diff --git a/01_materials/slides/00_introduction_slides.pdf b/01_materials/slides/00_introduction_slides.pdf index b50dd64e..baed8dea 100644 Binary files a/01_materials/slides/00_introduction_slides.pdf and b/01_materials/slides/00_introduction_slides.pdf differ diff --git a/01_materials/slides/01_probability_intro_slides.pdf b/01_materials/slides/01_probability_intro_slides.pdf index 13cb5a01..60f5d800 100644 Binary files a/01_materials/slides/01_probability_intro_slides.pdf and b/01_materials/slides/01_probability_intro_slides.pdf differ diff --git a/01_materials/slides/02_populations_and_samples_slides.pdf b/01_materials/slides/02_populations_and_samples_slides.pdf index dfe0c5b0..87b7db29 100644 Binary files a/01_materials/slides/02_populations_and_samples_slides.pdf and b/01_materials/slides/02_populations_and_samples_slides.pdf differ diff --git a/01_materials/slides/03_simple_probability_samples_slides.pdf b/01_materials/slides/03_simple_probability_samples_slides.pdf index 25849917..6a319f31 100644 Binary files a/01_materials/slides/03_simple_probability_samples_slides.pdf and b/01_materials/slides/03_simple_probability_samples_slides.pdf differ diff --git a/01_materials/slides/04_stratified_sampling_slides.pdf b/01_materials/slides/04_stratified_sampling_slides.pdf index 34abc9c9..ec3b7e89 100644 Binary files a/01_materials/slides/04_stratified_sampling_slides.pdf and b/01_materials/slides/04_stratified_sampling_slides.pdf differ diff --git a/01_materials/slides/05_cluster_sampling_slides.pdf b/01_materials/slides/05_cluster_sampling_slides.pdf index 616bd632..e2c424b4 100644 Binary files a/01_materials/slides/05_cluster_sampling_slides.pdf and b/01_materials/slides/05_cluster_sampling_slides.pdf differ diff --git a/01_materials/slides/06_errors_slides.pdf b/01_materials/slides/06_errors_slides.pdf index 588f157d..226a3c49 100644 Binary files a/01_materials/slides/06_errors_slides.pdf and b/01_materials/slides/06_errors_slides.pdf differ diff --git a/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf b/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf index 171c7b5d..8132db0f 100644 Binary files a/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf and b/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf differ diff --git a/01_materials/slides/08_ethics_slides.pdf b/01_materials/slides/08_ethics_slides.pdf index 25e74e9a..66a58a8e 100644 Binary files a/01_materials/slides/08_ethics_slides.pdf and b/01_materials/slides/08_ethics_slides.pdf differ diff --git a/01_materials/slides/09_privacy_slides.pdf b/01_materials/slides/09_privacy_slides.pdf index a15ad262..c2b1f4fa 100644 Binary files a/01_materials/slides/09_privacy_slides.pdf and b/01_materials/slides/09_privacy_slides.pdf differ diff --git a/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md b/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md index 50a8bac4..866a3885 100644 --- a/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md +++ b/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md @@ -38,7 +38,7 @@ $ echo "Data Sciences Institute" - Consider a population of size *N* - Simple random sampling **with replacement**: 1. Select one unit for measurement, with probability 1/ *N* - 2. Sampled unit is return to the population + 2. Sampled unit is returned to the population 3. Select second unit for measurement, with probability 1/ *N* 4. Repeat until desired sample size is obtained diff --git a/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md b/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md index 5756de8d..b9136764 100644 --- a/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md +++ b/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md @@ -79,7 +79,7 @@ $ echo "Data Sciences Institute" - The variance of the sample mean can be computed, - > $$ \hat{V}(\bar{y}) = \sum_{h=1}^{H}\frac{s_h^2}{n_h}(1-\frac{n_h}{N_h})(\frac{N^h}{N})^2 $$ + > $$ \hat{V}(\bar{y}) = \sum_{h=1}^{H}\frac{s_h^2}{n_h}(1-\frac{n_h}{N_h})(\frac{N_h}{N})^2 $$ (how much our mean will vary across samples) @@ -114,7 +114,7 @@ $ echo "Data Sciences Institute" - The population mean can be estimated directly using a weighted mean of recorded observations: - > $$ \bar{y} = \frac{\sum_{h=1}^{H} \sum_{i=1}^{h} w_{hi}y_{hi}}{\sum_{h=1}^{H} \sum_{i=1}^{h} w_{hi}} $$ + > $$ \bar{y} = \frac{\sum_{h=1}^{H} \sum_{i=1}^{n_h} w_{hi}y_{hi}}{\sum_{h=1}^{H} \sum_{i=1}^{n_h} w_{hi}} $$ - In stratified sampling, we need to sum over the weights and units in each stratum, and then sum over all strata. diff --git a/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md b/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md index a8da9f43..9853d04c 100644 --- a/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md +++ b/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md @@ -187,7 +187,7 @@ $ echo "Data Sciences Institute" - The sample variance of the PSU totals is, - > $$ s_t^2=\frac{1}{n-1}\sum_{i=1}^{N}(t_i-\frac{\hat{t}}{N})^2 $$ + > $$ s_t^2=\frac{1}{n-1}\sum_{i=1}^{n}(t_i-\frac{\hat{t}}{n})^2 $$ - $s_t^2$ can then be used to compute the standard error of the estimated sample mean: diff --git a/03_instructional_team/markdown_slides/09_privacy_slides.md b/03_instructional_team/markdown_slides/09_privacy_slides.md index f38b9c4d..7e13a5a9 100644 --- a/03_instructional_team/markdown_slides/09_privacy_slides.md +++ b/03_instructional_team/markdown_slides/09_privacy_slides.md @@ -14,7 +14,7 @@ $ echo "Data Sciences Institute" # Key Texts - Salganik, M. (2019). Understanding and managing informational risk. In *Bit by bit: Social research in the Digital age* (pp. 307–314). Chapter, Princeton University Press. -- Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., OBrien, D.R., Steinke, T., & Vadhan, S. (2018). *[Differential privacy: A primer for a non-technical audience](https://salil.seas.harvard.edu/publications/differential-privacy-primer-non-technical-audience)* . *Vanderbilt Journal of Entertainment & Technology Law, * 21(1) 209-275. +- Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., O'Brien, D.R., Steinke, T., & Vadhan, S. (2018). *[Differential privacy: A primer for a non-technical audience](https://salil.seas.harvard.edu/publications/differential-privacy-primer-non-technical-audience)* . *Vanderbilt Journal of Entertainment & Technology Law, * 21(1) 209-275. --- diff --git a/04_this_cohort/resources/sampling_multiple_imputation_exerises.py b/04_this_cohort/resources/sampling_multiple_imputation_exercises.py similarity index 100% rename from 04_this_cohort/resources/sampling_multiple_imputation_exerises.py rename to 04_this_cohort/resources/sampling_multiple_imputation_exercises.py diff --git a/README.md b/README.md index 79f8d2ef..99dade72 100644 --- a/README.md +++ b/README.md @@ -69,13 +69,11 @@ module, with access to live help. Content is not facilitated, but rather this time should be driven by participants. We encourage participants to come to these work periods with questions and problems to work through.   Participants are encouraged to engage actively during the learning -module. They key to developing the core skills in each learning module +module. The key to developing the core skills in each learning module is through practice. The more participants engage in coding along with the instructional team, and applying the skills in each module, the more likely it is that these skills will solidify.   -## Schedule - ## Schedule | Live Learning Session | Topic | Assignments | Resources | | @@ -127,7 +125,7 @@ Feel free to use the following as resources: - [LLN Demo](./04_this_cohort/resources/5.1_probability_lln_demo.py) - [Amazon Exercises](./04_this_cohort/resources/amazon_exercises.pdf) - [Multiple Imputation - Exercises](./04_this_cohort/resources/sampling_multiple_imputation_exerises.py) + Exercises](./04_this_cohort/resources/sampling_multiple_imputation_exercises.py) ### Videos