PSY290 HW - 5
Javier Hernandez
2026-03-09
Question 1
Fisman, Iyengar, Kamenica, and Simonson (2006) conducted a
large-scale experiment on dating behavior. They placed participants in a
speed-dating context, where each was randomly matched with several
potential partners (between 5 and 20) and conversed for four minutes.
After each meeting, participants rated how much they liked their
speed-dating partners as well as the partners’ attractiveness,
sincerity, intelligence, fun, and ambition. We focus on the ratings of
perceived physical attractiveness (other_attr), intelligence
(other_intel), and their relationship to overall likability
(other_like). All ratings were provided on a 10-point scale, ranging
from 1 (“awful”) to 10 (“great”).
a. Remove rows containing missing values (NA) and use the
complete cases for the remaining two questions.
speeddate_clean <- speeddate[complete.cases(speeddate), ]
speeddate_clean## X other_like other_attr other_intel
## 1 1 7.0 8.0 9.0
## 2 2 8.0 8.0 4.0
## 3 3 8.0 8.0 8.0
## 4 4 7.0 7.0 7.0
## 5 5 7.0 8.0 7.0
## 6 6 7.0 6.0 10.0
## 7 7 1.0 3.0 8.0
## 8 8 6.0 6.0 10.0
## 9 9 6.0 5.0 8.0
## 10 10 8.0 6.0 9.0
## 11 11 9.0 9.0 9.0
## 12 12 1.0 3.0 8.0
## 13 13 7.0 7.0 9.0
## 14 14 6.5 6.5 7.5
## 15 15 7.0 7.0 8.0
## 16 16 7.0 7.0 8.0
## 17 17 1.0 4.0 6.0
## 19 19 7.0 6.5 8.0
## 20 20 8.0 8.0 8.0
## 21 21 8.0 6.0 9.0
## 22 22 9.0 7.0 10.0
## 24 24 6.5 5.0 6.0
## 25 25 4.0 3.0 8.0
## 26 26 8.0 7.0 7.0
## 27 27 7.0 7.0 7.0
## 28 28 9.0 8.0 7.0
## 30 30 8.0 8.0 8.0
## 31 31 8.0 8.0 8.0
## 32 32 6.0 7.0 8.0
## 33 33 7.0 7.0 9.0
## 34 34 7.0 8.0 8.0
## 35 35 10.0 10.0 10.0
## 36 36 5.0 5.0 9.0
## 37 37 7.0 6.0 5.0
## 38 38 6.0 7.0 8.0
## 40 40 5.0 3.0 8.0
## 41 41 9.0 8.0 9.0
## 42 42 7.0 8.0 7.0
## 43 43 8.0 8.0 9.0
## 44 44 8.0 8.0 8.0
## 45 45 8.0 8.0 8.0
## 46 46 7.0 5.0 8.0
## 47 47 5.0 5.0 5.0
## 48 48 5.0 6.0 6.0
## 50 50 8.0 6.0 8.0
## 51 51 9.0 10.0 8.0
## 52 52 7.0 7.0 7.0
## 53 53 6.0 6.0 8.0
## 54 54 7.0 8.0 8.0
## 55 55 5.0 6.0 7.0
## 56 56 6.0 7.0 7.0
## 57 57 6.0 8.0 9.0
## 58 58 6.0 8.0 9.0
## 59 59 5.0 4.0 8.0
## 60 60 6.0 7.0 7.0
## 61 61 8.0 8.0 7.0
## 62 62 8.0 8.0 9.0
## 63 63 7.0 7.0 7.0
## 64 64 7.0 7.0 9.0
## 65 65 6.0 8.0 6.0
## 66 66 7.0 8.0 7.0
## 67 67 7.0 6.0 9.0
## 68 68 5.0 5.0 7.0
## 69 69 4.0 4.0 7.0
## 70 70 4.0 5.0 8.0
## 71 71 3.0 3.0 6.0
## 72 72 5.0 5.0 7.0
## 73 73 6.0 4.0 9.0
## 74 74 6.0 6.0 9.0
## 75 75 5.0 3.0 7.0
## 76 76 3.0 3.0 6.0
## 77 77 6.0 5.0 5.0
## 78 78 7.0 7.0 9.0
## 79 79 4.0 5.0 7.0
## 80 80 6.0 5.0 9.0
## 82 82 3.0 4.0 5.0
## 83 83 9.0 9.0 9.0
## 84 84 9.0 8.0 8.0
## 85 85 7.0 6.0 9.0
## 86 86 5.0 6.0 10.0
## 87 87 8.0 8.0 7.0
## 88 88 6.0 8.0 8.0
## 89 89 7.0 7.0 9.0
## 90 90 7.0 7.0 10.0
## 91 91 8.0 7.0 9.0
## 92 92 3.0 4.0 5.0
## 93 93 7.0 8.0 8.0
## 94 94 8.0 8.0 8.0
## 96 96 8.0 8.0 9.0
## 97 97 4.0 4.0 8.0
## 98 98 4.0 5.0 6.0
## 99 99 9.0 9.0 9.0
## 100 100 8.0 8.0 8.0
## 101 101 5.0 4.0 9.0
## 102 102 4.0 6.0 10.0
## 103 103 6.0 5.0 7.0
## 104 104 5.0 6.0 6.0
## 105 105 7.0 4.0 9.0
## 106 106 6.0 6.0 9.0
## 107 107 5.0 5.0 7.0
## 108 108 3.0 2.0 6.0
## 109 109 7.0 7.0 7.0
## 110 110 8.0 7.0 9.0
## 111 111 6.0 6.0 6.0
## 112 112 6.0 6.0 9.0
## 113 113 3.0 2.0 9.0
## 114 114 4.0 4.0 6.0
## 115 115 9.0 8.0 8.0
## 116 116 5.0 6.0 8.0
## 118 118 4.0 5.0 7.0
## 119 119 4.0 4.0 8.0
## 120 120 6.0 5.0 7.0
## 121 121 10.0 9.0 10.0
## 122 122 7.0 8.0 9.0
## 123 123 7.0 7.0 7.0
## 124 124 3.0 3.0 8.0
## 125 125 4.0 4.0 7.0
## 126 126 7.0 7.0 9.0
## 127 127 5.0 5.0 7.0
## 128 128 8.0 6.0 9.0
## 129 129 1.0 1.0 5.0
## 130 130 4.0 5.0 5.0
## 131 131 8.0 9.0 7.0
## 132 132 5.0 6.0 6.0
## 133 133 6.0 8.0 9.0
## 134 134 4.0 7.0 9.0
## 135 135 5.0 5.0 7.0
## 136 136 6.0 7.0 7.0
## 137 137 6.0 9.0 6.0
## 138 138 8.0 7.0 8.0
## 139 139 8.0 8.0 8.0
## 140 140 5.0 3.0 5.0
## 141 141 5.0 4.0 8.0
## 142 142 9.0 10.0 8.0
## 143 143 6.0 7.0 7.0
## 144 144 9.0 5.0 7.0
## 146 146 4.0 5.0 5.0
## 147 147 8.0 9.0 8.0
## 148 148 6.0 7.0 6.0
## 149 149 7.0 7.0 8.0
## 150 150 5.0 6.0 8.0
## 151 151 6.0 6.0 7.0
## 152 152 7.0 8.0 8.0
## 153 153 6.0 7.0 7.0
## 154 154 8.0 8.0 9.0
## 155 155 8.0 8.0 8.0
## 156 156 5.0 6.0 6.0
## 157 157 7.0 7.0 8.0
## 158 158 9.0 9.0 9.0
## 159 159 6.0 6.0 7.0
## 160 160 8.0 7.0 7.0
## 161 161 6.0 7.0 6.0
## 162 162 5.0 5.0 5.0
## 163 163 8.0 9.0 9.0
## 164 164 7.0 8.0 7.0
## 165 165 5.0 6.0 9.0
## 166 166 7.0 7.0 7.0
## 167 167 6.0 8.0 6.0
## 168 168 8.0 8.0 8.0
## 169 169 5.0 6.0 6.0
## 170 170 7.0 9.0 9.0
## 171 171 7.0 7.0 7.0
## 172 172 7.0 6.0 6.0
## 173 173 6.0 5.0 6.0
## 174 174 9.0 9.0 9.0
## 175 175 6.0 7.0 7.0
## 176 176 9.0 8.0 8.0
## 177 177 6.0 6.0 7.0
## 178 178 8.0 8.0 8.0
## 179 179 9.0 10.0 9.0
## 180 180 8.0 8.0 9.0
## 181 181 7.0 7.0 9.0
## 182 182 6.0 8.0 10.0
## 183 183 5.0 6.0 8.0
## 184 184 8.0 9.0 9.0
## 185 185 9.0 9.0 9.0
## 186 186 8.0 8.0 9.0
## 187 187 7.0 5.0 9.0
## 188 188 6.0 7.0 6.0
## 189 189 7.0 8.0 7.0
## 190 190 8.0 7.0 9.0
## 191 191 6.0 6.0 7.0
## 192 192 7.0 7.0 9.0
## 193 193 3.0 4.0 8.0
## 194 194 6.0 7.0 7.0
## 195 195 9.0 10.0 8.0
## 196 196 7.0 8.0 7.0
## 198 198 8.0 10.0 8.0
## 199 199 9.0 8.0 7.0
## 200 200 8.0 8.0 8.0
## 201 201 7.0 9.0 7.0
## 202 202 6.0 10.0 8.0
## 204 204 7.0 8.0 5.0
## 205 205 8.0 9.0 7.0
## 206 206 9.0 9.0 9.0
## 207 207 7.0 8.0 7.0
## 208 208 8.0 9.0 8.0
## 209 209 6.0 8.0 8.0
## 210 210 8.0 9.0 7.0
## 211 211 8.0 9.0 8.0
## 212 212 5.0 7.0 7.0
## 213 213 5.0 7.0 10.0
## 214 214 6.0 7.0 9.0
## 215 215 4.0 6.0 7.0
## 216 216 5.0 6.0 8.0
## 217 217 7.0 8.0 9.0
## 218 218 7.0 8.0 10.0
## 220 220 7.0 7.0 8.0
## 221 221 7.0 7.0 7.0
## 222 222 8.0 10.0 8.0
## 223 223 4.0 5.0 6.0
## 224 224 7.0 7.0 8.0
## 225 225 6.0 7.0 8.0
## 226 226 5.0 5.0 5.0
## 227 227 7.0 7.0 8.0
## 228 228 7.0 7.0 7.0
## 229 229 4.0 7.0 8.0
## 230 230 5.0 7.0 6.0
## 231 231 4.0 5.0 6.0
## 232 232 8.0 8.0 8.0
## 233 233 7.0 7.0 8.0
## 234 234 5.0 7.0 8.0
## 235 235 5.0 6.0 7.0
## 236 236 4.0 4.0 6.0
## 237 237 6.0 7.0 7.0
## 238 238 8.0 9.0 8.0
## 239 239 6.0 5.0 7.0
## 240 240 6.0 6.0 8.0
## 241 241 4.0 4.0 8.0
## 242 242 5.0 5.0 5.0
## 243 243 8.0 9.0 7.0
## 244 244 7.0 7.0 7.0
## 245 245 7.0 8.0 8.0
## 246 246 8.0 9.0 8.0
## 247 247 4.0 7.0 7.0
## 248 248 7.0 8.0 8.0
## 249 249 6.0 8.0 7.0
## 250 250 5.0 10.0 8.0
## 251 251 7.0 7.0 7.0
## 252 252 6.0 5.0 7.0
## 253 253 9.0 8.0 8.0
## 254 254 10.0 10.0 9.0
## 255 255 6.0 6.0 7.0
## 256 256 8.0 9.0 8.0
## 257 257 6.0 7.0 8.0
## 258 258 7.0 6.0 6.0
## 259 259 9.0 10.0 9.0
## 260 260 7.0 7.0 8.0
## 261 261 6.0 7.0 8.0
## 262 262 7.0 7.0 8.0
## 263 263 4.0 6.0 3.0
## 264 264 7.0 8.0 6.0
## 265 265 7.0 5.0 8.0
## 266 266 6.0 9.0 8.0
## 267 267 7.0 7.0 7.0
## 268 268 6.0 6.0 5.0
## 269 269 7.0 7.0 7.0
## 270 270 8.0 9.0 8.0
## 271 271 6.0 6.0 7.0
## 272 272 8.0 10.0 8.0
## 273 273 5.0 5.0 6.0
## 274 274 6.0 6.0 7.0
## 275 275 8.0 9.0 8.0
## 276 276 5.0 6.0 7.0
## 277 277 8.0 8.0 8.0
## 278 278 5.0 7.0 10.0
## 279 279 6.0 5.0 7.0
## 280 280 8.0 8.0 9.0
## 281 281 7.0 8.0 10.0
## 282 282 6.0 7.0 7.0
## 283 283 7.0 7.0 7.0
## 284 284 6.0 7.0 6.0
## 285 285 6.0 7.0 8.0
## 286 286 8.0 8.0 10.0
## 287 287 6.0 6.0 8.0
## 288 288 6.0 6.0 9.0
## 289 289 7.0 6.0 7.0
## 290 290 6.0 7.0 7.0
## 291 291 7.0 9.0 8.0
## 292 292 5.0 6.0 6.0
## 293 293 8.0 10.0 8.0
## 294 294 6.0 8.0 8.0
## 295 295 5.0 5.0 6.0
## 296 296 5.0 7.0 6.0
## 297 297 8.0 8.0 8.0
## 298 298 7.0 9.0 8.0
## 299 299 8.0 8.0 8.0
## 300 300 6.0 6.0 6.0
## 301 301 9.0 6.0 8.0
## 302 302 9.0 10.0 8.0
## 303 303 5.0 6.0 6.0
## 304 304 9.0 9.0 9.0
## 305 305 8.0 8.0 7.0
## 306 306 7.0 7.0 8.0
## 307 307 4.0 4.0 6.0
## 308 308 5.0 5.0 8.0
## 309 309 4.0 4.0 7.0
## 310 310 8.0 7.0 10.0
## 311 311 4.0 4.0 8.0
## 312 312 5.0 5.0 8.0
## 313 313 1.0 4.0 8.0
## 314 314 5.0 6.0 7.0
## 315 315 5.0 5.0 8.0
## 316 316 2.0 4.0 5.0
## 317 317 6.0 6.0 7.0
## 318 318 5.0 6.0 8.0
## 319 319 5.0 5.0 10.0
## 320 320 2.0 2.0 7.0
## 321 321 3.0 4.0 10.0
## 322 322 1.0 2.0 3.0
## 323 323 9.0 5.0 9.0
## 324 324 5.0 4.0 8.0
## 325 325 4.0 4.0 8.0
## 326 326 8.0 7.0 10.0
## 327 327 7.0 5.0 10.0
## 328 328 5.0 5.0 8.0
## 329 329 2.0 3.0 8.0
## 330 330 4.0 4.0 8.0
## 331 331 7.0 5.0 10.0
## 332 332 6.0 3.0 7.0
## 333 333 5.0 4.0 5.0
## 334 334 5.0 5.0 6.0
## 335 335 7.0 6.0 9.0
## 336 336 1.0 1.0 6.0
## 337 337 3.0 3.0 10.0
## 338 338 3.0 2.0 6.0
## 339 339 8.0 8.0 8.0
## 340 340 5.0 5.0 7.0
## 341 341 7.0 7.0 8.0
## 342 342 7.0 8.0 7.0
## 343 343 7.0 8.0 7.0
## 344 344 8.0 8.0 7.0
## 345 345 5.0 7.0 7.0
## 346 346 8.0 9.0 8.0
## 347 347 7.0 9.0 7.0
## 348 348 6.0 8.0 8.0
## 349 349 6.0 6.0 7.0
## 350 350 5.0 5.0 6.0
## 351 351 8.0 9.0 9.0
## 352 352 6.0 7.0 6.0
## 353 353 8.0 7.0 10.0
## 354 354 7.0 7.0 7.0
## 355 355 9.0 10.0 9.0
## 356 356 8.0 5.0 8.0
## 357 357 9.0 8.0 8.0
## 358 358 9.0 10.0 9.0
## 359 359 6.0 6.0 8.0
## 360 360 6.0 7.0 7.0
## 361 361 4.0 5.0 8.0
## 362 362 7.0 8.0 8.0
## 363 363 7.0 8.0 8.0
## 364 364 9.0 9.0 8.0
## 365 365 8.0 8.0 8.0
## 366 366 5.0 7.0 7.0
## 367 367 8.0 10.0 9.0
## 368 368 7.0 8.0 7.0
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## 370 370 6.0 7.0 6.0
## 371 371 8.0 8.0 9.0
## 372 372 7.0 6.0 9.0
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## 374 374 8.0 9.0 9.0
## 375 375 5.0 5.0 10.0
## 376 376 5.0 5.0 6.0
## 377 377 8.0 6.0 8.0
## 378 378 6.0 4.0 7.0
## 379 379 7.0 6.0 7.0
## 380 380 8.0 9.0 7.0
## 381 381 7.0 5.0 8.0
## 382 382 5.0 5.0 7.0
## 383 383 5.0 5.0 8.0
## 384 384 3.0 2.0 6.0
## 385 385 4.0 7.0 10.0
## 386 386 1.0 3.0 7.0
## 387 387 8.0 6.0 8.0
## 388 388 8.0 9.0 9.0
## 389 389 7.0 6.0 7.0
## 390 390 9.0 9.0 8.0
## 391 391 6.0 6.0 8.0
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## 393 393 8.0 7.0 9.0
## 394 394 5.0 5.0 6.0
## 395 395 7.0 7.0 8.0
## 396 396 7.5 7.0 7.0
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## 400 400 6.0 6.0 6.0
## 401 401 7.0 6.0 10.0
## 402 402 3.0 5.0 6.0
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## 409 409 4.0 4.0 7.0
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## 1114 1114 7.0 6.0 7.0
## 1115 1115 7.0 6.0 8.0
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## 1117 1117 6.0 6.0 6.0
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## 1483 1483 5.0 6.0 9.0
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## 1485 1485 3.0 2.0 3.0
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## 1488 1488 2.0 2.0 4.0
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## 1491 1491 5.0 5.0 8.0
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## 1494 1494 3.0 5.0 6.0
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## 1496 1496 4.0 4.0 6.0
## 1497 1497 9.0 9.0 9.0
## 1498 1498 6.0 5.0 7.0
## 1500 1500 8.0 6.0 8.0
## 1501 1501 5.0 6.0 6.0
## 1502 1502 6.0 5.0 8.0
## 1503 1503 7.0 8.0 9.0
## 1504 1504 9.0 6.0 9.0
## 1505 1505 4.0 3.0 7.0
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## 1507 1507 5.0 5.0 5.0
## 1508 1508 3.0 3.0 3.0
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## 1510 1510 6.0 8.0 8.0
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## 1512 1512 4.0 5.0 8.0
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## 1514 1514 2.0 4.0 5.0
## 1515 1515 7.0 7.0 7.0
## 1516 1516 5.0 4.0 4.0
## 1517 1517 6.0 5.0 9.0
## 1519 1519 2.0 5.0 3.0
## 1520 1520 7.0 7.0 9.0
## 1521 1521 8.0 7.0 7.0
## 1522 1522 7.0 6.0 10.0
## 1523 1523 5.0 5.0 9.0
## 1524 1524 5.0 5.0 7.0
## 1525 1525 3.0 3.0 4.0
## 1526 1526 10.0 10.0 10.0
## 1527 1527 2.0 4.0 4.0
## 1528 1528 2.0 2.0 7.0
## 1529 1529 7.0 5.0 7.0
## 1530 1530 5.0 4.0 8.0
## 1531 1531 7.0 7.0 9.0
## 1532 1532 4.0 5.0 8.0
## 1533 1533 6.0 6.0 8.0
## 1534 1534 6.0 6.0 6.0
## 1535 1535 6.0 4.0 7.0
## 1536 1536 3.0 4.0 6.0
## 1537 1537 6.0 5.0 9.0
## 1539 1539 4.0 3.0 9.0
## 1540 1540 7.0 5.0 10.0
## 1541 1541 6.0 6.0 7.0
## 1542 1542 7.0 6.0 7.0
## 1543 1543 7.0 6.0 9.0
## 1544 1544 7.0 6.0 10.0
## 1545 1545 4.0 2.0 7.0
## 1546 1546 5.0 7.0 7.0
## 1547 1547 5.0 4.0 6.0
## 1548 1548 5.0 8.0 10.0
## 1550 1550 6.0 4.0 8.0
## 1551 1551 6.0 7.0 8.0
## 1552 1552 8.0 6.0 9.0
## 1553 1553 8.0 6.0 9.0
## 1554 1554 8.0 6.0 7.0
## 1555 1555 7.0 1.0 7.0
## 1556 1556 7.0 9.0 8.0
## 1557 1557 8.0 6.0 8.0
## 1559 1559 2.0 4.0 2.0
## 1560 1560 4.0 5.0 7.0
## 1561 1561 7.0 6.0 7.0
## 1562 1562 6.0 8.0 8.0b. Conduct a moderation analysis to investigate whether perceived
intelligence moderates the effect of perceived attractiveness on
partner’s likability ratings? Print out the output from lavaan. Is the
interaction between attractiveness and intelligence significant?
speeddate_clean$attr_intel <- speeddate_clean$other_attr * speeddate_clean$other_intel
model <- '
other_like ~ b1*other_attr + b2*other_intel + b3*attr_intel
'
fit <- sem(model, data = speeddate_clean)
summary(fit, standardized = TRUE)## lavaan 0.6-21 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 4
##
## Number of observations 1509
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## other_like ~
## other_ttr (b1) 0.657 0.077 8.550 0.000 0.657 0.691
## other_ntl (b2) 0.488 0.060 8.148 0.000 0.488 0.396
## attr_intl (b3) -0.017 0.010 -1.746 0.081 -0.017 -0.183
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .other_like 1.555 0.057 27.468 0.000 1.555 0.465^The interaction between attractiveness and intelligence is not signifcant. (.081 > .05)
c. Redo the moderation analysis after centering other_attr and
other_intel. Print out the output from lavaan. Does centering change the
significance of the interaction between attractiveness and
intelligence?
speeddate_clean$attr_c <- scale(speeddate_clean$other_attr, center = TRUE, scale = FALSE)
speeddate_clean$intel_c <- scale(speeddate_clean$other_intel, center = TRUE, scale = FALSE)
speeddate_clean$attr_intel_c <- speeddate_clean$attr_c * speeddate_clean$intel_c
model_c <- '
other_like ~ attr_c + intel_c + attr_intel_c
'
fit_c <- sem(model_c, data = speeddate_clean)
summary(fit_c, standardized = TRUE)## lavaan 0.6-21 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 4
##
## Number of observations 1509
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## other_like ~
## attr_c 0.528 0.018 29.519 0.000 0.528 0.556
## intel_c 0.380 0.024 15.731 0.000 0.380 0.308
## attr_intel_c -0.017 0.010 -1.746 0.081 -0.017 -0.032
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .other_like 1.555 0.057 27.468 0.000 1.555 0.465^The interaction between attractiveness and intelligence remains not signifcant even after mean-centering. (.081 > .05)
Question 2
Davis-Kean (2005) showed that parents’ education levels are related
to children’s academic achievement through parents’ beliefs and
behaviors. To test a similar hypothesis, we investigate whether home
environment (HE) mediates the relation between mothers’ education (ME)
and children’s mathematical achievement (Math). Data used in this
example are from the National Longitudinal Survey of Youth, the 1979
cohort (NLSY79, for Human Resource Research, 2006).
a. Conduct the mediation analysis using the lavaan package and
obtain the confidence intervals using boostrapping. Print the output
from lavaan.
model_med <- '
HE ~ a*ME
math ~ b*HE + c*ME
indirect := a*b
total := c + (a*b)
'
fit_med <- sem(model_med,
data = nlsy,
se = "bootstrap",
bootstrap = 1000)## Warning: lavaan->lav_model_nvcov_bootstrap():
## 5 bootstrap runs failed or did not converge.summary(fit_med, standardized = TRUE, ci = TRUE)## lavaan 0.6-21 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 371
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 995
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## HE ~
## ME (a) 0.139 0.040 3.454 0.001 0.063 0.225
## math ~
## HE (b) 0.465 0.130 3.574 0.000 0.214 0.719
## ME (c) 0.463 0.119 3.878 0.000 0.232 0.689
## Std.lv Std.all
##
## 0.139 0.166
##
## 0.465 0.165
## 0.463 0.196
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HE 2.724 0.202 13.497 0.000 2.344 3.136
## .math 20.621 2.801 7.361 0.000 15.754 26.797
## Std.lv Std.all
## 2.724 0.972
## 20.621 0.924
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## indirect 0.065 0.026 2.516 0.012 0.022 0.123
## total 0.528 0.121 4.352 0.000 0.285 0.758
## Std.lv Std.all
## 0.065 0.027
## 0.528 0.223b. Are the three effects (i.e., a, c’, and ab) significant? Does
a mediation effect exist? If yes, is it a complete or partial
mediation?
The a path (ME → HE) is significant (b = 0.139, p = 0.001). The
indirect effect (ab) is also significant (b = 0.065, p = 0.010). The
direct effect remains significant (b = 0.463, p < 0.001). Therefore,
there is partial mediation.
c. Display the path plot using the semPaths() function from the
semPlot package.?
semPaths(fit_med,
"std",
whatLabels = "std",
edge.label.cex = 1.2,
layout = "tree",
residuals = TRUE,
intercepts = FALSE) 