4.5: Quantitative data analysis in operational social science research

.tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="ec94cc220caa0bff13ed8a2bf635a9c2"] { padding: 0px 0px 25px 0px; } .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="ec94cc220caa0bff13ed8a2bf635a9c2"] > .tb-container-inner { max-width: 1080%; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] { grid-template-columns: minmax(0, 0.665fr) minmax(0, 0.335fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="e6c4afbefba40535b2bab546846e6fb6"] { padding: 0px 15px 0px 15px;border-left: 1px solid rgba( 0, 0, 0, 1 ); } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="de9f256dff43aa336bbea121bb835519"] { padding: 25px 25px 0px 25px; }  .tb-field[data-toolset-blocks-field="71b7dc0d84e1946aa37eda50781bf506"] { font-style: italic; }  .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="32d5e7600c84dc6b2b8307f4bc4dd4cf"] { padding: 25px 0px 25px 0px; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .tb-field[data-toolset-blocks-field="32a4261a5f6db7b4e1792858e8526146"] a { color: rgba( 0, 0, 0, 1 );text-decoration: none; } .tb-field[data-toolset-blocks-field="32a4261a5f6db7b4e1792858e8526146"] a:hover { text-decoration: underline; } .tb-field[data-toolset-blocks-field="32a4261a5f6db7b4e1792858e8526146"] a:active { font-weight: bold;color: rgba( 0, 0, 0, 1 ); } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="3034fbe886c11054e95b46b09d3e4112"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="df3b9771e217657ed0f786e74be6bd22"] { padding: 25px 0px 25px 0px;margin-top: 25px;border-top: 1px solid rgba( 0, 0, 0, 1 ); } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="a7e982f86c19762acbbe97e9f0760071"] { padding: 0px;margin-right: 0px; } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}.tb-field[data-toolset-blocks-field="437856ef8c88168a18051613aa5b4a2a"] { font-size: 14px; }   .tb-field[data-toolset-blocks-field="c2a140b1768abfe6224c2bc244734de6"] { font-size: 26px;font-family: Montserrat;font-weight: regular;line-height: 32px; }  .tb-field[data-toolset-blocks-field="542180d48efb5ed02193b749c85b92a9"] { border-bottom: 0px solid rgba( 228, 228, 228, 1 ); }  @media only screen and (max-width: 781px) { .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}  .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="3034fbe886c11054e95b46b09d3e4112"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}     } @media only screen and (max-width: 599px) { .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"] { grid-template-columns: minmax(0, 1fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="a98080c9ad4b08134f60bc81636322c2"]  > .tb-grid-column:nth-of-type(1n+1) { grid-column: 1 } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}  .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="3034fbe886c11054e95b46b09d3e4112"] { display: flex; } .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}     } 

This session gives an overview of how to apply quantitative approaches to analyse data in operational social science research.

Note: You should have completed module 4.3 before beginning this module.

Learning outcomes

Become aware of the inferential statistics that might be helpful to apply to this type of data.
Learn how to analyse data collected with quantitative tools such as Knowledge, Attitudes and Practice / Perceptions (KAP) surveys using descriptive statistics.