Item Response Warehouse

NOTE: The website may not reflect all data currently available in the IRW. To see the most recent set of available data resources, please look here.

Metadata

Examine sample sizes and other dataset summary statistics.

vars = new Map([["Number responses", "n_responses"],
                ["Number participants", "n_participants"],
                ["Number items", "n_items"],
                ["Responses per participant", "responses_per_participant"],
                ["Responses per item", "responses_per_item"],
                ["Sparsity (#responses/(#ids*#items))", "sparsity"]])

// vars = new Map(Object.entries(cont_vars))
// console.log(Array.from(vars.values())[1])
viewof x_var = Inputs.select(vars, {value: "n_items",        label: "X axis"})
viewof y_var = Inputs.select(vars, {value: "n_participants", label: "Y axis"})

// selector for color variable
default_color = "grey"
color_opts = new Map([["None", null],
                      ["Dichotomous vs. Polytomous", "partition"]])

viewof color_var = Inputs.select(color_opts, {label: "Color"})

plt_color = color_var || default_color

// histogram
viewof metadata_hist = Plot.plot({
      // x: {type: "log"},
      y: {grid: true},
  color: {legend: color_var != null},
  marks: [
    Plot.rectY(metadata_trans, Plot.binX({y: "count"}, {x: x_var, fill: plt_color})),
    Plot.ruleY([0])
  ]
})

// scatter plot
viewof metadata_scatter = Plot.plot({
      x: {type: "log"},
      y: {type: "log"},
   grid: true,
  color: {legend: color_var != null},
  marks: [
    // points
    Plot.dot(metadata_trans, {x: x_var, y: y_var, stroke: plt_color}),
    // tooltips
    Plot.tip(metadata_trans, Plot.pointer({x: x_var, y: y_var, stroke: plt_color, title: "dataset_name"})),
    // regression line
    Plot.linearRegressionY(metadata_trans, {x: x_var, y: y_var, stroke: plt_color})
  ]
})

// display histogram and scatter plots side by side
html`<div style="display: flex;">
 <div style="flex-basis:50%"> ${viewof metadata_hist}    </div>
 <div style="flex-basis:50%"> ${viewof metadata_scatter} </div>
</div>`

Individual datasets

Explore each dataset’s distribution of mean responses by item and subject.

summaries_map = new Map(Object.entries(summaries))
viewof dataset = Inputs.select(summaries_map, {label: 'Dataset'})

// transpose all data for plotting
items = transpose(dataset.items)
subjects = transpose(dataset.subjects)
metadata_trans = transpose(metadata)

Plot = import("https://esm.sh/@observablehq/plot@0.6.13")

// plot items
viewof item_plot = Plot.plot({
      x: {label: "Item mean response"},
      y: {grid: true},
  marks: [
    Plot.rectY(items, Plot.binX({y: "count"}, {x: "mean"})),
    Plot.ruleY([0])
  ]
})

// plot subjects
viewof subj_plot = Plot.plot({
      x: {label: "Subject mean response"},
      y: {grid: true},
  marks: [
    Plot.rectY(subjects, Plot.binX({y: "count"}, {x: "mean"})),
    Plot.ruleY([0])
  ]
})

// display item and subject plots side by side
html`<div style="display: flex;">
 <div style="flex-basis:50%"> ${viewof item_plot} </div>
 <div style="flex-basis:50%"> ${viewof subj_plot} </div>
</div>`