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Bermonti-Pérez, MA, PhD\"\nformat: \n revealjs:\n incremental: true \n chalkboard: true \n theme: dark\n center: true\n # pdf:\n # echo: false\n---\n\n\n\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n## Objectives\n:::{.center}\n- Understand the usefulness of the independent t-test\n- Understand the basic concepts of the independent t-test\n- Conduct the independent t-test\n- Interpret the results of the independent t-test\n::::\n\n# t-test\n::: {.notes}\nWhat is a t-test?\n:::\n\n\n# Basics\n\n## Usefulness\n![](./assets/ex1.png)\n\n## Usefulness\n![](./assets/ex2.png)\n\n## Usefulness\n![](./assets/ex3.png)\n\n## General form\n![](./assets/comp_groups.png)\n\n::: {.notes}\nCompare 2 groups\n:::\n\n## Even more general\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/comp_conds.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/comp_means.png)\n:::\n::::\n\n::: {.notes}\nMore general, powerful\n:::\n\n## GLM form\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex1.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex2.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex3.png)\n:::\n::::\n\n# General process (conceptual)\n\n## Get data\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-5-1.png){width=960}\n:::\n:::\n\n\n## Group data\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-6-1.png){width=960}\n:::\n:::\n\n::: {.notes}\nwhat is next?\n:::\n\n## Estimate means\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-7-1.png){width=960}\n:::\n:::\n\n::: {.notes}\nwhat is next?\n:::\n\n## Estimate effect (difference)\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-8-1.png){width=960}\n:::\n:::\n\n\n## That is all folks!\n\n# Conducting and interpreting\n\n## Hands on exercises\n### Get ready!\n::: {.notes}\nDownload data from teams\n:::\n\n## Study\n:::: {.columns}\n::: {.column width=\"50%\"}\nEffect of ADHD on planning ability\n:::\n\n::: {.column width=\"50%\"}\n### Variables\n- ADHD\n + healthy\n + ADHD\n- Plannig: Standardized test\n:::\n::::\n\n::: {.notes}\nStandardized tests: mean? SD?\n:::\n\n## Hypothesis\n- H0: No effect\n- H1: Effect\n\n::: {.notes}\n- NHST - null = 0\n\n- H0: Hardheaded\n:::\n\n## Info needed\n:::: {.columns}\n::: {.column width=\"50%\"}\n- Effect size\n:::\n\n::: {.column width=\"50%\"}\n- Statistical significance\n:::\n::::\n\n::: {.notes}\nWhat are these?\n:::\n\n## Viz\n:::: {.columns}\n::: {.column width=\"50%\"}\n- Bar plot\n:::\n\n::: {.column width=\"50%\"}\n- Box plot\n:::\n::::\n\n::: {.notes}\n- Which provides more info?\n\n- Why use both? More info = better\n:::\n\n## Descriptive stats\n::: {.notes}\n- groupby\n- detailed: mean, median, SD, Range\n:::\n\n# Conducting an independent t-test\n![](./assets/conduct_ttest.png)\n\n## Interpretation\n\n## Effect size\n\n## Unstandardized\n- Mean difference\n- 95% CI\n\n::: {.notes}\nDiff in raw scores?\n:::\n\n## Standardized\n:::: {.columns}\n::: {.column width=\"50%\"}\n- Hedges g\n- 95% CI\n::: \n\n::: {.column width=\"50%\"}\n### Interpretation\n + small: .2 - 0.49\n + medium: 0.5 - 0.79\n + large: 0.8+\n:::\n::::\n\n::: {.notes}\n- Diff in terms of SD?\n- Why Hedges?\n:::\n\n## Statistical significance\n:::: {.columns}\n::: {.column width=\"50%\"}\n### Where to look\n- Equal variances not assumed\n- Significance, two sided\n:::\n\n::: {.column width=\"50%\"}\n### Interpretation\n- p-value < 0.05 = Statistical significant\n- p-value > 0.05 = not statistical significant\n:::\n:::: \n\n# Assumptions\n## Assumptions\n- Types of variables (GLM)\n- Normal distribution\n- Homegeinity of variance (Welch's test)\n- No outliers\n\n::: {.notes}\nWhat are assumptions?\n:::\n\n## Variable types\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n### Effect of ADHD on planning\n- ADHD\n\n- Planning \n:::\n::::\n\n::: {.notes}\nDo we meet them?\n:::\n\n## Normal distribution\n- Histograms\n- QQ plots\n\n::: {.notes}\n- QQ plots: quantiles\n\n- group by condition\n\n- normal: line\n- devs: kurtosis, extremely values (at ends) \n- S shape: skewness (negative = high scores, positive = low scores)\n:::\n\n## Outliers\n- Boxplot\n\n::: {.notes}\nDo we meet them?\n:::\n\n# Closing\n\n## Recap\n- What analysis?\n- What is its use?\n- Predictor type?\n- Type of outcome?\n- Pieces of info needed?\n- Assumptions?\n\n::: {.notes}\n- t test\n- compare means\n- categorical\n- numeric\n- ES, stats sig\n- var types, normal distribution, homo variances, outliers\n:::\n\n## Questions?\n\n::: {.notes}\n- What was easiest?\n- What was hardest?\n::: ", + "markdown": "---\ntitle: \"Comparing 2 groups\"\nsubtitle: \"t-test\"\nauthor: \"Mario E. Bermonti-Pérez, MA, PhD\"\nformat: \n revealjs:\n incremental: true \n center: true\n # pdf:\n # echo: false\n---\n\n\n\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n\n\n::: {.cell}\n\n:::\n\n\n\n## Objectives\n:::{.center}\n- Understand the basic logic behind comparing 2 groups\n- Identify the usefulness of the independent t-test\n- Identify the basic concepts of the independent t-test\n- Conduct an independent t-test\n- Interpret the results of an independent t-test\n::::\n\n# t-test\n::: {.notes}\nWhat is a t-test?\n:::\n\n# Basics\n\n## Usefulness\n![](./assets/ex1.png)\n\n## Usefulness\n![](./assets/ex2.png)\n\n## Usefulness\n![](./assets/ex3.png)\n\n## General form\n![](./assets/comp_groups.png)\n\n::: {.notes}\nCompare 2 groups\n:::\n\n## More general\n![](./assets/comp_conds.png)\n\n::: {.notes}\nmore general, powerful\nintervention\n:::\n\n## Even more general\n![](./assets/comp_means.png)\n\n::: {.notes}\nmost powerful\n:::\n\n## GLM form\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex1.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex2.png)\n:::\n::::\n\n## GLM form - examples\n:::: {.columns}\n::: {.column width=\"50%\"}\n![](./assets/glm_form.png)\n:::\n\n::: {.column width=\"50%\"}\n![](./assets/glm_form_ex3.png)\n:::\n::::\n\n# General process (conceptual)\n\n## Get data\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-5-1.png){width=960}\n:::\n:::\n\n\n::: {.notes}\nbasic question: Are there 2 groups?\n:::\n\n## Group data\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-6-1.png){width=960}\n:::\n:::\n\n::: {.notes}\n- how do we compare the groups? \n- each point or overall?\n- how do we know an overall?\n:::\n\n## Estimate means\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-7-1.png){width=960}\n:::\n:::\n\n\n::: {.notes}\nDraw a line!\n:::\n\n## Estimate effect (difference)\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](ttest_files/figure-revealjs/unnamed-chunk-8-1.png){width=960}\n:::\n:::\n\n\n## That is all folks!\n## Not exactly...\n \n## We need to know:\n + is the difference real?\n + size of the difference?\n\n# Conducting and interpreting\n\n## Hands on exercises\n### Get ready!\n\n::: {.notes}\nDownload data from teams\n:::\n\n## Study\n:::: {.columns}\n::: {.column width=\"50%\"}\nEffect of ADHD on planning ability\n:::\n\n::: {.column width=\"50%\"}\n### Variables\n- ADHD\n + healthy\n + ADHD\n- Plannig: Standardized test\n:::\n::::\n\n::: {.notes}\nStandardized tests: mean? SD?\n:::\n\n## Hypothesis\n- H0: No effect\n- H1: Effect\n\n::: {.notes}\n- H0: Hardheaded\n:::\n\n## Info needed\n:::: {.columns}\n::: {.column width=\"50%\"}\n- Effect size\n:::\n\n::: {.column width=\"50%\"}\n- Statistical significance\n:::\n::::\n\n::: {.notes}\nWhat are these?\n:::\n\n## Viz\n:::: {.columns}\n::: {.column width=\"50%\"}\n- Bar plot\n:::\n\n::: {.column width=\"50%\"}\n- Box plot\n:::\n::::\n\n::: {.notes}\n- Which provides more info?\n\n- Why use both? More info = better\n:::\n\n## Descriptive stats\n- basic\n- detailed\n \n::: {.notes}\n- group data\n- detailed: mean, median, SD, Range\n:::\n\n# Conducting an independent t-test\n![](./assets/conduct_ttest.png)\n\n## Interpretation\n\n## Effect size\n\n## Unstandardized\n- Mean difference\n- 95% CI\n\n::: {.notes}\nDiff in raw scores?\n:::\n\n## Standardized\n\n:::: {.columns}\n::: {.column width=\"50%\"}\n### Indicators\n- Hedges g\n- 95% CI\n::: \n\n::: {.column width=\"50%\"}\n### Interpretation\n- small: .2 - 0.49\n- medium: 0.5 - 0.79\n- large: 0.8+\n:::\n::::\n\n::: {.notes}\n- Diff in terms of SD?\n- Why Hedges?\n:::\n\n## Statistical significance\n:::: {.columns}\n::: {.column width=\"50%\"}\n### Where to look\n- Equal variances not assumed\n- Significance, two sided\n:::\n\n::: {.column width=\"50%\"}\n### Interpretation\n- p-value < 0.05 = Statistical significant\n- p-value > 0.05 = not statistical significant\n:::\n:::: \n\n# Assumptions\n## Assumptions\n- Types of variables (GLM)\n- Normal distribution\n- Homegeinity of variance (Welch's test) - not important\n- No outliers\n\n::: {.notes}\nWhat are assumptions?\n:::\n\n# Closing\n\n## Recap\n- What analysis?\n- Usefulness?\n- Predictor type?\n- Type of outcome?\n- Pieces of info t-tests provide?\n- Assumptions?\n\n::: {.notes}\n- t test\n- compare means\n- categorical\n- numeric\n- ES, stats sig\n- var types, normal distribution, homo variances, outliers\n:::\n\n## Questions?\n\n::: {.notes}\n- What was easiest?\n- What was hardest?\n::: ", "supporting": [ "ttest_files" ], diff --git a/ttest/ttest.qmd b/ttest/ttest.qmd index 831bcbb..0e4de7a 100644 --- a/ttest/ttest.qmd +++ b/ttest/ttest.qmd @@ -1,12 +1,10 @@ --- -title: "Comparing 2 means" +title: "Comparing 2 groups" subtitle: "t-test" author: "Mario E. Bermonti-Pérez, MA, PhD" format: revealjs: incremental: true - chalkboard: true - theme: dark center: true # pdf: # echo: false @@ -75,10 +73,11 @@ write.csv(data_adhd_plan_no_effect, "./data_adhd_plan_2.csv", row.names = FALSE) ## Objectives :::{.center} -- Understand the usefulness of the independent t-test -- Understand the basic concepts of the independent t-test -- Conduct the independent t-test -- Interpret the results of the independent t-test +- Understand the basic logic behind comparing 2 groups +- Identify the usefulness of the independent t-test +- Identify the basic concepts of the independent t-test +- Conduct an independent t-test +- Interpret the results of an independent t-test :::: # t-test @@ -86,7 +85,6 @@ write.csv(data_adhd_plan_no_effect, "./data_adhd_plan_2.csv", row.names = FALSE) What is a t-test? ::: - # Basics ## Usefulness @@ -105,19 +103,19 @@ What is a t-test? Compare 2 groups ::: -## Even more general -:::: {.columns} -::: {.column width="50%"} +## More general ![](./assets/comp_conds.png) + +::: {.notes} +more general, powerful +intervention ::: -::: {.column width="50%"} +## Even more general ![](./assets/comp_means.png) -::: -:::: ::: {.notes} -More general, powerful +most powerful ::: ## GLM form @@ -171,13 +169,19 @@ More general, powerful ggplot(data_adhd_plan_effect, aes(planning)) + geom_histogram() ``` +::: {.notes} +basic question: Are there 2 groups? +::: + ## Group data ```{r} ggplot(data_adhd_plan_effect, aes(x = group, y = planning)) + geom_jitter(width=0.15) ``` ::: {.notes} -what is next? +- how do we compare the groups? +- each point or overall? +- how do we know an overall? ::: ## Estimate means @@ -192,8 +196,9 @@ stat_summary( fill = "red" ) ``` + ::: {.notes} -what is next? +Draw a line! ::: ## Estimate effect (difference) @@ -212,11 +217,17 @@ stat_summary(fun=mean, geom="line", aes(group=1)) ``` ## That is all folks! +## Not exactly... + +## We need to know: + + is the difference real? + + size of the difference? # Conducting and interpreting ## Hands on exercises ### Get ready! + ::: {.notes} Download data from teams ::: @@ -245,8 +256,6 @@ Standardized tests: mean? SD? - H1: Effect ::: {.notes} -- NHST - null = 0 - - H0: Hardheaded ::: @@ -283,8 +292,11 @@ What are these? ::: ## Descriptive stats +- basic +- detailed + ::: {.notes} -- groupby +- group data - detailed: mean, median, SD, Range ::: @@ -304,17 +316,19 @@ Diff in raw scores? ::: ## Standardized + :::: {.columns} ::: {.column width="50%"} +### Indicators - Hedges g - 95% CI ::: ::: {.column width="50%"} ### Interpretation - + small: .2 - 0.49 - + medium: 0.5 - 0.79 - + large: 0.8+ +- small: .2 - 0.49 +- medium: 0.5 - 0.79 +- large: 0.8+ ::: :::: @@ -342,60 +356,21 @@ Diff in raw scores? ## Assumptions - Types of variables (GLM) - Normal distribution -- Homegeinity of variance (Welch's test) +- Homegeinity of variance (Welch's test) - not important - No outliers ::: {.notes} What are assumptions? ::: -## Variable types -:::: {.columns} -::: {.column width="50%"} -![](./assets/glm_form.png) -::: - -::: {.column width="50%"} -### Effect of ADHD on planning -- ADHD - -- Planning -::: -:::: - -::: {.notes} -Do we meet them? -::: - -## Normal distribution -- Histograms -- QQ plots - -::: {.notes} -- QQ plots: quantiles - -- group by condition - -- normal: line -- devs: kurtosis, extremely values (at ends) -- S shape: skewness (negative = high scores, positive = low scores) -::: - -## Outliers -- Boxplot - -::: {.notes} -Do we meet them? -::: - # Closing ## Recap - What analysis? -- What is its use? +- Usefulness? - Predictor type? - Type of outcome? -- Pieces of info needed? +- Pieces of info t-tests provide? - Assumptions? ::: {.notes}