diff --git a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb index 873f5985..4ac2a820 100644 --- a/02_activities/assignments/a1_sampling_and_reproducibility.ipynb +++ b/02_activities/assignments/a1_sampling_and_reproducibility.ipynb @@ -16,7 +16,23 @@ "cell_type": "markdown", "id": "4ea73db3", "metadata": {}, - "source": [] + "source": [ + " First, the code creates a group of 1000 people. There are 200 people at the wedding and 800 people at the brunch. This group of 1000 people is the sampling frame.\n", + "\n", + "The first random sample happens when the code chooses who gets infected:\n", + "\n", + "`np.random.choice(ppl.index, size=int(len(ppl) * ATTACK_RATE), replace=False)`\n", + "\n", + "Since `ATTACK_RATE = 0.10`, the code randomly chooses 100 people out of the 1000 people to be infected. The function uses `ppl.index` as the sampling frame. The argument `replace=False` means that one person cannot be selected more than once.\n", + "\n", + "The second random step happens during primary contact tracing:\n", + "\n", + "`np.random.rand(sum(ppl['infected'])) < TRACE_SUCCESS`\n", + "\n", + "This part randomly decides which infected people are successfully traced. Since `TRACE_SUCCESS = 0.20`, each infected person has a 20% chance of being traced. This is like a Bernoulli process because each infected person either gets traced or does not get traced.\n", + "\n", + "The third stage is secondary contact tracing. This step is depends on the random results from the earlier steps. The code counts how many traced infected people came from each event. If an event has at least 2 traced cases, then all infected people from that event are marked as traced." + ] }, { "cell_type": "markdown", @@ -30,7 +46,9 @@ "cell_type": "markdown", "id": "4cf5d993", "metadata": {}, - "source": [] + "source": [ + "When I changed the number of repetitions, the graphs changed in how stable they looked. With 10 repetitions, the graph was very rough and unstable. Since the simulation only ran 10 times, each random result had a big effect on the final graph. The traced cases from weddings were especially spread out. In some simulations, very few traced cases were connected to weddings, while in others the proportion was much higher. With 100 repetitions, the graph became more stable than the 10-repetition graph. The results were more clearly centered around 0.20, which makes sense because 200 out of the 1000 people were at the wedding. However, there was still some variation, especially for the traced cases. With 1000 repetitions, the graph was much smoother. This is because running the simulation many more times helps average out random variation. However, the results are still not fully reproducible unless a random seed is set. Without a seed, the script can still choose different infected and traced people each time it runs." + ] }, { "cell_type": "markdown", @@ -44,7 +62,13 @@ "cell_type": "markdown", "id": "77613cc3", "metadata": {}, - "source": [] + "source": [ + "I made the code reproducible by adding `np.random.seed(42)` before running the simulation.\n", + "\n", + "This is needed because the model uses random functions, such as `np.random.choice()` to choose infected people and `np.random.rand()` to decide which infected people are traced. Without setting a seed, these random choices change every time the code runs.\n", + "\n", + "After adding the seed, NumPy uses the same sequence of random numbers each time. This means that if I run the script multiple times with the same number of repetitions, I get the same output and the same graph." + ] }, { "cell_type": "markdown", @@ -56,10 +80,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "837f4d27", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "114.41s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (2.3.3)\n", + "Requirement already satisfied: numpy in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (2.0.2)\n", + "Requirement already satisfied: matplotlib in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (3.9.4)\n", + "Collecting seaborn\n", + " Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from pandas) (2026.1.post1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from pandas) (2026.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from matplotlib) (1.3.0)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from matplotlib) (4.60.2)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/eve/Downloads/LCR/lcr-env/lib/python3.9/site-packages (from 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"Installing collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pandas numpy matplotlib seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "ab8587a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Import necessary libraries\n", "import pandas as pd\n", @@ -75,6 +156,8 @@ "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", + "np.random.seed(42)\n", + "\n", "# Constants representing the parameters of the model\n", "ATTACK_RATE = 0.10\n", "TRACE_SUCCESS = 0.20\n", @@ -131,7 +214,7 @@ " return p_wedding_infections, p_wedding_traces\n", "\n", "# Run the simulation 1000 times\n", - "results = [simulate_event(m) for m in range(1000)]\n", + "results = [simulate_event(m) for m in range(100)]\n", "props_df = pd.DataFrame(results, columns=[\"Infections\", \"Traces\"])\n", "\n", "# Plotting the results\n", @@ -193,9 +276,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python (lcr-env)", "language": "python", - "name": "python3" + "name": "lcr-env" }, "language_info": { "codemirror_mode": { @@ -207,7 +290,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/02_activities/assignments/a2_survey_design_and_evaluation.md b/02_activities/assignments/a2_survey_design_and_evaluation.md index b4f036f2..167d6f6a 100644 --- a/02_activities/assignments/a2_survey_design_and_evaluation.md +++ b/02_activities/assignments/a2_survey_design_and_evaluation.md @@ -44,26 +44,107 @@ The number of your chosen topic: `#` Describe the purpose of your survey: ``` -write your answer here... +The purpose of this survey is to examine how age is associated with music taste and perceptions of popular music. The survey will compare responses across different age groups while also asking individuals to reflect on how their own music preferences have changed over time. The results will be used to understand whether age influences genre preference, openness to current popular music, and evaluations of popular music. ``` Describe your target population, sampling frame, sampling units, and observational units: ``` -write your answer here... +The target population is adults aged 18 and older living in Canada. The sampling frame is a Canadian online survey panel containing adults who can be contacted for survey participation and whose age group is known. The sampling units are individual adult respondents. + +The sampling strategy will use stratified quota sampling by age group. This allows comparison across age groups, but the results will be interpreted as age-group or cohort differences rather than direct causal effects of age. Retrospective questions will also ask respondents to compare their current music taste with their mid-teen years, allowing the survey to capture perceived changes over the life course. ``` Your 5-10 question survey: ``` -1. write your question here... -2. write your question here... -3. write your question here... -4. write your question here... -5. write your question here... -6. write your question here... (optional) -7. write your question here... (optional) -8. write your question here... (optional) -9. write your question here... (optional) -10. write your question here... (optional) +Definition: In this survey, “popular music today” refers to mainstream music that has been widely listened to, streamed, shared on social media, or played on radio/music charts in the past 12 months. + +1. What is your age group? + + A 30–39 + B 40–49 + C 50–59 + D 60–69 + E 70 or older + +2. How often do you listen to popular music today, as defined above? + + A Daily + B Several times a week + C About once a week + D Less than once a week + E Rarely or never + +3. Which types of popular music do you currently listen to most often? Please select up to three. + + A Pop + B Hip-hop / rap + C R&B + D Rock + E Electronic / dance music + F Country + G Indie / alternative + H K-pop / J-pop / C-pop + I Latin music + J Other: ________ + +4. How much do you enjoy popular music today? + + A Not at all + B Slightly + C Moderately + D Very much + E Extremely + +5. How similar is popular music today to the music you personally prefer? + + A Not similar at all + B Slightly similar + C Moderately similar + D Very similar + E Extremely similar + +6. How open are you to listening to popular music made by artists from a younger generation than your own? + + A Not open at all + B Slightly open + C Moderately open + D Very open + E Extremely open + +7. Thinking back to your late teens or early adulthood, around ages 16–24, which types of popular music did you listen to most often at that time? Please select up to three. + + A Pop + B Hip-hop / rap + C R&B + D Rock + E Electronic / dance music + F Country + G Indie / alternative + H K-pop / J-pop / C-pop + I Latin music + J Other: ________ + +8. Compared with your late teens or early adulthood, how much has your personal taste in popular music changed? + + A. Not at all + B. Slightly + C. Moderately + D. A lot + E. Completely + +9. When you compare the popular music you liked in your late teens or early adulthood with popular music today, which statement best describes your view? + + A. My personal taste has changed more than popular music itself. + B. Popular music itself has changed more than my personal taste. + C. Both my personal taste and popular music have changed. + D. Neither my personal taste nor popular music has changed very much. + E. I am not sure. + +10. In one or two sentences, please describe how getting older has changed, or has not changed, the way you perceive popular music. + + + + ``` ## Part B - Survey Evaluation: @@ -71,7 +152,33 @@ Your 5-10 question survey: Identify and describe survey features: ``` -write your answer here +Sample type: This was a cross-sectional sample survey. It used a stratified probability sample. The sample was stratified by province and census metropolitan area. It also used a two-stage design: first, groups of telephone numbers were selected, and then one eligible person aged 15 or older was randomly selected from each household. + +Sample size: Statistics Canada used a field sample of about 50,000 units. Around 40,000 invitation letters were sent for the online questionnaire, and about 24,000 completed questionnaires were expected. + +Target population: The target population was people aged 15 and older living in the ten provinces of Canada. The survey did not include people living in the territories or full-time residents of institutions. + +Sampling frame: The sampling frame combined landline and cellphone numbers from the Census and administrative sources with Statistics Canada’s dwelling frame. This means the frame was based on groups of telephone numbers connected to household addresses. + +Survey mode(s):The survey was completed either online through an electronic questionnaire or by computer-assisted telephone interviewing. Respondents could answer in English or French. Proxy responses were not allowed. + +Timeline: Data collection took place from September 4, 2018 to December 28, 2018. The data were released on January 26, 2021. + +Response rate: The overall response rate was 41.9%. + +Weights: The data file includes a person-level weight called WGHT_PER. This weight helps make the survey results representative of the population aged 15 and older in the ten provinces. Bootstrap weights were also created so researchers can estimate sampling error. + +Data processing: Statistics Canada processed the data using automatic and manual checks. They checked for consistency, correct questionnaire flow, and possible errors. For example, they checked whether a respondent’s age matched their birth date. The telephone interview system also included built-in checks during the interview. + +Cleaning and imputation: Missing data were handled through imputation. In most cases, Statistics Canada used similar donor records to fill in missing answers. If donor imputation could not be used, mean imputation was used. Income information was partly obtained by linking survey responses to tax data for respondents who did not object to this linkage. + +Sources of error:The survey has both sampling error and non-sampling error. Sampling error happens because the survey is based on a sample rather than the whole population. Non-sampling errors may include coverage error, non-response error, response error, and processing error. + +Limitations and known biases:One limitation is that households without telephones were not covered by the survey frame. Another limitation is non-response, because not everyone selected for the survey answered it. Statistics Canada adjusted the weights to reduce this problem, but some bias may still remain. Another important limitation is that 2018 results should not be directly compared with earlier cycles because the 2018 survey added an Internet response option and changed some questions and definitions. + +Documentation and sources used：I used Statistics Canada’s survey record for the 2018 General Social Survey on Giving, Volunteering and Participating, the Public Use Microdata File Documentation and User Guide, and the Statistics Canada Summary of Changes page for this survey. + + ``` ## Rubric diff --git a/uv.lock b/uv.lock index 467e0b9c..7e717539 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,29 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.12'", "python_full_version < '3.12'", ] +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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