessibility. Journal of Personality and Social Psychology, 73, 437-446.

[NOTE THAT YOU DO NOT HAVE TO READ THIS PAPER, BUT YOU CAN IF YOU WISH TO]

Note that you do not have to use other sources for answering these questions, but if you do then you must

correctly cite and reference these sources.

Formatting Recommendations

Our preferences

Use the font “Times New Roman”, 12-point size, and double-space all the lines.

Indent the beginning of each paragraph using one tab space.

Use APA referencing style

Due date: 11:59pm Friday, April 23rd (Week 7). Late penalty of 5% per calendar day applies.

Online submission: All submissions are to be made online on the ATHK1001 Canvas website.

Submissions will be checked for plagiarism.

Incorrect submissions: If you discover before the closing date that the file you submitted on

Turnitin was incorrect, and let us know, you may be given the option to resubmit a corrected

version which will incur a 50% penalty or the relevant lateness penalty, whichever is greater.

Word length: 750 words across all questions (excluding references in Question 12). A penalty of

10% will apply to papers that exceed this limit by more than 10%, a 20% penalty if you exceed

20% of the limit, and 30% if you exceed the limit by 30%.

Total marks: 60 (17.5% of total grade for class)

Background and Aims

Sometimes people do not have access to the data they need, so they have to make informed

estimates. However, there can be biases in people’s estimates. Tversky and Kahneman (1974) identified

one such bias they called “anchoring” (amongst other biases in decision making). They showed that when

people tried to estimate the numerical answers to a question they do not know, they can be influenced by

a number they have just seen. For example, if people estimate the proportion of African countries in the

United Nations, then they give higher estimate if they first had to say if the answer was higher or lower

than 65 rather than 10. This was true even though they were told that the number was randomly

generated. The first number appeared to anchor their estimate and drag the estimate towards the anchor.

In the experiment you did during Week 2 tutorials explored the anchoring bias to estimation.

Although participants in Tversky and Kahneman’s (1974) study was told that the number was

randomly generated, perhaps they did not believe the experimenter and instead thought the number they

were given was actually related to the true answer, so rather than the number they were given being an

anchor it may have been regarded as useful information. To test this possibility, in our experiment for one

of our tasks we had participants generate a number based on their own phone number, so they knew it

was unrelated to the question. Participants then said whether or not the Attila the Hun was defeated at the

Battle of Chalons on a date before or after the year equal to their phone number (plus 100). They then

estimated the true answer. If a knowingly random number can anchor an estimate, then the higher a

participant’s phone number the higher the estimate should be.

The anchoring bias seems to imply that any random number we are exposed to could influence us

whenever we have to try to estimate a numerical answer. So an important question is how wide is the

scope of the anchoring effect? We tested this using a task based on that used by Strack and Mussweiler

(1997). We gave participants a set of pairs of questions. The first question in the pair asked them whether

the answer to the question was higher or lower than a given answer (i.e., the anchor), the second question

in the pair asked them to either give a numerical answer to the same question or to a different question.

The anchor was either substantially higher than the true answer or substantially lower than it. If to be

influential an anchor must be directly related to the number being estimated, then when the anchor is high

estimates should be higher when the second question in the pair is the same than when it is different, and

when the anchor is low estimates should be lower when the second question in the pair is the same than

when it is different.

In the class experiment we examined hypotheses about both of these tasks.

Method

Participants

A total of 294 students from analytic thinking course (ATHK1001) participated as part of a class

experiment. Additional students participated but either did not complete the experiment or did not consent

2

to having their data analysed. Of these 177 were female, 117 were male and they had a mean age 19.5

years).

Materials

For the Phone task participants answered three questions:

“Think of the last 3 digits of your telephone number, now add 100 to its value, then write the

answer in the box below” (adding 100 was a way to make participants focus on the number).

“Looking at the number you created above did the following event occur before or after this date

AD: Attila the Hun was defeated at the Battle of Chalons”

“Provide your estimate of what year AD the event occurred” (true answer is 451)

An example of the questions asked in the Paired task was the following: “Please indicate whether

you think the true answer for the quantity is higher or lower than the random number in blue. The

population of New York City in 2019 was [anchor number in blue] ”. They then answered on the

following two questions:

“What is the population of New York City in 2019?” [Same question condition]

“What was the number of babies born in the USA in 2018?” [Different question condition]

The eight other questions in the Same question condition were:

What was the total livestock population of ducks in France in 2008?

In 2014, what was the Gross National Debt of the Republic of Congo (US$)?

What is the length of time an American person spends eating dinners per year in minutes?

What was the weight of King Henry 8th of England (in pounds)?

What is the total area (square kilometres) of Chile?

What is the height of the mountain K2 (in feet)?

What is the annual consumption of dairy products per person (in pounds)?

What was the total worldwide gross of the film 'How to Train Your Dragon II' (US$)?

The eight other questions in the Different question condition were:

In 2012, what was the annual consumption of electricity (megawatt hours) in Kazakhstan?

What was the lowest daily value of shares traded on New York Stock Exchange in Year 2003

(US$)?

What is the average time spent by a person per year on social networking online (minutes)?

How much does a typical refrigerator weight (in pounds)?

What is the population of Suriname?

What is the average distance a car travels per year (in km)?

What is the average annual water consumption per household (in Liters)?

What were the global iPhone sales during 2018 (units)?

The high anchors were randomly generated but always had a magnitude one greater than the true

answer. The low anchors were randomly generated always with a magnitude one less than the true

answer.

Design and Procedure

The experiment had two independent variables: Question condition, either same or different;

Anchor condition, either high or low. These independent variables were varied between participants, each

participant received all their paired questions in either the same or different condition, and they received

either all the high anchor high or all the low anchors.

During tutorials for the class Analytic Thinking at the University of Sydney participants

completed the experiment individually on computers in class or online. They then completed the

experiment in a set of steps. First participants answered some demographic questions, then they did the

Paired task, and then the Phone task. After completing the experiment participants indicated whether or

not they consented to having their data included in the data set.

3

Hypotheses

We proposed four hypotheses related to anchoring effects. First, we will test whether there is an

anchoring effect when participants can know for certain that the anchor is random and completely

unrelated to the question being asked. We predict that participants with relatively high phone numbers

will produce higher estimates than those with relatively low phone number.

Hypothesis 1: Participants with phone numbers above the median produced higher Battle of

Chalons estimates than those with phone number below the median.

Another way to test whether people’s phone numbers influence their estimates is to calculate the

correlation between them.

Hypothesis 2: Participants’ phone numbers and their estimates for the Battle of Chalons will have a

positive correlation.

For the Paired task, if the anchoring effect depends on the similarity of the question introducing the

anchor and the question asking for the estimate, then would expect a bigger impact of the anchor for

participants in the same question condition than the different question condition.

Hypothesis 3: In the Paired task, participants in the High anchor condition will produce higher

aggregated estimates if they were also in the same question condition than if they were in the

different question condition.

For the Paired task, we would expect a result consist with the result of Hypothesis 3 for participants in the

low anchor condition.

Hypothesis 4: In the Paired task, participants in the Low anchor condition will produce lower

aggregated estimates if they were also in the same question condition than if they were in the

different question condition.

Results

The data set for our class can be found on the Canvas site for ATHK1001 under “Assignment 1”.

This assignment description can be found there as well as an Excel file called “Assignment1_dataset.xls”.

This Excel file contains all the data for the assignment and has 294 data lines, one for each participant.

Each participant has values for 6 variables, and the values of each variable are in a single column of the

file.

The first variable is an id number. There are two variables related to the Phone task, the variable

“phone_number” which is the number participants entered for their phone number (plus 100), and the

variable “Chalons_estimate” which shows participants’ estimates for the year of the Battle of Chalons.

There are only 261 participants with values for these variables because this task came at the end of the

experiment, so some participants ran out of time. Excel formula can sometimes act nonintuitively when

presented with blank cells, so be aware of this.

The need to make this data public for the assignment created a privacy issue due to the

“phone_number” variable because it potentially includes part of each participant’s phone number. Some

of the values for this variable are unique, so if a student has friends in the class who know their phone

number, then these friends may be able to identify the student’s data. This could not be done with

complete confidence because only about one-third of the class has data for this variable and participants

could have deliberately or accidently entered incorrect numbers. However, participants were told that

their data would be kept private so we decided we needed to make identifying a participant much more

difficult. We did this by randomly adding a number between 0 and 100 to each value for the

phone_number variable. The data file you have access to contains this modified data. We checked all the

analysis impacted by this variable that you are requested to carry out and found that there was no material

impact on the analysis, so you can ignore this change to the data when answering the questions below.

There are three variables related to the Paired task. “Anchor_condition” says whether the

participant was given high or low anchors, “Question_condition” says whether the participant received

4

the same question or a different question to that used to present the anchor. “Aggregated_estimates”

which represent how close overall a participant’s estimate was to the mean estimate, with negative

numbers indicating they tended to be below the mean of everyone’s estimates and positive numbers

indicating they tended to be above.

The calculation of the Aggregated estimates variable is somewhat complicated and to interpret it

you do not need to understand how it was calculated, just what it represents. If you are interested though,

this is what we did. We could not just take the average of participants estimates because the questions had

very different answers meaning they were on different scales. To put them onto to the same scale we

converted them into standard scores (standard scores are calculated by first calculating the sample’s mean

and standard deviation for a variable, then calculating the difference between each participant’s estimate

for that variable and the variable’s mean, and finally dividing this difference by the sample’s standard

deviation). We then calculated each participant’s mean standard score across their estimates, which is

what Aggregated_estimates are.

WHAT YOU WILL WRITE

Your task is to analyse the data in order to test the four hypotheses proposed above. You will do this by

addressing each of the following twelve questions. Answer all questions with complete sentences, not

with just numbers, notes or tables. Do not include the text of the questions in your assignment (this

will trigger a plagiarism warning), but you should include the number of the question being addressed.

1) For the Phone task the median phone number (modified) was 611. For participants with phone

numbers below or equal to the median calculate and state the mean and standard deviation of their

estimate for the Battle of Chalons. Do the same for participants with phone numbers above the median. (4

marks)

2) Based on the means you calculated in Question 1 use a t-test to test Hypothesis 1, that participants with

phone numbers above the median produced higher Battle of Chalons estimates than those with phone

number below the median. Report the p-level for the t-test and state clearly whether or not Hypothesis 1

was supported, and why. (Note that we will be discussing hypothesis testing in lectures in Week 4 and

practicing using Excel to test hypotheses in tutorials in Week 5. So you may need to wait to answer this

question until we have covered the relevant material in class.)

(4 marks)

3) Test Hypothesis 2 by calculating the correlation between participants’ phone numbers and their

estimates for the Battle of Chalons. To test the statistical significance of the correlation you can use the

fact that for a sample n=261 any correlation with greater magnitude than .122 is statistically significant at

the p<.05 level. State whether Hypothesis 2 was supported, and why. (4 marks)

4) Present a scatter graph to show the relationship between phone numbers and their estimates for the

Battle of Chalons. Based on your analysis in Questions 1-4, how strong does the influence of the anchor

on the estimated answers appear to be? Is testing Hypothesis 1 or testing Hypothesis 2 better convey the

nature of this influence? (6 marks)

5) For the Paired task calculate four means and standard deviations for aggregated estimates: for

participants in the high anchor condition and the same question condition, for participants in the high

anchor condition and the different question condition, for participants in the low anchor condition and the

same question condition, for participants in the low anchor condition and the different question condition.

(8 marks)

6) Based on the means calculated in Question 5, test Hypothesis 3 that participants in the High anchor

condition will produce higher aggregated estimates if they were also in the same question condition than

5

if they were in the different question condition. Report the p-level for the t-test and state clearly whether

or not Hypothesis 3 was supported, and why. (4 marks)

7) Based on the means calculated in Question 5, test Hypothesis 4 that participants in the Low anchor

condition will produce lower aggregated estimates if they were also in the same question condition than if

they were in the different question condition. Report the p-level for the t-test and state clearly whether or

not Hypothesis 4 was supported, and why. (4 marks)

8) Are your conclusions for Hypothesis 3 and Hypothesis 4 consistent? Explain your answer and any

implications (3 marks)

9) Identify three different issues with the way we collected data which could limit our ability to draw

conclusions from it. These issues could relate to one or more of the hypotheses. Clearly differentiate the

three issues as “Issue 1”, “Issue 2” and “Issue 3” and explain how each of these issues relates to a data

collection consideration raised in ATHK1001 lectures. For each issue suggest a way it might be resolved

in future research on this topic or if it cannot be resolved then explain why. (12 marks)

10) Summarize what YOUR data analysis tells us about anchoring effects. What do our results add to

Tversky and Kahneman’s (1974) discussion of anchoring, particularly on page 1128 of their article?

Explain your answers with reference to the results of your testing of the hypotheses and possibly the

issues you raised in Question 9. (6 marks)

11) What do you think is the single most important finding from our experiment? Why? (3 marks)

12) Include a reference section which lists the full reference for any paper you have cited when

addressing these questions. You must cite Tversky and Kahneman (1974) in Question 10, and include

citations where ever appropriate. You should use APA style for citations and references, but we will

accept other standard journal article referencing formats. (2 marks)

References

Judgement under uncertainty. Author(s): Amos Tversky and Daniel Kahneman. Science, volume 185,

pages 1124-1131

THIS IS NOT IN A STANDARD REFERENCING FORMAT, YOU WILL NEED TO REFORMAT

THIS FOR Q.12.

Strack, F., & Mussweiler. T. (1997). Explaining the Enigmatic Anchoring Effect: Mechanisms of

Selective Accessibility. Journal of Personality and Social Psychology, 73, 437-446.

[NOTE THAT YOU DO NOT HAVE TO READ THIS PAPER, BUT YOU CAN IF YOU WISH TO]

Note that you do not have to use other sources for answering these questions, but if you do then you must

correctly cite and reference these sources.

Formatting Recommendations

Our preferences

Use the font “Times New Roman”, 12-point size, and double-space all the lines.

Indent the beginning of each paragraph using one tab space.

Use APA referencing style

- 留学生代写
- Python代写
- Java代写
- c/c++代写
- 数据库代写
- 算法代写
- 机器学习代写
- 数据挖掘代写
- 数据分析代写
- Android代写
- html代写
- 计算机网络代写
- 操作系统代写
- 计算机体系结构代写
- R代写
- 数学代写
- 金融作业代写
- 微观经济学代写
- 会计代写
- 统计代写
- 生物代写
- 物理代写
- 机械代写
- Assignment代写
- sql数据库代写
- analysis代写
- Haskell代写
- Linux代写
- Shell代写
- Diode Ideality Factor代写
- 宏观经济学代写
- 经济代写
- 计量经济代写
- math代写
- 金融统计代写
- 经济统计代写
- 概率论代写
- 代数代写
- 工程作业代写
- Databases代写
- 逻辑代写
- JavaScript代写
- Matlab代写
- Unity代写
- BigDate大数据代写
- 汇编代写
- stat代写
- scala代写
- OpenGL代写
- CS代写
- 程序代写
- 简答代写
- Excel代写
- Logisim代写
- 代码代写
- 手写题代写
- 电子工程代写
- 判断代写
- 论文代写
- stata代写
- witness代写
- statscloud代写
- 证明代写
- 非欧几何代写
- 理论代写
- http代写
- MySQL代写
- PHP代写
- 计算代写
- 考试代写
- 博弈论代写
- 英语代写
- essay代写
- 不限代写
- lingo代写
- 线性代数代写
- 文本处理代写
- 商科代写
- visual studio代写
- 光谱分析代写
- report代写
- GCP代写
- 无代写
- 电力系统代写
- refinitiv eikon代写
- 运筹学代写
- simulink代写
- 单片机代写
- GAMS代写
- 人力资源代写
- 报告代写
- SQLAlchemy代写
- Stufio代写
- sklearn代写
- 计算机架构代写
- 贝叶斯代写
- 以太坊代写
- 计算证明代写
- prolog代写
- 交互设计代写
- mips代写
- css代写
- 云计算代写
- dafny代写
- quiz考试代写
- js代写
- 密码学代写
- ml代写
- 水利工程基础代写
- 经济管理代写
- Rmarkdown代写
- 电路代写
- 质量管理画图代写
- sas代写
- 金融数学代写
- processing代写
- 预测分析代写
- 机械力学代写
- vhdl代写
- solidworks代写
- 不涉及代写
- 计算分析代写
- Netlogo代写
- openbugs代写
- 土木代写
- 国际金融专题代写
- 离散数学代写
- openssl代写
- 化学材料代写
- eview代写
- nlp代写
- Assembly language代写
- gproms代写
- studio代写
- robot analyse代写
- pytorch代写
- 证明题代写
- latex代写
- coq代写
- 市场营销论文代写
- 人力资论文代写
- weka代写
- 英文代写
- Minitab代写
- 航空代写
- webots代写
- Advanced Management Accounting代写
- Lunix代写
- 云基础代写
- 有限状态过程代写
- aws代写
- AI代写
- 图灵机代写
- Sociology代写
- 分析代写
- 经济开发代写
- Data代写
- jupyter代写
- 通信考试代写
- 网络安全代写
- 固体力学代写
- spss代写
- 无编程代写
- react代写
- Ocaml代写
- 期货期权代写
- Scheme代写
- 数学统计代写
- 信息安全代写
- Bloomberg代写
- 残疾与创新设计代写
- 历史代写
- 理论题代写
- cpu代写
- 计量代写
- Xpress-IVE代写
- 微积分代写
- 材料学代写
- 代写
- 会计信息系统代写
- 凸优化代写
- 投资代写
- F#代写
- C#代写
- arm代写
- 伪代码代写
- 白话代写
- IC集成电路代写
- reasoning代写
- agents代写
- 精算代写
- opencl代写
- Perl代写
- 图像处理代写
- 工程电磁场代写
- 时间序列代写
- 数据结构算法代写
- 网络基础代写
- 画图代写
- Marie代写
- ASP代写
- EViews代写
- Interval Temporal Logic代写
- ccgarch代写
- rmgarch代写
- jmp代写
- 选择填空代写
- mathematics代写
- winbugs代写
- maya代写
- Directx代写
- PPT代写
- 可视化代写
- 工程材料代写
- 环境代写
- abaqus代写
- 投资组合代写
- 选择题代写
- openmp.c代写
- cuda.cu代写
- 传感器基础代写
- 区块链比特币代写
- 土壤固结代写
- 电气代写
- 电子设计代写
- 主观题代写
- 金融微积代写
- ajax代写
- Risk theory代写
- tcp代写
- tableau代写
- mylab代写
- research paper代写
- 手写代写
- 管理代写
- paper代写
- 毕设代写
- 衍生品代写
- 学术论文代写
- 计算画图代写
- SPIM汇编代写
- 演讲稿代写
- 金融实证代写
- 环境化学代写
- 通信代写
- 股权市场代写
- 计算机逻辑代写
- Microsoft Visio代写
- 业务流程管理代写
- Spark代写
- USYD代写
- 数值分析代写
- 有限元代写
- 抽代代写
- 不限定代写
- IOS代写
- scikit-learn代写
- ts angular代写
- sml代写
- 管理决策分析代写
- vba代写
- 墨大代写
- erlang代写
- Azure代写
- 粒子物理代写
- 编译器代写
- socket代写
- 商业分析代写
- 财务报表分析代写
- Machine Learning代写
- 国际贸易代写
- code代写
- 流体力学代写
- 辅导代写
- 设计代写
- marketing代写
- web代写
- 计算机代写
- verilog代写
- 心理学代写
- 线性回归代写
- 高级数据分析代写
- clingo代写
- Mplab代写
- coventorware代写
- creo代写
- nosql代写
- 供应链代写
- uml代写
- 数字业务技术代写
- 数字业务管理代写
- 结构分析代写
- tf-idf代写
- 地理代写
- financial modeling代写
- quantlib代写
- 电力电子元件代写
- atenda 2D代写
- 宏观代写
- 媒体代写
- 政治代写
- 化学代写
- 随机过程代写
- self attension算法代写
- arm assembly代写
- wireshark代写
- openCV代写
- Uncertainty Quantificatio代写
- prolong代写
- IPYthon代写
- Digital system design 代写
- julia代写
- Advanced Geotechnical Engineering代写
- 回答问题代写
- junit代写
- solidty代写
- maple代写
- 光电技术代写
- 网页代写
- 网络分析代写
- ENVI代写
- gimp代写
- sfml代写
- 社会学代写
- simulationX solidwork代写
- unity 3D代写
- ansys代写
- react native代写
- Alloy代写
- Applied Matrix代写
- JMP PRO代写
- 微观代写
- 人类健康代写
- 市场代写
- proposal代写
- 软件代写
- 信息检索代写
- 商法代写
- 信号代写
- pycharm代写
- 金融风险管理代写
- 数据可视化代写
- fashion代写
- 加拿大代写
- 经济学代写
- Behavioural Finance代写
- cytoscape代写
- 推荐代写
- 金融经济代写
- optimization代写
- alteryxy代写
- tabluea代写
- sas viya代写
- ads代写
- 实时系统代写
- 药剂学代写
- os代写
- Mathematica代写
- Xcode代写
- Swift代写
- rattle代写
- 人工智能代写
- 流体代写
- 结构力学代写
- Communications代写
- 动物学代写
- 问答代写
- MiKTEX代写
- 图论代写
- 数据科学代写
- 计算机安全代写
- 日本历史代写
- gis代写
- rs代写
- 语言代写
- 电学代写
- flutter代写
- drat代写
- 澳洲代写
- 医药代写
- ox代写
- 营销代写
- pddl代写
- 工程项目代写
- archi代写
- Propositional Logic代写
- 国际财务管理代写
- 高宏代写
- 模型代写
- 润色代写
- 营养学论文代写
- 热力学代写
- Acct代写
- Data Synthesis代写
- 翻译代写
- 公司法代写
- 管理学代写
- 建筑学代写
- 生理课程代写
- 动画代写
- 高数代写
- 内嵌式代写
- Truffles代写
- 地质学代写