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Updated before introduction to STATA
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| // Setting Working directory | ||
| cd "C:\Users\slupp\OneDrive\Skrivebord\NTNU\Mehmet\PSY8003\Dummy_moderation_and_mediation" | ||
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| //// SECTION 1: DUMMY VARIABLE REGRESSION //// | ||
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| // Loading Dataset | ||
| use flats.dta | ||
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| // Labeling variables (this is allready done) | ||
| // Creating variable with labels | ||
| label define labels_location 1 "centre" 2 "south" 3 "west" 4 "east" | ||
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| // Applying Labels to location | ||
| label values location labels_location | ||
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| // Checking that it is done correctly | ||
| codebook location | ||
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| // Regression flat_price location | ||
| // wrong way | ||
| reg flat_price location | ||
| // Right way | ||
| reg flat_price i.location | ||
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| // This works even if our variable lacks labels | ||
| // removing labels from location | ||
| label values location | ||
| reg flat_price i.location | ||
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| // Changing Reference Group | ||
| reg flat_price ib2.location | ||
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| // Creating a boxplot | ||
| graph box flat_price, over(location) | ||
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| // Regression flat_price location floor_size | ||
| // Running the regression | ||
| reg flat_price i.location floor_size | ||
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| // Visualizing the regression | ||
| reg flat_price i.location floor_size // You do not need to run this again, unless it is not the last command you've run | ||
| margins , at(floor_size = (20(50)220) location = (1(1)4)) | ||
| marginsplot | ||
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| //// SECTION 2: INTERACTION //// | ||
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| // Interaction between two continuous variables | ||
| // Running a regression between floor_size interaction with year_built | ||
| reg flat_price c.floor_size##c.year_built | ||
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| // Visualizing the interaction | ||
| reg flat_price c.floor_size##c.year_built | ||
| margins , at (floor_size = (20(200)220) year_built = (1930(20)2010)) | ||
| marginsplot | ||
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| // Interaction between a continuous and a categorical variable | ||
| // Running a regression with location and floor size (with interaction) | ||
| reg flat_price i.location##c.floor_size | ||
|
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||
| //Visualizing the regression | ||
| reg flat_price i.location##c.floor_size | ||
| margins , at(floor_size = (20(200)220) location = (1(1)4)) | ||
| marginsplot | ||
|
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| //// SECTION 3: MEDIATION //// | ||
|
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| // Installing medsem package (i have commented it out, since i already have installed both packages) | ||
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| // Install github package (allows you to download packages from github) | ||
| *net install github, from("https://haghish.github.io/github/") | ||
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| //medsem (using github package) | ||
| *github install mmoglu/medsem | ||
|
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||
| // Loading dataset | ||
| use workout.dta, clear // the clear option clears the dataset which is already loaded | ||
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| // Regression approach dependent | ||
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| // Dependent = calories | ||
| // Independent = attract | ||
| // Mediating = appear | ||
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| // Step 1 assessing direct effect between independent and dependent | ||
| reg calories attract | ||
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| // Step 2 assessing direct effect between independent and mediating | ||
| reg appear attract | ||
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| // Step 3 assessing direct effect between mediating and dependent | ||
| reg calories appear | ||
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| // Step 4 assessing direct effect independent and dependent while controlling for mediating | ||
| reg calories attract appear | ||
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||
| // SEM approach | ||
|
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||
| // First creating our model as a SEM | ||
| sem (calories <- attract appear) (appear <- attract) | ||
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||
|
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||
| // assessing the same model using the medsem package (this has to be run after the previous command) | ||
| medsem, indep(attract) med(appear) dep(calories) | ||
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||
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| //// APPENDIX: USING PLS-SEM //// | ||
|
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||
| //Installing plsem package (commented out because i have installed it already) | ||
|
|
||
| *net install github, from("https://haghish.github.io/github/") | ||
| *github install sergioventurini/plssem | ||
|
|
||
|
|
||
| // The syntax of plssem reflects the measurement and structural part of a PLS-SEM model, and | ||
| // accordingly requires the user to specify both of these parts simultaneously. Since a full PLSSEM model would include a structural model, i.e., the relationship between latent variables | ||
| // (LV), we need to have at least two latent variables specified in the measurement part. Each | ||
| // latent variable will be defined by a block of indicators (say, indblock). For example, if we | ||
| // have two latent variables in our PLS-SEM model, the plssem syntax requires to specify the | ||
| // measurement part by typing | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2). | ||
| // Clearly, one can specify as many LVs as it is needed in the model. The specification of | ||
| // reflective measures in the measurement model require to use the greater-than sign between | ||
| // a latent variable and its associated indicators (e.g., LV1 > indblock1), while the less-than | ||
| // sign needs to be provided when one needs to include latent variables measured in a formative | ||
| // way (e.g., LV1 < indblock1). | ||
| // To specify the structural part11, one needs to provide the endogenous/dependent latent variable (say, LV2) first followed by the exogenous latent variables (say, LV1) by typing | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2), structural(LV2 LV1). | ||
| // One can specify further structural relationships following the same approach. For example, | ||
| // suppose one has two further latent variables in the model, LV3 and LV4, still measured in a | ||
| // reflective way, with LV4 endogenous and LV3 exogenous. Then, the syntax for the structural | ||
| // part should be | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2) (LV3 > indblock3) /// | ||
| // (LV4 > indblock4), structural(LV2 LV1, LV4 LV3). | ||
| // | ||
| // | ||
| // First two lines = measuremnt model (in this case it is formative), last line = structural model | ||
| plssem (Appear < appear) (Attract < attract) (Calories < calories), /// | ||
| structural(Calories Appear Attract, Appear Attract) | ||
|
|
||
| estat mediate, indep(Attract) dep(Calories) med(Appear) | ||
|
|
||
| // Setting Working directory | ||
| cd "C:\Users\slupp\OneDrive\Skrivebord\NTNU\Mehmet\PSY8003\Dummy_moderation_and_mediation" | ||
|
|
||
|
|
||
| //// SECTION 1: DUMMY VARIABLE REGRESSION //// | ||
|
|
||
| // Loading Dataset | ||
| use flats.dta | ||
|
|
||
|
|
||
| // Labeling variables (this is allready done) | ||
| // Creating variable with labels | ||
| label define labels_location 1 "centre" 2 "south" 3 "west" 4 "east" | ||
|
|
||
| // Applying Labels to location | ||
| label values location labels_location | ||
|
|
||
| // Checking that it is done correctly | ||
| codebook location | ||
|
|
||
| // Regression flat_price location | ||
| // wrong way | ||
| reg flat_price location | ||
| // Right way | ||
| reg flat_price i.location | ||
|
|
||
| // This works even if our variable lacks labels | ||
| // removing labels from location | ||
| label values location | ||
| reg flat_price i.location | ||
|
|
||
| // Changing Reference Group | ||
| reg flat_price ib2.location | ||
|
|
||
| // Creating a boxplot | ||
| graph box flat_price, over(location) | ||
|
|
||
| // Regression flat_price location floor_size | ||
| // Running the regression | ||
| reg flat_price i.location floor_size | ||
|
|
||
| // Visualizing the regression | ||
| reg flat_price i.location floor_size // You do not need to run this again, unless it is not the last command you've run | ||
| margins , at(floor_size = (20(50)220) location = (1(1)4)) | ||
| marginsplot | ||
|
|
||
|
|
||
| //// SECTION 2: INTERACTION //// | ||
|
|
||
| // Interaction between two continuous variables | ||
| // Running a regression between floor_size interaction with year_built | ||
| reg flat_price c.floor_size##c.year_built | ||
|
|
||
| // Visualizing the interaction | ||
| reg flat_price c.floor_size##c.year_built | ||
| margins , at (floor_size = (20(200)220) year_built = (1930(20)2010)) | ||
| marginsplot | ||
|
|
||
| // Interaction between a continuous and a categorical variable | ||
| // Running a regression with location and floor size (with interaction) | ||
| reg flat_price i.location##c.floor_size | ||
|
|
||
| //Visualizing the regression | ||
| reg flat_price i.location##c.floor_size | ||
| margins , at(floor_size = (20(200)220) location = (1(1)4)) | ||
| marginsplot | ||
|
|
||
| //// SECTION 3: MEDIATION //// | ||
|
|
||
| // Installing medsem package (i have commented it out, since i already have installed both packages) | ||
|
|
||
| // Install github package (allows you to download packages from github) | ||
| *net install github, from("https://haghish.github.io/github/") | ||
|
|
||
| //medsem (using github package) | ||
| *github install mmoglu/medsem | ||
|
|
||
| // Loading dataset | ||
| use workout.dta, clear // the clear option clears the dataset which is already loaded | ||
|
|
||
| // Regression approach dependent | ||
|
|
||
| // Dependent = calories | ||
| // Independent = attract | ||
| // Mediating = appear | ||
|
|
||
| // Step 1 assessing direct effect between independent and dependent | ||
| reg calories attract | ||
|
|
||
| // Step 2 assessing direct effect between independent and mediating | ||
| reg appear attract | ||
|
|
||
| // Step 3 assessing direct effect between mediating and dependent | ||
| reg calories appear | ||
|
|
||
| // Step 4 assessing direct effect independent and dependent while controlling for mediating | ||
| reg calories attract appear | ||
|
|
||
| // SEM approach | ||
|
|
||
| // First creating our model as a SEM | ||
| sem (calories <- attract appear) (appear <- attract) | ||
|
|
||
|
|
||
| // assessing the same model using the medsem package (this has to be run after the previous command) | ||
| medsem, indep(attract) med(appear) dep(calories) | ||
|
|
||
|
|
||
| //// APPENDIX: USING PLS-SEM //// | ||
|
|
||
| //Installing plsem package (commented out because i have installed it already) | ||
|
|
||
| *net install github, from("https://haghish.github.io/github/") | ||
| *github install sergioventurini/plssem | ||
|
|
||
|
|
||
| // The syntax of plssem reflects the measurement and structural part of a PLS-SEM model, and | ||
| // accordingly requires the user to specify both of these parts simultaneously. Since a full PLSSEM model would include a structural model, i.e., the relationship between latent variables | ||
| // (LV), we need to have at least two latent variables specified in the measurement part. Each | ||
| // latent variable will be defined by a block of indicators (say, indblock). For example, if we | ||
| // have two latent variables in our PLS-SEM model, the plssem syntax requires to specify the | ||
| // measurement part by typing | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2). | ||
| // Clearly, one can specify as many LVs as it is needed in the model. The specification of | ||
| // reflective measures in the measurement model require to use the greater-than sign between | ||
| // a latent variable and its associated indicators (e.g., LV1 > indblock1), while the less-than | ||
| // sign needs to be provided when one needs to include latent variables measured in a formative | ||
| // way (e.g., LV1 < indblock1). | ||
| // To specify the structural part11, one needs to provide the endogenous/dependent latent variable (say, LV2) first followed by the exogenous latent variables (say, LV1) by typing | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2), structural(LV2 LV1). | ||
| // One can specify further structural relationships following the same approach. For example, | ||
| // suppose one has two further latent variables in the model, LV3 and LV4, still measured in a | ||
| // reflective way, with LV4 endogenous and LV3 exogenous. Then, the syntax for the structural | ||
| // part should be | ||
| // plssem (LV1 > indblock1) (LV2 > indblock2) (LV3 > indblock3) /// | ||
| // (LV4 > indblock4), structural(LV2 LV1, LV4 LV3). | ||
| // | ||
| // | ||
| // First two lines = measuremnt model (in this case it is formative), last line = structural model | ||
| plssem (Appear < appear) (Attract < attract) (Calories < calories), /// | ||
| structural(Calories Appear Attract, Appear Attract) | ||
|
|
||
| estat mediate, indep(Attract) dep(Calories) med(Appear) |
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