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sift:tutorials:perform_statistical_parametric_mapping [2024/11/29 16:45] – wikisysopsift:tutorials:perform_statistical_parametric_mapping [2024/12/17 18:27] (current) – [Analysis] wikisysop
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 We begin by creating a General Linear Model (GLM) of our data. This is a [[sift:statistical_parametric_mapping:using_statistical_parametric_mapping_in_biomechanics|necessary step]] to easily analyze our data and create statistical parametric maps. Since we are hoping to understand the (potential) differences between 2 groups of data, we can model it by crafting the design matrix within the GLM to suit our needs. Sift automatically creates a categorical design matrix, that is, one where each grouping is selected. This will lead to a regression which calculates the means, based on these groupings. The residual matrix can then be easily calculated, and we have obtained our GLM. We begin by creating a General Linear Model (GLM) of our data. This is a [[sift:statistical_parametric_mapping:using_statistical_parametric_mapping_in_biomechanics|necessary step]] to easily analyze our data and create statistical parametric maps. Since we are hoping to understand the (potential) differences between 2 groups of data, we can model it by crafting the design matrix within the GLM to suit our needs. Sift automatically creates a categorical design matrix, that is, one where each grouping is selected. This will lead to a regression which calculates the means, based on these groupings. The residual matrix can then be easily calculated, and we have obtained our GLM.
  
-{{ :GLM_Dialog.png?500}}+{{ :glm_dialog.png?500}}
  
 This process is completed through the following steps: This process is completed through the following steps:
 +
 +=== Original Data ===
  
   - On the SPM tab, within the [[Sift:application:Analyse_Page|Analyse Page]], select the groups and workspaces you want to include in your GLM   - On the SPM tab, within the [[Sift:application:Analyse_Page|Analyse Page]], select the groups and workspaces you want to include in your GLM
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   - Enter the following into the GLM Dialog:   - Enter the following into the GLM Dialog:
     * GLM Name: GLM     * GLM Name: GLM
 +    * Statistical Test: Two-Sample T-Test
     * Group By: Group     * Group By: Group
     * The Groups Selected should be OA and NC     * The Groups Selected should be OA and NC
 +    * Use Workspace Mean: Unchecked
   - Select Create GLM   - Select Create GLM
  
 You will then repeat this process for the registered data: You will then repeat this process for the registered data:
 +
 +=== Registered Data ===
  
   - On the SPM tab, within the [[Sift:application:Analyse_Page|Analyse Page]], select the groups and workspaces you want to include in your GLM   - On the SPM tab, within the [[Sift:application:Analyse_Page|Analyse Page]], select the groups and workspaces you want to include in your GLM
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   - Enter the following into the GLM Dialog:   - Enter the following into the GLM Dialog:
     * GLM Name: GLM_Registered     * GLM Name: GLM_Registered
 +    * Statistical Test: Two-Sample T-Test
     * Group By: Group     * Group By: Group
     * The Groups Selected should be OA_Registered and NC_Registered     * The Groups Selected should be OA_Registered and NC_Registered
 +    * Use Workspace Mean: Unchecked
   - Select Create GLM   - Select Create GLM
  
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 To complete these SPMs: To complete these SPMs:
 +
 +=== Original Data ===
  
   - Select GLM: GLM   - Select GLM: GLM
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     - For unregistered data:     - For unregistered data:
       * SPM Name: SPM       * SPM Name: SPM
-      * Statistic: t Statistic+      * Statistic: Two Sample T-Test
       * Group 1: OA       * Group 1: OA
       * Group 2: NC       * Group 2: NC
 +      * Threshold: 0.05
 +      * Two-Tailed: Checked
 +
 +=== Registered Data ===
  
   - Select GLM: GLM_Registered   - Select GLM: GLM_Registered
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     - For registered data:     - For registered data:
       * SPM Name: SPM_Registered       * SPM Name: SPM_Registered
-      * Statistic: t Statistic+      * Statistic: Two Sample T-Test
       * Group 1: OA_Registered       * Group 1: OA_Registered
       * Group 2: NC_Registered       * Group 2: NC_Registered
 +      * Threshold: 0.05
 +      * Two-Tailed: Checked
  
-We have now calculated two SPMs, which we can easily compare/contrast by selecting the GLM and SPM Results we want in the top bar of the SPM tab. Both SPMs provide us with similar insights: there is a statistical difference between the 2 groups, and it is most prominent during mid-stance(roughly 10%-25% of the gait cycle), as well as mid-swing (roughly 55%-85% of the gait cycle). For the given threshold (defaults to alpha=0.01), we have established that there is a difference between the 2 groups (more precisely, we determined that the null hypothesis that the 2 groups are from the same population sample is false at alpha<0.01).+We have now calculated two SPMs, which we can easily compare/contrast by selecting the GLM and SPM Results we want in the top bar of the SPM tab. Both SPMs provide us with similar insights: there is a statistical difference between the 2 groups, and it is most prominent during mid-stance(roughly 10%-25% of the gait cycle), as well as mid-swing (roughly 55%-85% of the gait cycle). For the given threshold (defaults to alpha=0.01), we have established that there is a difference between the 2 groups (more precisely, we determined that the null hypothesis that the 2 groups are from the same population sample is false at alpha<0.05).
  
  
 {{:SPM_Plot1.png?800}} {{:SPM_Plot1.png?800}}
  
-The difference between both SPMs is most apparent at ~65% of the gait cycle. Here we can see a significantly more pronounced t statistic (~10 vs ~12.5). While both are well above the specified threshold where alpha=0.01, this can show us how curve registration can be useful to correctly align our data, and get more meaningful results from our analysis. +The difference between both SPMs is most apparent at ~65% of the gait cycle. Here we can see a significantly more pronounced t statistic (~10 vs ~12.5). While both are well above the specified threshold where alpha=0.05, this can show us how curve registration can be useful to correctly align our data, and get more meaningful results from our analysis. 
  
 {{:SPM_Plot2.png?800}} {{:SPM_Plot2.png?800}}
sift/tutorials/perform_statistical_parametric_mapping.1732898700.txt.gz · Last modified: 2024/11/29 16:45 by wikisysop