159.102 Instructions


159.102 Instructions for Assignment 3
Assignment 3 starts in Week 9 and is due in Week 11 (26 October 2023).
NOTE: Assignment 3 counts 10% towards your final result.
It is a good idea to put your name and ID number in a comment at the top of your program.
You have been given a contract by a factory that produces buttons. It is important that the factory
identifies damaged buttons so that they are not supplied to the stores. The factory has a camera that takes
a photo of buttons. The camera works only in black and white (no colour) and the resolution is not very
good, but that is not a problem.
Your job is to write a C++ program that identifies any damaged buttons in the photo. You need to
produce an image that displays a box around each button. If the button is damaged you must display a
red box and if the button is not damaged you must display a green box. Make sure you read carefully
through all the sections below.
Section A - input
The input to your program is the photo taken by the camera in the factory. This is available in a .ppm file
called Buttons.ppm. This file is available under Assessments on Stream. Do not edit this file in any way.
If you accidently modify the file then download a fresh copy of the file from Stream.
Your program must be able to work with any such photo. Do not assume a specific number of buttons.
Do not assume that buttons will always be in the same place in the photo. You can assume that buttons
are always the same basic size and that buttons will not be touching each other.
(Hint: Before starting on your program, check that the .ppm file has no errors. Download Buttons.ppm
from Stream and convert it to .bmp (or other format) and look at it. The display should look like this:
Just for interest – you can tell that the resolution of the camera is low because of the “stepped” edges to
the buttons in the image. Actually, for many problems of this type (i.e. identifying defects in products) it
is often better to use a black-and-white photo because the defects stand out more clearly.
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Section B – understanding the problem
Like many “real-life” systems, this type of project can never be perfect (which is what makes real-life
projects interesting). We do the best we can by noting the following:
Notes about the problem:
1. Buttons appear as white (or light grey) objects on a dark background. This is a black-and-white
photo which means every pixel is a shade of grey (i.e. the R, G and B values are the same for each
pixel). We define a pixel to be part of a button if its R (or G or B) value is greater than 128.
2. There will always be a few pixels around the edge of a button (depending on the shadows) that are
darker than this and will thus not count as part of the button. This does not matter.
3. We need to know how to “identify” a button. Basically we look for pixels where the R value is
greater than 128. But we need more than this – see next section below.
4. To draw a box around a button you need to know the minimum and maximum x-value of all
pixels in the button and also the minimum and maximum y-value of all pixels. The box then has a
top left corner of (xmin, ymin) and a bottom right corner of (xmax, ymax) and so on.
5. Some thought needs to be given to how we define a “damaged” button. This is entirely up to you.
Hint: a damaged button will have less total pixels than an undamaged button.
Section C – the algorithm to identify a button in the image
A button consists of pixels with R value greater than 128 AND the pixels must touch each other. If we
work through every pixel, we can identify a button by:
a) finding a pixel with R value greater than 128
b) finding all other pixels that connect to that pixel (and have R value greater than 128)
c) go back to where we were in (a) and continue
The image below is trying to show this. Step (a) is shown in yellow. We start at the top left and work
steadily across and down the image until we find a suitable pixel. Step (b) is shown in red – we find all
pixels connected to the first one. Step (c) is shown in green – we go back to where we were at step (a)
and continue looking for pixels with R value greater than 128. Note that this diagram is to get the idea –
the drawing is not perfect.
Now let us look at step (b) in more detail – if we have one pixel in the button, how do we find the others?
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Assume that we have found pixel A at location (x, y) with an R value of greater than 128. Thus we know
that pixel A is inside a button. We can then work through the pixels that touch pixel A (they are pixels B,
C, D and E). Note the locations of these pixels. Pixel B is in the same row of the image as pixel A so has
the same y value. But pixel B is one place to the left so it has a x value of x – 1. Pixel E is in the same
vertical column of the image as pixel A so has the same x value. But pixel E is one place further down
the screen so it has a y value of y + 1. Similarly for the other pixels.
Now that we know how to identify the “next door” pixels of pixel A, we have an algorithm as follows:
Find pixel A at location (x, y) and look for all connected pixels by:
Find pixel B at location (x – 1, y) and look for all connected pixels;
Find pixel C at location (x + 1, y) and look for all connected pixels;
Find pixel D at location (x, y – 1) and look for all connected pixels;
Find pixel E at location (x, y + 1) and look for all connected pixels;
This is a recursive algorithm – find the pixels connected to A by finding the pixels connected to B, etc.
While this may not look like the famous “caves” program, it is essentially the same situation. And we
have the same problem. We could develop an infinite loop where pixel A checks pixel B which checks
pixel A which checks pixel B, etc. And we solve the problem in the same way, i.e. we put a boolean into
each pixel and as soon as we have checked a pixel we exclude it from the search. Do not check that pixel
again.
Every recursive function needs a base case. In this case there are two which are:
- return if the pixel you are checking has an R values of 128 or less
- return if this pixel is excluded from the search (i.e. if this pixel has been checked before)
Some astute readers may have noticed that we are going on as if they are FOUR pixels next door to pixel
A when in fact there are EIGHT next door pixels. We left out the diagonal pixels. The reason for this is
that the recursion eventually works its way through all adjacent pixels. E.g. the pixel that is up and to the
left of A is also above B so will be checked when B is checked.
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Section D – program design
There was another programmer who used to work at the factory. Unfortunately, that programmer did not
study 159.102 at Massey and was therefore unable to complete the project. You may find some
interesting ideas in the partially completed program which is called Ass3-start.cpp and is available under
Assessments on Stream.
Download the program called Ass3-start.cpp and study it.
You MUST use the class called pixel_class. Note that two of the methods are at the end of the program.
This class is as discussed in the notes but has an extra boolean variable called exclude to assist with the
recursive function. This exclude variable is set to false at the start of the program and is set to true if this
particular pixel has been checked.
You MUST use the following global variables:
int screenx, screeny, maxcolours;
pixel_class picture[600][600];
It is highly recommended that you also use the global variables:
int total, xmin, xmax, ymin, ymax; // these MUST be global
However, if you make the program work without these variables then you do not need to use them.
You MUST use the function called loadButtons exactly as it is in the program.
The rest is up to you. You can keep everything currently in the program or replace some of it as long as
you use the compulsory sections of code listed above.
The basic outline of the main program is as follows:
• load the photo data into the picture using the function loadButtons
• work through all pixels identifying buttons and placing boxes into the picture
• write the picture data to a new .ppm file
• (outside your program) convert your new .ppm file to .bmp and view it
Extra notes on drawing a box:
You draw a box (or in fact anything) by placing pixels of a particular colour into the picture.
A box needs four values called xmin, xmax, ymin, ymax.
The top left corner of the box is (xmin, ymin) and the top right corner of the box is (xmax, ymin).
The bottom left corner of the box is (xmin, ymax) and the bottom right corner of the box is (xmax, ymax).
To draw the top line of the box, use the following loop (or similar):
for (x = xmin; x <= xmax; x++) {
 picture[x][ymin].loaddata(R, G, B);
 picture[x][ymin].setexclude(true);
}
It is very important to set the exclude variable in each pixel to true. These pixels are now part of a box
and no longer part of the buttons image. They must be excluded from any future searches for buttons.
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Section E – output
The output from your program is an image stored in a .ppm file. In order to view the image you will
probably need to convert it to a different format, e.g. a .bmp file.
The output image must show the buttons with boxes displayed around each button. The box must be red
if the button is damaged and green if the button is acceptable. It should look like this:
Oh dear, this image shows only green boxes. This is not the correct result.
Note 1: if you look very closely, you may see that some boxes do not perfectly sit around the button.
There may be one or two pixels on the “wrong side” of the green line. Do not worry about this. Do not
waste hours of time trying to get your boxes better than what is shown above. These boxes are perfectly
adequate to show which button is being referred to.
Note 2: you need to decide what defines a “damaged” button. Some buttons are obviously damaged,
others may be ok, not quite sure about that. Welcome to programming in the real world! As long as the
obviously damaged buttons are classified as damaged, that is ok. There may be one or two buttons that
some people may regard as damaged and other people may not. In the real factory, the “damaged”
buttons are checked by human experts before being discarded.
Some general notes about the assignments in 159.102
• You can find the assignment instructions in a file under Assessments and also the start week.
• You submit your assignments via Stream (under Assessments) before the due date and time
• The due date and time appear on the Assignment under Assessments (where you submit)
• Submit only your .cpp file
• Do not submit the .exe file or any data files or screen shots of the program running
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• Staff are not available to check your assignment before you submit it.
• Do not rush into submitting an assignment. You may find useful information in the notes during
the week after the assignment starts.
• Assignments may use C++ knowledge from 159.101, 159.102 and elsewhere. However, if you
use knowledge from elsewhere, make sure you use it correctly.
IMPORTANT rules for assignments in 159.102
• You may get assistance when writing an assignment. Assistance includes asking questions and
getting ideas from teaching staff and other students. Assistance also includes asking for help
when your program is not working correctly and you cannot find the error.
• You may NOT get someone else to write your assignment for you. If you submit a program
written by someone else, you will lose a significant amount of the marks for the assignment.
• You may NOT copy a program from the internet. If you submit a program copied from the
internet you will receive ZERO marks for the assignment. It is very easy for markers to find the
same program on the internet.
• The important thing is that you must show that you understand what is happening in the program
you submit. Teaching staff will sometimes arrange zoom sessions with students to check that they
understand their submission. If this happens to you, please do not be offended – it is something
we have to do as part of the quality assurance for the course.
Working on your assignments in 159.102
• You need an editor/compiler to create and run your program. Atom is provided (see notes to
install Atom under Week 1) but you can use any other IDE that supports C++
• Build up your program, for example: start by only converting decimal to binary. When this is
working include binary to decimal. Then build in the error checking.
• Give yourself plenty of time. Do not start 6 hours before the deadline!
• Do not give up just because the deadline arrives. You will still get some marks for a partial
solution. In a difficult situation, you can apply for an extension.
Marking criteria for assignments in 159.102
Assignments are marked out of 10 and marks can be lost for:
• programs not compiling or running
• errors in code
• programs that have not been tested for a variety of situations
• programs that do not follow the instructions that are provided
• programs that appear to be written by someone else
• programs that are copied from the internet

请教我如何看懂这个Mplus跑出来的数据结果:Mplus VERSION 8.3 (Mac) MUTHEN & MUTHEN 09/17/2025 10:20 AM INPUT INSTRUCTIONS TITLE: RI-CLPM with 3 Time Points (x, m, y); DATA: FILE = data.dat; FORMAT = FREE; LISTWISE = ON; VARIABLE: NAMES = id x1 x2 x3 m1 m2 m3 y1 y2 y3; USEVARIABLES = x1 x2 x3 m1 m2 m3 y1 y2 y3; MISSING = ALL (-999); ! 指定缺失值编码 DEFINE: CENTER x1 x2 x3 m1 m2 m3 y1 y2 y3 (GRANDMEAN); ! 可选的中心化处理 MODEL: ! 随机截距(个体特质成分) RI_x BY x1@1 x2@1 x3@1; RI_m BY m1@1 m2@1 m3@1; RI_y BY y1@1 y2@1 y3@1; RI_x RI_m RI_y@1; ! 固定随机截距方差为1(识别性) ! 结构部分(动态成分) x2 ON x1 m1 y1; x3 ON x2 m2 y2; m2 ON x1 m1 y1; m3 ON x2 m2 y2; y2 ON x1 m1 y1; y3 ON x2 m2 y2; ! 允许同一时间点的残差相关(可选) x1 WITH m1 y1; m1 WITH y1; x2 WITH m2 y2; m2 WITH y2; x3 WITH m3 y3; m3 WITH y3; ! 允许随机截距之间相关 RI_x WITH RI_m RI_y; RI_m WITH RI_y; OUTPUT: STANDARDIZED CINTERVAL MODINDICES(3.84); INPUT READING TERMINATED NORMALLY RI-CLPM with 3 Time Points (x, m, y); SUMMARY OF ANALYSIS Number of groups 1 Number of observations 1297 Number of dependent variables 9 Number of independent variables 0 Number of continuous latent variables 3 Observed dependent variables Continuous X1 X2 X3 M1 M2 M3 Y1 Y2 Y3 Continuous latent variables RI_X RI_M RI_Y Variables with special functions Centering (GRANDMEAN) X1 X2 X3 M1 M2 M3 Y1 Y2 Y3 Estimator ML Information matrix OBSERVED Maximum number of iterations 1000 Convergence criterion 0.500D-04 Maximum number of steepest descent iterations 20 Input data file(s) data.dat Input data format FREE UNIVARIATE SAMPLE STATISTICS UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS Variable/ Mean/ Skewness/ Minimum/ % with Percentiles Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median X1 0.000 0.283 -8.200 1.39% -3.200 -1.200 -0.200 1297.000 13.066 0.176 11.800 0.62% 0.800 2.800 X2 0.000 0.182 -8.226 3.70% -3.226 -1.226 -0.226 1297.000 14.370 0.478 11.774 1.16% 0.774 2.774 X3 0.000 0.209 -8.322 3.39% -3.322 -0.322 -0.322 1297.000 14.419 0.457 11.678 1.31% 0.678 2.678 M1 0.000 0.087 -14.548 1.70% -6.548 -1.548 0.452 1297.000 45.309 -0.250 21.452 0.31% 1.452 5.452 M2 0.000 -0.071 -14.228 4.78% -6.228 -1.228 0.772 1297.000 50.310 -0.261 21.772 0.39% 2.772 5.772 M3 0.000 -0.075 -13.690 6.17% -5.690 -1.690 0.310 1297.000 51.609 -0.374 22.310 0.23% 2.310 6.310 Y1 0.000 0.999 -7.326 4.24% -4.326 -2.326 -1.326 1297.000 25.693 0.992 19.674 0.31% -0.326 3.674 Y2 0.000 1.262 -7.263 6.55% -4.263 -2.263 -1.263 1297.000 28.518 2.058 19.737 1.08% 0.737 3.737 Y3 0.000 1.035 -7.138 7.32% -4.138 -2.138 -0.138 1297.000 25.910 1.585 19.862 0.62% 0.862 2.862 THE MODEL ESTIMATION TERMINATED NORMALLY WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE RI_M. MODEL FIT INFORMATION Number of Free Parameters 50 Loglikelihood H0 Value -33396.332 H1 Value -33390.429 Information Criteria Akaike (AIC) 66892.663 Bayesian (BIC) 67151.054 Sample-Size Adjusted BIC 66992.228 (n* = (n + 2) / 24) Chi-Square Test of Model Fit Value 11.805 Degrees of Freedom 4 P-Value 0.0189 RMSEA (Root Mean Square Error Of Approximation) Estimate 0.039 90 Percent C.I. 0.014 0.065 Probability RMSEA <= .05 0.725 CFI/TLI CFI 0.998 TLI 0.984 Chi-Square Test of Model Fit for the Baseline Model Value 4517.007 Degrees of Freedom 36 P-Value 0.0000 SRMR (Standardized Root Mean Square Residual) Value 0.010 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value RI_X BY X1 1.000 0.000 999.000 999.000 X2 1.000 0.000 999.000 999.000 X3 1.000 0.000 999.000 999.000 RI_M BY M1 1.000 0.000 999.000 999.000 M2 1.000 0.000 999.000 999.000 M3 1.000 0.000 999.000 999.000 RI_Y BY Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 X2 ON X1 0.189 0.088 2.139 0.032 M1 0.177 0.041 4.290 0.000 Y1 0.164 0.030 5.405 0.000 X3 ON X2 0.209 0.121 1.730 0.084 M2 0.277 0.061 4.557 0.000 Y2 0.178 0.036 4.960 0.000 M2 ON X1 0.339 0.259 1.308 0.191 M1 0.792 0.098 8.059 0.000 Y1 0.443 0.087 5.079 0.000 M3 ON X2 0.310 0.362 0.857 0.392 M2 1.132 0.170 6.663 0.000 Y2 0.575 0.124 4.632 0.000 Y2 ON X1 0.133 0.147 0.905 0.365 M1 0.360 0.040 9.104 0.000 Y1 0.360 0.043 8.330 0.000 Y3 ON X2 0.188 0.204 0.920 0.357 M2 0.507 0.066 7.690 0.000 Y2 0.400 0.057 7.071 0.000 RI_X WITH RI_M -6.923 2.220 -3.118 0.002 RI_Y -1.881 1.428 -1.317 0.188 RI_M WITH RI_Y -13.047 1.221 -10.686 0.000 X1 WITH M1 13.113 2.597 5.049 0.000 Y1 9.185 1.562 5.882 0.000 M1 WITH Y1 23.846 1.388 17.175 0.000 X2 WITH M2 13.162 2.514 5.234 0.000 Y2 8.933 1.536 5.815 0.000 M2 WITH Y2 21.944 2.387 9.192 0.000 X3 WITH M3 18.508 4.661 3.971 0.000 Y3 12.017 2.554 4.704 0.000 M3 WITH Y3 32.830 5.690 5.770 0.000 Intercepts X1 0.000 0.100 0.000 1.000 X2 0.000 0.098 0.000 1.000 X3 0.000 0.106 0.000 1.000 M1 0.000 0.189 0.000 1.000 M2 0.000 0.177 0.000 1.000 M3 0.000 0.216 0.000 1.000 Y1 0.000 0.140 0.000 1.000 Y2 0.000 0.136 0.000 1.000 Y3 0.000 0.147 0.000 1.000 Variances RI_X 2.051 1.361 1.507 0.132 RI_M -14.028 3.778 -3.713 0.000 RI_Y 1.000 0.000 999.000 999.000 Residual Variances X1 10.961 1.528 7.171 0.000 X2 10.490 1.552 6.759 0.000 X3 12.425 2.688 4.623 0.000 M1 60.235 4.020 14.983 0.000 M2 54.567 6.053 9.015 0.000 M3 74.646 13.493 5.532 0.000 Y1 24.284 0.965 25.160 0.000 Y2 22.962 1.080 21.256 0.000 Y3 26.951 2.469 10.916 0.000 STANDARDIZED MODEL RESULTS STDYX Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value RI_X BY X1 0.397 0.133 2.982 0.003 X2 0.378 0.126 3.001 0.003 X3 0.376 0.125 3.007 0.003 RI_M BY M1 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 RI_Y BY Y1 0.199 0.004 52.391 0.000 Y2 0.188 0.004 51.241 0.000 Y3 0.197 0.004 51.385 0.000 X2 ON X1 0.180 0.084 2.140 0.032 M1 0.318 0.073 4.380 0.000 Y1 0.218 0.040 5.467 0.000 X3 ON X2 0.208 0.120 1.733 0.083 M2 0.517 0.112 4.632 0.000 Y2 0.248 0.049 5.035 0.000 M2 ON X1 0.172 0.132 1.300 0.194 M1 0.757 0.091 8.279 0.000 Y1 0.313 0.062 5.058 0.000 M3 ON X2 0.163 0.191 0.855 0.392 M2 1.120 0.165 6.803 0.000 Y2 0.426 0.091 4.687 0.000 Y2 ON X1 0.090 0.100 0.902 0.367 M1 0.459 0.049 9.388 0.000 Y1 0.340 0.039 8.642 0.000 Y3 ON X2 0.140 0.152 0.919 0.358 M2 0.710 0.090 7.902 0.000 Y2 0.419 0.058 7.284 0.000 RI_X WITH RI_M 999.000 999.000 999.000 999.000 RI_Y -1.313 1.415 -0.928 0.353 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 X1 WITH M1 0.510 0.077 6.599 0.000 Y1 0.563 0.059 9.589 0.000 M1 WITH Y1 0.623 0.023 27.159 0.000 X2 WITH M2 0.550 0.070 7.828 0.000 Y2 0.576 0.057 10.070 0.000 M2 WITH Y2 0.620 0.030 20.461 0.000 X3 WITH M3 0.608 0.083 7.331 0.000 Y3 0.657 0.064 10.242 0.000 M3 WITH Y3 0.732 0.033 21.905 0.000 Intercepts X1 0.000 0.028 0.000 1.000 X2 0.000 0.026 0.000 1.000 X3 0.000 0.028 0.000 1.000 M1 0.000 0.028 0.000 1.000 M2 0.000 0.025 0.000 1.000 M3 0.000 0.030 0.000 1.000 Y1 0.000 0.028 0.000 1.000 Y2 0.000 0.026 0.000 1.000 Y3 0.000 0.029 0.000 1.000 Variances RI_X 1.000 0.000 999.000 999.000 RI_M 999.000 999.000 999.000 999.000 RI_Y 1.000 0.000 999.000 999.000 Residual Variances X1 0.842 0.106 7.968 0.000 X2 0.731 0.104 7.001 0.000 X3 0.855 0.182 4.690 0.000 M1 1.304 0.084 15.594 0.000 M2 1.078 0.124 8.670 0.000 M3 1.442 0.265 5.438 0.000 Y1 0.960 0.002 636.146 0.000 Y2 0.809 0.035 23.071 0.000 Y3 1.044 0.096 10.864 0.000 STDY Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value RI_X BY X1 0.397 0.133 2.982 0.003 X2 0.378 0.126 3.001 0.003 X3 0.376 0.125 3.007 0.003 RI_M BY M1 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 RI_Y BY Y1 0.199 0.004 52.391 0.000 Y2 0.188 0.004 51.241 0.000 Y3 0.197 0.004 51.385 0.000 X2 ON X1 0.180 0.084 2.140 0.032 M1 0.318 0.073 4.380 0.000 Y1 0.218 0.040 5.467 0.000 X3 ON X2 0.208 0.120 1.733 0.083 M2 0.517 0.112 4.632 0.000 Y2 0.248 0.049 5.035 0.000 M2 ON X1 0.172 0.132 1.300 0.194 M1 0.757 0.091 8.279 0.000 Y1 0.313 0.062 5.058 0.000 M3 ON X2 0.163 0.191 0.855 0.392 M2 1.120 0.165 6.803 0.000 Y2 0.426 0.091 4.687 0.000 Y2 ON X1 0.090 0.100 0.902 0.367 M1 0.459 0.049 9.388 0.000 Y1 0.340 0.039 8.642 0.000 Y3 ON X2 0.140 0.152 0.919 0.358 M2 0.710 0.090 7.902 0.000 Y2 0.419 0.058 7.284 0.000 RI_X WITH RI_M 999.000 999.000 999.000 999.000 RI_Y -1.313 1.415 -0.928 0.353 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 X1 WITH M1 0.510 0.077 6.599 0.000 Y1 0.563 0.059 9.589 0.000 M1 WITH Y1 0.623 0.023 27.159 0.000 X2 WITH M2 0.550 0.070 7.828 0.000 Y2 0.576 0.057 10.070 0.000 M2 WITH Y2 0.620 0.030 20.461 0.000 X3 WITH M3 0.608 0.083 7.331 0.000 Y3 0.657 0.064 10.242 0.000 M3 WITH Y3 0.732 0.033 21.905 0.000 Intercepts X1 0.000 0.028 0.000 1.000 X2 0.000 0.026 0.000 1.000 X3 0.000 0.028 0.000 1.000 M1 0.000 0.028 0.000 1.000 M2 0.000 0.025 0.000 1.000 M3 0.000 0.030 0.000 1.000 Y1 0.000 0.028 0.000 1.000 Y2 0.000 0.026 0.000 1.000 Y3 0.000 0.029 0.000 1.000 Variances RI_X 1.000 0.000 999.000 999.000 RI_M 999.000 999.000 999.000 999.000 RI_Y 1.000 0.000 999.000 999.000 Residual Variances X1 0.842 0.106 7.968 0.000 X2 0.731 0.104 7.001 0.000 X3 0.855 0.182 4.690 0.000 M1 1.304 0.084 15.594 0.000 M2 1.078 0.124 8.670 0.000 M3 1.442 0.265 5.438 0.000 Y1 0.960 0.002 636.146 0.000 Y2 0.809 0.035 23.071 0.000 Y3 1.044 0.096 10.864 0.000 STD Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value RI_X BY X1 1.432 0.475 3.015 0.003 X2 1.432 0.475 3.015 0.003 X3 1.432 0.475 3.015 0.003 RI_M BY M1 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 RI_Y BY Y1 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 X2 ON X1 0.189 0.088 2.139 0.032 M1 0.177 0.041 4.290 0.000 Y1 0.164 0.030 5.405 0.000 X3 ON X2 0.209 0.121 1.730 0.084 M2 0.277 0.061 4.557 0.000 Y2 0.178 0.036 4.960 0.000 M2 ON X1 0.339 0.259 1.308 0.191 M1 0.792 0.098 8.059 0.000 Y1 0.443 0.087 5.079 0.000 M3 ON X2 0.310 0.362 0.857 0.392 M2 1.132 0.170 6.663 0.000 Y2 0.575 0.124 4.632 0.000 Y2 ON X1 0.133 0.147 0.905 0.365 M1 0.360 0.040 9.104 0.000 Y1 0.360 0.043 8.330 0.000 Y3 ON X2 0.188 0.204 0.920 0.357 M2 0.507 0.066 7.690 0.000 Y2 0.400 0.057 7.071 0.000 RI_X WITH RI_M 999.000 999.000 999.000 999.000 RI_Y -1.313 1.415 -0.928 0.353 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 X1 WITH M1 13.113 2.597 5.049 0.000 Y1 9.185 1.562 5.882 0.000 M1 WITH Y1 23.846 1.388 17.175 0.000 X2 WITH M2 13.162 2.514 5.234 0.000 Y2 8.933 1.536 5.815 0.000 M2 WITH Y2 21.944 2.387 9.192 0.000 X3 WITH M3 18.508 4.661 3.971 0.000 Y3 12.017 2.554 4.704 0.000 M3 WITH Y3 32.830 5.690 5.770 0.000 Intercepts X1 0.000 0.100 0.000 1.000 X2 0.000 0.098 0.000 1.000 X3 0.000 0.106 0.000 1.000 M1 0.000 0.189 0.000 1.000 M2 0.000 0.177 0.000 1.000 M3 0.000 0.216 0.000 1.000 Y1 0.000 0.140 0.000 1.000 Y2 0.000 0.136 0.000 1.000 Y3 0.000 0.147 0.000 1.000 Variances RI_X 1.000 0.000 999.000 999.000 RI_M 999.000 999.000 999.000 999.000 RI_Y 1.000 0.000 999.000 999.000 Residual Variances X1 10.961 1.528 7.171 0.000 X2 10.490 1.552 6.759 0.000 X3 12.425 2.688 4.623 0.000 M1 60.235 4.020 14.983 0.000 M2 54.567 6.053 9.015 0.000 M3 74.646 13.493 5.532 0.000 Y1 24.284 0.965 25.160 0.000 Y2 22.962 1.080 21.256 0.000 Y3 26.951 2.469 10.916 0.000 R-SQUARE Observed Two-Tailed Variable Estimate S.E. Est./S.E. P-Value X1 0.158 0.106 1.491 0.136 X2 0.269 0.104 2.572 0.010 X3 0.145 0.182 0.798 0.425 M1 Undefined -0.30359E+00 M2 Undefined -0.78144E-01 M3 Undefined -0.44246E+00 Y1 0.040 0.002 26.196 0.000 Y2 0.191 0.035 5.431 0.000 Y3 Undefined -0.43814E-01 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.152E-04 (ratio of smallest to largest eigenvalue) CONFIDENCE INTERVALS OF MODEL RESULTS Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% RI_X BY X1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 X2 1.000 1.000 1.000 1.000 1.000 1.000 1.000 X3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 RI_M BY M1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 M2 1.000 1.000 1.000 1.000 1.000 1.000 1.000 M3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 RI_Y BY Y1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Y2 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Y3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 X2 ON X1 -0.039 0.016 0.044 0.189 0.335 0.363 0.417 M1 0.071 0.096 0.109 0.177 0.245 0.258 0.284 Y1 0.086 0.105 0.114 0.164 0.214 0.224 0.243 X3 ON X2 -0.102 -0.028 0.010 0.209 0.409 0.447 0.521 M2 0.120 0.158 0.177 0.277 0.377 0.396 0.433 Y2 0.085 0.107 0.119 0.178 0.236 0.248 0.270 M2 ON X1 -0.329 -0.169 -0.087 0.339 0.765 0.847 1.006 M1 0.539 0.599 0.630 0.792 0.954 0.985 1.045 Y1 0.218 0.272 0.300 0.443 0.587 0.614 0.668 M3 ON X2 -0.623 -0.400 -0.286 0.310 0.907 1.021 1.244 M2 0.695 0.799 0.853 1.132 1.412 1.465 1.570 Y2 0.255 0.332 0.371 0.575 0.779 0.818 0.895 Y2 ON X1 -0.246 -0.155 -0.109 0.133 0.376 0.422 0.513 M1 0.258 0.282 0.295 0.360 0.425 0.437 0.461 Y1 0.249 0.275 0.289 0.360 0.431 0.445 0.471 Y3 ON X2 -0.337 -0.212 -0.148 0.188 0.523 0.587 0.712 M2 0.337 0.378 0.399 0.507 0.616 0.636 0.677 Y2 0.254 0.289 0.307 0.400 0.493 0.511 0.546 RI_X WITH RI_M -12.642 -11.275 -10.575 -6.923 -3.271 -2.572 -1.205 RI_Y -5.559 -4.680 -4.230 -1.881 0.468 0.918 1.798 RI_M WITH RI_Y -16.192 -15.440 -15.056 -13.047 -11.039 -10.654 -9.902 X1 WITH M1 6.424 8.023 8.841 13.113 17.386 18.204 19.803 Y1 5.163 6.124 6.616 9.185 11.754 12.246 13.207 M1 WITH Y1 20.269 21.124 21.562 23.846 26.129 26.567 27.422 X2 WITH M2 6.685 8.233 9.025 13.162 17.298 18.090 19.639 Y2 4.976 5.922 6.406 8.933 11.460 11.943 12.889 M2 WITH Y2 15.795 17.265 18.017 21.944 25.871 26.622 28.093 X3 WITH M3 6.501 9.372 10.840 18.508 26.176 27.644 30.514 Y3 5.437 7.010 7.815 12.017 16.219 17.024 18.597 M3 WITH Y3 18.174 21.678 23.470 32.830 42.190 43.982 47.486 Intercepts X1 -0.258 -0.196 -0.165 0.000 0.165 0.196 0.258 X2 -0.253 -0.193 -0.162 0.000 0.162 0.193 0.253 X3 -0.272 -0.207 -0.174 0.000 0.174 0.207 0.272 M1 -0.486 -0.370 -0.310 0.000 0.310 0.370 0.486 M2 -0.455 -0.347 -0.291 0.000 0.291 0.347 0.455 M3 -0.557 -0.424 -0.356 0.000 0.356 0.424 0.557 Y1 -0.360 -0.274 -0.230 0.000 0.230 0.274 0.360 Y2 -0.350 -0.266 -0.224 0.000 0.224 0.266 0.350 Y3 -0.378 -0.288 -0.241 0.000 0.241 0.288 0.378 Variances RI_X -1.454 -0.616 -0.187 2.051 4.290 4.719 5.557 RI_M -23.758 -21.432 -20.242 -14.028 -7.814 -6.624 -4.297 RI_Y 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Residual Variances X1 7.024 7.965 8.447 10.961 13.475 13.957 14.898 X2 6.492 7.448 7.937 10.490 13.043 13.532 14.488 X3 5.501 7.157 8.003 12.425 16.846 17.693 19.348 M1 49.879 52.355 53.621 60.235 66.848 68.114 70.590 M2 38.976 42.704 44.610 54.567 64.524 66.431 70.158 M3 39.892 48.201 52.451 74.646 96.842 101.092 109.401 Y1 21.798 22.393 22.697 24.284 25.872 26.176 26.771 Y2 20.180 20.845 21.185 22.962 24.739 25.079 25.745 Y3 20.592 22.112 22.890 26.951 31.012 31.790 33.310 CONFIDENCE INTERVALS OF STANDARDIZED MODEL RESULTS STDYX Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% RI_X BY X1 0.054 0.136 0.178 0.397 0.616 0.658 0.740 X2 0.054 0.131 0.171 0.378 0.585 0.625 0.703 X3 0.054 0.131 0.170 0.376 0.581 0.620 0.697 RI_M BY M1 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y BY Y1 0.189 0.191 0.193 0.199 0.205 0.206 0.209 Y2 0.178 0.181 0.182 0.188 0.194 0.195 0.197 Y3 0.187 0.189 0.190 0.197 0.203 0.204 0.207 X2 ON X1 -0.037 0.015 0.042 0.180 0.319 0.345 0.397 M1 0.131 0.176 0.199 0.318 0.438 0.461 0.505 Y1 0.115 0.140 0.152 0.218 0.284 0.296 0.321 X3 ON X2 -0.101 -0.027 0.011 0.208 0.405 0.443 0.517 M2 0.229 0.298 0.333 0.517 0.700 0.735 0.804 Y2 0.121 0.151 0.167 0.248 0.329 0.345 0.375 M2 ON X1 -0.169 -0.087 -0.046 0.172 0.389 0.431 0.512 M1 0.521 0.578 0.606 0.757 0.907 0.936 0.992 Y1 0.154 0.192 0.211 0.313 0.415 0.435 0.473 M3 ON X2 -0.329 -0.211 -0.151 0.163 0.478 0.538 0.656 M2 0.696 0.797 0.849 1.120 1.391 1.442 1.544 Y2 0.192 0.248 0.276 0.426 0.575 0.604 0.660 Y2 ON X1 -0.168 -0.106 -0.074 0.090 0.255 0.287 0.348 M1 0.333 0.363 0.379 0.459 0.539 0.555 0.585 Y1 0.238 0.263 0.275 0.340 0.404 0.417 0.441 Y3 ON X2 -0.252 -0.158 -0.110 0.140 0.390 0.438 0.531 M2 0.479 0.534 0.562 0.710 0.858 0.886 0.941 Y2 0.271 0.307 0.325 0.419 0.514 0.532 0.568 RI_X WITH RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y -4.958 -4.087 -3.641 -1.313 1.015 1.460 2.332 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 999.000 999.000 999.000 X1 WITH M1 0.311 0.359 0.383 0.510 0.638 0.662 0.710 Y1 0.412 0.448 0.466 0.563 0.660 0.678 0.714 M1 WITH Y1 0.564 0.578 0.586 0.623 0.661 0.668 0.683 X2 WITH M2 0.369 0.412 0.435 0.550 0.666 0.688 0.731 Y2 0.428 0.464 0.482 0.576 0.670 0.688 0.723 M2 WITH Y2 0.542 0.561 0.570 0.620 0.670 0.679 0.698 X3 WITH M3 0.394 0.445 0.471 0.608 0.744 0.770 0.821 Y3 0.492 0.531 0.551 0.657 0.762 0.782 0.822 M3 WITH Y3 0.646 0.666 0.677 0.732 0.787 0.797 0.818 Intercepts X1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 X2 -0.067 -0.051 -0.043 0.000 0.043 0.051 0.067 X3 -0.071 -0.054 -0.046 0.000 0.046 0.054 0.071 M1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 M2 -0.064 -0.049 -0.041 0.000 0.041 0.049 0.064 M3 -0.077 -0.059 -0.049 0.000 0.049 0.059 0.077 Y1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 Y2 -0.066 -0.050 -0.042 0.000 0.042 0.050 0.066 Y3 -0.074 -0.057 -0.048 0.000 0.048 0.057 0.074 Variances RI_X 1.000 1.000 1.000 1.000 1.000 1.000 1.000 RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Residual Variances X1 0.570 0.635 0.668 0.842 1.016 1.050 1.115 X2 0.462 0.527 0.559 0.731 0.903 0.936 1.000 X3 0.385 0.497 0.555 0.855 1.154 1.212 1.324 M1 1.088 1.140 1.166 1.304 1.441 1.467 1.519 M2 0.758 0.834 0.874 1.078 1.283 1.322 1.398 M3 0.759 0.923 1.006 1.442 1.879 1.962 2.126 Y1 0.957 0.957 0.958 0.960 0.963 0.963 0.964 Y2 0.719 0.741 0.752 0.809 0.867 0.878 0.900 Y3 0.796 0.855 0.886 1.044 1.202 1.232 1.291 STDY Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% RI_X BY X1 0.054 0.136 0.178 0.397 0.616 0.658 0.740 X2 0.054 0.131 0.171 0.378 0.585 0.625 0.703 X3 0.054 0.131 0.170 0.376 0.581 0.620 0.697 RI_M BY M1 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y BY Y1 0.189 0.191 0.193 0.199 0.205 0.206 0.209 Y2 0.178 0.181 0.182 0.188 0.194 0.195 0.197 Y3 0.187 0.189 0.190 0.197 0.203 0.204 0.207 X2 ON X1 -0.037 0.015 0.042 0.180 0.319 0.345 0.397 M1 0.131 0.176 0.199 0.318 0.438 0.461 0.505 Y1 0.115 0.140 0.152 0.218 0.284 0.296 0.321 X3 ON X2 -0.101 -0.027 0.011 0.208 0.405 0.443 0.517 M2 0.229 0.298 0.333 0.517 0.700 0.735 0.804 Y2 0.121 0.151 0.167 0.248 0.329 0.345 0.375 M2 ON X1 -0.169 -0.087 -0.046 0.172 0.389 0.431 0.512 M1 0.521 0.578 0.606 0.757 0.907 0.936 0.992 Y1 0.154 0.192 0.211 0.313 0.415 0.435 0.473 M3 ON X2 -0.329 -0.211 -0.151 0.163 0.478 0.538 0.656 M2 0.696 0.797 0.849 1.120 1.391 1.442 1.544 Y2 0.192 0.248 0.276 0.426 0.575 0.604 0.660 Y2 ON X1 -0.168 -0.106 -0.074 0.090 0.255 0.287 0.348 M1 0.333 0.363 0.379 0.459 0.539 0.555 0.585 Y1 0.238 0.263 0.275 0.340 0.404 0.417 0.441 Y3 ON X2 -0.252 -0.158 -0.110 0.140 0.390 0.438 0.531 M2 0.479 0.534 0.562 0.710 0.858 0.886 0.941 Y2 0.271 0.307 0.325 0.419 0.514 0.532 0.568 RI_X WITH RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y -4.958 -4.087 -3.641 -1.313 1.015 1.460 2.332 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 999.000 999.000 999.000 X1 WITH M1 0.311 0.359 0.383 0.510 0.638 0.662 0.710 Y1 0.412 0.448 0.466 0.563 0.660 0.678 0.714 M1 WITH Y1 0.564 0.578 0.586 0.623 0.661 0.668 0.683 X2 WITH M2 0.369 0.412 0.435 0.550 0.666 0.688 0.731 Y2 0.428 0.464 0.482 0.576 0.670 0.688 0.723 M2 WITH Y2 0.542 0.561 0.570 0.620 0.670 0.679 0.698 X3 WITH M3 0.394 0.445 0.471 0.608 0.744 0.770 0.821 Y3 0.492 0.531 0.551 0.657 0.762 0.782 0.822 M3 WITH Y3 0.646 0.666 0.677 0.732 0.787 0.797 0.818 Intercepts X1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 X2 -0.067 -0.051 -0.043 0.000 0.043 0.051 0.067 X3 -0.071 -0.054 -0.046 0.000 0.046 0.054 0.071 M1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 M2 -0.064 -0.049 -0.041 0.000 0.041 0.049 0.064 M3 -0.077 -0.059 -0.049 0.000 0.049 0.059 0.077 Y1 -0.072 -0.054 -0.046 0.000 0.046 0.054 0.072 Y2 -0.066 -0.050 -0.042 0.000 0.042 0.050 0.066 Y3 -0.074 -0.057 -0.048 0.000 0.048 0.057 0.074 Variances RI_X 1.000 1.000 1.000 1.000 1.000 1.000 1.000 RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Residual Variances X1 0.570 0.635 0.668 0.842 1.016 1.050 1.115 X2 0.462 0.527 0.559 0.731 0.903 0.936 1.000 X3 0.385 0.497 0.555 0.855 1.154 1.212 1.324 M1 1.088 1.140 1.166 1.304 1.441 1.467 1.519 M2 0.758 0.834 0.874 1.078 1.283 1.322 1.398 M3 0.759 0.923 1.006 1.442 1.879 1.962 2.126 Y1 0.957 0.957 0.958 0.960 0.963 0.963 0.964 Y2 0.719 0.741 0.752 0.809 0.867 0.878 0.900 Y3 0.796 0.855 0.886 1.044 1.202 1.232 1.291 STD Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% RI_X BY X1 0.209 0.501 0.651 1.432 2.214 2.363 2.656 X2 0.209 0.501 0.651 1.432 2.214 2.363 2.656 X3 0.209 0.501 0.651 1.432 2.214 2.363 2.656 RI_M BY M1 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M2 999.000 999.000 999.000 999.000 999.000 999.000 999.000 M3 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y BY Y1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Y2 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Y3 1.000 1.000 1.000 1.000 1.000 1.000 1.000 X2 ON X1 -0.039 0.016 0.044 0.189 0.335 0.363 0.417 M1 0.071 0.096 0.109 0.177 0.245 0.258 0.284 Y1 0.086 0.105 0.114 0.164 0.214 0.224 0.243 X3 ON X2 -0.102 -0.028 0.010 0.209 0.409 0.447 0.521 M2 0.120 0.158 0.177 0.277 0.377 0.396 0.433 Y2 0.085 0.107 0.119 0.178 0.236 0.248 0.270 M2 ON X1 -0.329 -0.169 -0.087 0.339 0.765 0.847 1.006 M1 0.539 0.599 0.630 0.792 0.954 0.985 1.045 Y1 0.218 0.272 0.300 0.443 0.587 0.614 0.668 M3 ON X2 -0.623 -0.400 -0.286 0.310 0.907 1.021 1.244 M2 0.695 0.799 0.853 1.132 1.412 1.465 1.570 Y2 0.255 0.332 0.371 0.575 0.779 0.818 0.895 Y2 ON X1 -0.246 -0.155 -0.109 0.133 0.376 0.422 0.513 M1 0.258 0.282 0.295 0.360 0.425 0.437 0.461 Y1 0.249 0.275 0.289 0.360 0.431 0.445 0.471 Y3 ON X2 -0.337 -0.212 -0.148 0.188 0.523 0.587 0.712 M2 0.337 0.378 0.399 0.507 0.616 0.636 0.677 Y2 0.254 0.289 0.307 0.400 0.493 0.511 0.546 RI_X WITH RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y -4.958 -4.087 -3.641 -1.313 1.015 1.460 2.332 RI_M WITH RI_Y 999.000 999.000 999.000 999.000 999.000 999.000 999.000 X1 WITH M1 6.424 8.023 8.841 13.113 17.386 18.204 19.803 Y1 5.163 6.124 6.616 9.185 11.754 12.246 13.207 M1 WITH Y1 20.269 21.124 21.562 23.846 26.129 26.567 27.422 X2 WITH M2 6.685 8.233 9.025 13.162 17.298 18.090 19.639 Y2 4.976 5.922 6.406 8.933 11.460 11.943 12.889 M2 WITH Y2 15.795 17.265 18.017 21.944 25.871 26.622 28.093 X3 WITH M3 6.501 9.372 10.840 18.508 26.176 27.644 30.514 Y3 5.437 7.010 7.815 12.017 16.219 17.024 18.597 M3 WITH Y3 18.174 21.678 23.470 32.830 42.190 43.982 47.486 Intercepts X1 -0.258 -0.196 -0.165 0.000 0.165 0.196 0.258 X2 -0.253 -0.193 -0.162 0.000 0.162 0.193 0.253 X3 -0.272 -0.207 -0.174 0.000 0.174 0.207 0.272 M1 -0.486 -0.370 -0.310 0.000 0.310 0.370 0.486 M2 -0.455 -0.347 -0.291 0.000 0.291 0.347 0.455 M3 -0.557 -0.424 -0.356 0.000 0.356 0.424 0.557 Y1 -0.360 -0.274 -0.230 0.000 0.230 0.274 0.360 Y2 -0.350 -0.266 -0.224 0.000 0.224 0.266 0.350 Y3 -0.378 -0.288 -0.241 0.000 0.241 0.288 0.378 Variances RI_X 1.000 1.000 1.000 1.000 1.000 1.000 1.000 RI_M 999.000 999.000 999.000 999.000 999.000 999.000 999.000 RI_Y 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Residual Variances X1 7.024 7.965 8.447 10.961 13.475 13.957 14.898 X2 6.492 7.448 7.937 10.490 13.043 13.532 14.488 X3 5.501 7.157 8.003 12.425 16.846 17.693 19.348 M1 49.879 52.355 53.621 60.235 66.848 68.114 70.590 M2 38.976 42.704 44.610 54.567 64.524 66.431 70.158 M3 39.892 48.201 52.451 74.646 96.842 101.092 109.401 Y1 21.798 22.393 22.697 24.284 25.872 26.176 26.771 Y2 20.180 20.845 21.185 22.962 24.739 25.079 25.745 Y3 20.592 22.112 22.890 26.951 31.012 31.790 33.310 MODEL MODIFICATION INDICES NOTE: Modification indices for direct effects of observed dependent variables regressed on covariates may not be included. To include these, request MODINDICES (ALL). Minimum M.I. value for printing the modification index 3.840 M.I. E.P.C. Std E.P.C. StdYX E.P.C. ON/BY Statements X1 ON RI_X / RI_X BY X1 7.479 0.528 0.756 0.210 X1 ON RI_M / RI_M BY X1 10.109 0.194 999.000 999.000 X2 ON RI_X / RI_X BY X2 6.867 -1.425 -2.041 -0.539 X2 ON RI_M / RI_M BY X2 9.796 -0.439 999.000 999.000 X3 ON RI_X / RI_X BY X3 5.582 -0.598 -0.856 -0.224 X3 ON RI_M / RI_M BY X3 6.920 -0.233 999.000 999.000 M1 ON RI_X / RI_X BY M1 5.510 -0.475 -0.680 -0.100 M1 ON RI_M / RI_M BY M1 3.950 -0.165 999.000 999.000 M2 ON RI_X / RI_X BY M2 9.174 0.746 1.069 0.150 Y3 ON RI_M / RI_M BY Y3 4.805 0.261 999.000 999.000 RI_Y ON RI_X / RI_X BY RI_Y 4.025 1.741 2.493 2.493 RI_Y ON RI_M / RI_M BY RI_Y 4.052 0.252 999.000 999.000 RI_Y ON RI_Y / RI_Y BY RI_Y 4.156 -3.327 -3.327 -3.327 ON Statements RI_X ON X1 4.806 -0.168 -0.117 -0.422 RI_X ON X3 4.053 -0.504 -0.352 -1.342 RI_X ON M1 5.785 -0.053 -0.037 -0.250 RI_X ON M2 6.766 0.630 0.440 3.130 RI_Y ON X2 4.240 0.306 0.306 1.157 RI_Y ON M1 6.877 0.065 0.065 0.439 RI_Y ON M3 4.363 0.157 0.157 1.130 X1 ON X1 7.492 0.528 0.528 0.528 X1 ON X2 5.062 0.483 0.483 0.507 X1 ON M1 10.124 0.195 0.195 0.367 X1 ON M2 9.075 0.274 0.274 0.541 X2 ON X2 6.864 -1.425 -1.425 -1.425 X2 ON X3 9.461 1.335 1.335 1.344 X2 ON M2 9.790 -0.438 -0.438 -0.823 X3 ON X1 5.718 -0.133 -0.133 -0.126 X3 ON X3 5.621 -0.600 -0.600 -0.600 X3 ON M1 7.140 -0.044 -0.044 -0.079 X3 ON M3 6.954 -0.234 -0.234 -0.442 M1 ON X1 5.502 -0.475 -0.475 -0.252 M1 ON X2 7.330 -0.470 -0.470 -0.262 M1 ON M1 3.942 -0.165 -0.165 -0.165 M2 ON X2 9.179 0.746 0.746 0.397 M2 ON X3 5.349 0.216 0.216 0.116 Y3 ON M1 4.574 0.048 0.048 0.064 Y3 ON M3 4.818 0.261 0.261 0.370 WITH Statements M1 WITH RI_Y 6.910 3.361 3.361 0.433 M1 WITH X2 4.191 11.321 11.321 0.450 M2 WITH RI_X 5.553 3.004 2.098 0.284 M2 WITH RI_Y 6.864 -4.591 -4.591 -0.621 M2 WITH X1 8.558 -6.901 -6.901 -0.282 M2 WITH X3 6.690 2.166 2.166 0.083 Y3 WITH RI_Y 6.057 -6.790 -6.790 -1.308 Y3 WITH M1 5.473 2.933 2.933 0.073 Y3 WITH M2 5.011 -2.600 -2.600 -0.068 Variances/Residual Variances RI_Y 4.058 -6.575 -6.575 -6.575 Beginning Time: 10:20:15 Ending Time: 10:20:15 Elapsed Time: 00:00:00 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2019 Muthen & Muthen
09-18
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