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PSYC2001 ASSIGNMENT
Creation date：2024-05-04 14:33:13
Belonging category：心理学Psychology
Psychology

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PSYC2001 ASSIGNMENT

This assignment is worth 20% of your final mark and is to be submitted electronically via the
Turnitin link on Moodle by Midnight on Friday April 22 (Week 10). Please see the School of
Psychology Student Guide for information on School policies regarding assessment, late
penalties, and submission of work.
This assignment is designed to give you the experience of carrying out both planned and
exploratory analyses and then reporting them. It is deliberately open-ended and involves
judgment on your part, just as real research does. It draws on material from both the first and
the second half of the course, and in particular on the Week 7 lecture on replication and the
online module on Writing a Results Section.
The assignment is based on a fictitious study on wellbeing in two different cultures, one that
emphasizes competition and individual achievement (“individualistic”) and one that
emphasizes cooperation and a sense of community (“communal”). The researcher is
interested in variables that predict wellbeing across the sample as a whole, as well as variables
that distinguish between the two cultures. She administers a survey to 50 adult participants
from each culture, and collects the following data:
Variable jamovi name Categorical variables
Culture culture 1 = individualistic, 2 = communal
Gender gender 1 = female, 2 = male
Relationship relationship 1 = single, 2 = in relationship
Employment employment 1 = unemployed, 2 = in job
Continuous variables
Age age years
Exercise exercise 1 = little to 7 = regular strenuous exercise
Number of close friends friends count
Illness illness 1 = healthy to 10 = serious illness
Social media use socialmedia 0 = none to 7 = more than 4 hours/day
Trust in government trustgov 1 = very little to 5 = high
Total assets assets thousands of dollars
Total debt debt thousands of dollars
Daily commute time commute minutes
Subjective wellbeing wellbeing 1 (low) to 9 (high)


You can download the data file from the Assignment section on Moodle. The data are
available in two formats:
1) a ‘csv’ file, which is easily editable in Excel,
2) an ‘omv’ file, the format that jamovi expects. Note that this file is NOT edited with the
appropriate variable types. You will need to edit the variables.

Your assignment has three parts.
Part 1: Hypotheses [half a page double spaced] 5 marks
Imagine yourself in the position of the researcher after designing the study but before
collecting the data. Based only on the design outlined on the previous page, plan one analysis
to address the researcher’s interest in predicting wellbeing, and one analysis to compare the
two cultures.
Prediction: choose up to four variables that you hypothesise will be good predictors of
wellbeing, and indicate how you would combine them together to form a single predictor
variable using the jamovi computed variable function. Explain why you have selected this
combination of variables.
Group comparison: choose one variable that you hypothesise will best differentiate between
the two cultures. Explain why you have chosen this variable.
In writing this section you need to state the prediction analysis you plan to do and explain
why, as well as state the culture comparison analysis you wish to do and why. You should
also include the hypothesized outcome of these analyses. You do not need to cite any papers
or do additional research to provide support for your reasoning. For this assignment, it is fine
for your reasoning to be based on your intuitions.
Please do not look at the data or perform any calculations before writing this part of the
assignment. This will defeat the purpose of the exercise. We want you to have the
experience of forming hypotheses on theoretical grounds, and then in the next step find out
whether your hypotheses are supported by the data or not. This is how real research is carried
out. Your answers in this section will not be marked on how well your hypotheses match the
actual data, but on your rationale for forming them in the first place.
Part 2: Analyses [half a page double spaced, plus output] 7 marks
First, carry out the analyses you planned in Part 1 and show the jamovi output. In one
sentence, describe how you felt when you saw these results.
Second, explore the data set, using whatever descriptive statistics you consider appropriate
(there is no need to show these analyses). On the basis of this exploration, come up with one
analysis to capture what the data tell us about prediction of wellbeing, and one comparison
that best illustrates the difference between the two cultures. These analyses will be of the
same type as the ones you planned, but this time they will be based on what you see in the
data, not on what you expected to see. Show the jamovi output for these two analyses. For
each analysis, explain why you chose this analysis specifically.
Comment on the differences between your planned and exploratory analyses, and on how
likely each analysis would be to replicate if the study was repeated.
Part 3: Report your results [1 page, double spaced] 8 marks
Write a brief report of the results of your analyses in the form of a typical Results section in a
journal. For a guide on how to write up a Results section, please see the “How to Write a
Results Section” module on Moodle (under Week 7). For example, you may want to report
descriptive statistics, the results of null hypothesis significance tests or confidence intervals,
and a visual depiction of your results. You should finish with a brief conclusion, based on
your analyses.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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