I have a balanced panel data with life satisfaction for each individual over three years, 2014, 2016 and 2018.
Is there any efficient way to examine whether life satisfaction varies over three years 2014, 2016, 2018 for each individual?
My related and similar questions are:
Can I create a matrix or table of values to examine whether life satisfaction varies over three years 2014, 2016, 2018 for each individual? I can use "br" command for my variable of interest but the table is huge.
Is there any command to know how many individuals give the same answer for life satisfaction across three years?
I'm copying my data below.
Look forward for hearing your advice on this issue,
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(pid2014 year) byte life_satisfaction 5 2014 2 5 2018 2 5 2016 3 6 2016 2 6 2018 2 6 2014 3 7 2018 2 7 2016 3 7 2014 3 8 2014 1 8 2018 2 8 2016 2 9 2014 1 9 2018 2 9 2016 2 10 2016 2 10 2018 2 10 2014 2 14 2016 2 14 2018 2 14 2014 3 16 2018 2 16 2016 2 16 2014 3 17 2016 2 17 2018 2 17 2014 3 18 2016 2 18 2014 2 18 2018 2 22 2018 2 22 2016 2 22 2014 4 26 2018 2 26 2014 2 26 2016 2 27 2018 1 27 2016 2 27 2014 2 30 2016 2 30 2018 2 30 2014 3 35 2018 2 35 2014 3 35 2016 3 36 2016 2 36 2018 2 36 2014 3 39 2014 2 39 2018 2 39 2016 2 45 2016 3 45 2018 3 45 2014 4 47 2018 2 47 2016 2 47 2014 3 53 2014 2 53 2018 3 53 2016 3 57 2018 1 57 2016 2 57 2014 3 60 2016 2 60 2018 2 60 2014 2 61 2016 2 61 2018 3 61 2014 3 65 2016 2 65 2018 2 65 2014 3 68 2018 2 68 2014 2 68 2016 3 72 2016 2 72 2018 3 72 2014 4 77 2018 2 77 2016 3 77 2014 4 78 2016 2 78 2018 2 78 2014 3 80 2016 2 80 2014 2 80 2018 2 81 2018 2 81 2014 2 81 2016 2 82 2014 2 82 2018 2 82 2016 2 83 2018 2 83 2014 3 83 2016 3 87 2018 2 87 2016 2 87 2014 2 88 2018 2 88 2016 3 88 2014 3 90 2014 2 90 2018 2 90 2016 2 91 2018 2 91 2014 3 91 2016 3 92 2016 2 92 2018 2 92 2014 3 94 2016 2 94 2014 2 94 2018 2 97 2016 2 97 2014 2 97 2018 2 98 2018 2 98 2016 3 98 2014 4 102 2016 2 102 2014 2 102 2018 3 105 2018 2 105 2016 2 105 2014 3 106 2018 2 106 2016 2 106 2014 3 108 2016 2 108 2018 2 108 2014 3 109 2016 2 109 2014 2 109 2018 2 112 2016 2 112 2014 2 112 2018 3 114 2014 1 114 2016 2 114 2018 2 115 2016 2 115 2014 2 115 2018 2 116 2016 3 116 2018 3 116 2014 4 117 2014 2 117 2018 2 117 2016 3 118 2014 2 118 2018 2 118 2016 3 119 2018 2 119 2016 3 119 2014 4 124 2016 2 124 2018 2 124 2014 3 128 2016 2 128 2014 2 128 2018 2 129 2016 2 129 2018 2 129 2014 2 130 2018 2 130 2016 2 130 2014 2 135 2016 2 135 2018 2 135 2014 3 140 2014 2 140 2016 2 140 2018 2 147 2018 2 147 2016 2 147 2014 2 148 2016 2 148 2018 2 148 2014 2 155 2014 2 155 2018 2 155 2016 2 158 2018 2 158 2014 3 158 2016 3 165 2018 2 165 2016 3 165 2014 3 167 2018 2 167 2016 3 167 2014 3 169 2016 4 169 2018 4 169 2014 4 175 2018 2 175 2016 2 175 2014 3 176 2014 2 176 2018 2 176 2016 2 181 2018 2 181 2016 2 181 2014 3 182 2018 2 182 2016 3 182 2014 3 183 2014 2 183 2018 2 183 2016 2 184 2018 2 184 2016 3 184 2014 3 188 2018 2 188 2016 2 188 2014 3 189 2018 2 189 2016 2 189 2014 4 190 2014 2 190 2018 2 190 2016 3 193 2016 2 193 2018 2 193 2014 3 195 2016 2 195 2014 2 195 2018 2 201 2018 2 201 2016 2 201 2014 3 204 2014 2 204 2018 2 204 2016 2 205 2018 2 205 2014 3 205 2016 3 206 2016 2 206 2018 3 206 2014 3 211 2014 1 211 2016 2 211 2018 2 212 2018 2 212 2016 2 212 2014 3 213 2018 2 213 2016 2 213 2014 3 214 2014 2 214 2016 3 214 2018 3 215 2018 2 215 2016 2 215 2014 3 217 2016 2 217 2014 2 217 2018 2 218 2016 2 218 2014 2 218 2018 2 223 2014 2 223 2016 2 223 2018 2 224 2016 2 224 2014 3 224 2018 3 225 2016 2 225 2014 3 225 2018 3 226 2018 3 226 2014 3 226 2016 3 227 2014 2 227 2016 3 227 2018 3 232 2016 2 232 2018 2 232 2014 3 233 2018 1 233 2016 2 233 2014 3 234 2016 2 234 2014 2 234 2018 3 237 2014 3 237 2018 3 237 2016 4 240 2018 3 240 2016 3 240 2014 3 241 2018 2 241 2016 3 241 2014 3 242 2016 1 242 2018 2 242 2014 3 244 2018 2 244 2016 2 244 2014 3 246 2018 2 246 2016 2 246 2014 2 251 2018 2 251 2016 2 251 2014 2 252 2014 2 252 2016 2 252 2018 3 253 2018 2 253 2016 2 253 2014 3 255 2016 2 255 2018 2 255 2014 2 256 2016 2 256 2014 2 256 2018 2 259 2018 2 259 2014 2 259 2016 2 263 2016 2 263 2018 2 263 2014 3 270 2018 2 270 2016 2 270 2014 2 273 2018 2 273 2016 2 273 2014 3 274 2018 1 274 2016 2 274 2014 2 278 2018 1 278 2016 2 278 2014 2 279 2014 2 279 2016 2 279 2018 2 280 2018 2 280 2016 2 280 2014 3 284 2016 2 284 2014 2 284 2018 2 285 2014 2 285 2016 2 285 2018 2 287 2018 2 287 2016 2 287 2014 3 289 2014 2 289 2018 2 289 2016 2 292 2014 2 292 2016 2 292 2018 2 295 2018 2 295 2016 3 295 2014 3 296 2016 2 296 2018 2 296 2014 2 297 2018 1 297 2016 2 297 2014 3 302 2018 2 302 2014 2 302 2016 2 305 2014 2 305 2018 2 305 2016 2 309 2018 2 309 2016 2 309 2014 4 310 2018 2 310 2016 3 310 2014 4 311 2014 2 311 2018 2 311 2016 2 316 2016 2 316 2018 2 316 2014 4 318 2016 2 318 2014 2 318 2018 3 319 2018 2 319 2014 3 319 2016 3 322 2018 2 322 2016 2 322 2014 4 327 2018 2 327 2014 2 327 2016 3 331 2018 2 331 2016 2 331 2014 2 332 2014 2 332 2016 2 332 2018 3 334 2014 1 334 2016 2 334 2018 3 336 2014 1 336 2018 2 336 2016 2 338 2014 1 338 2018 2 338 2016 2 340 2014 1 340 2018 2 340 2016 3 341 2014 2 341 2018 2 341 2016 3 342 2014 2 342 2016 2 342 2018 2 344 2018 1 344 2014 1 344 2016 2 345 2014 2 345 2018 2 345 2016 3 346 2016 1 346 2018 2 346 2014 2 351 2016 1 351 2014 2 351 2018 2 354 2014 2 354 2016 2 354 2018 3 355 2016 2 355 2018 3 355 2014 3 356 2014 2 356 2018 2 356 2016 3 359 2014 1 359 2018 2 359 2016 2 364 2018 2 364 2016 2 364 2014 2 366 2016 2 366 2014 2 366 2018 2 367 2014 1 367 2016 2 367 2018 2 368 2016 2 368 2018 2 368 2014 2 369 2014 2 369 2018 2 369 2016 2 371 2014 2 371 2018 2 371 2016 2 372 2016 2 372 2014 3 372 2018 3 377 2014 1 377 2018 2 377 2016 2 380 2014 3 380 2016 3 380 2018 4 381 2016 2 381 2018 2 381 2014 3 382 2018 2 382 2014 2 382 2016 2 383 2018 2 383 2014 2 383 2016 2 384 2018 2 384 2014 2 384 2016 2 387 2016 2 387 2014 2 387 2018 2 388 2014 2 388 2016 3 388 2018 3 392 2014 2 392 2018 2 392 2016 3 394 2018 2 394 2016 2 394 2014 3 395 2014 2 395 2016 2 395 2018 3 396 2018 2 396 2016 2 396 2014 3 397 2018 2 397 2016 2 397 2014 2 400 2016 2 400 2018 2 400 2014 4 401 2018 2 401 2016 2 401 2014 2 end label values life_satisfaction P56Q10 label def P56Q10 1 "Very satisfied", modify label def P56Q10 2 "Satisfied", modify label def P56Q10 3 "Not satisfied", modify label def P56Q10 4 "Disappointed", modify
0 Response to how to check whether a variable varies over time for each individual in a panel data
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