# 2 way interactions - KolobKreations by huanghengdong

VIEWS: 4 PAGES: 38

• pg 1
```									      Paste Correlations Table
into A1 and Standardized
Regression Weights Table
into F1, then click me.

Caveats and Assumptions:
1. Your latent variable names do not
end in numbers (bad: F1, Factor12). It
is okay to have oberved variables
named whatever you want.
2. Your error/residual names (if any)
do end with numbers (good: e1, res12)
3. Your variable names are not any of
the following: AVE, ASV, CR, MSV
Chi-square           df       p-val   Invariant?
Step 1. provide chi-square and df for unconstr
Overall Model
and constrained models, and provide the num
Unconstrained                             3405.7     1872                          groups. The thresholds (green cells) will be upd
Fully constrained                         3474.8     1918                                             automatically.
Number of groups                                        2
Difference                                69.1      46     0.015          NO Groups are different at the model level. Check
differences.
Chi-square Thresholds
90% Confidence                           3408.41     1873
Difference              2.71                          1     0.100
95% Confidence                           3409.54     1873                            Any chi-square more than the threshold (Gre
Difference              3.84                          1     0.050                  Cells) will be variant for a path by path analy
99% Confidence                           3412.33     1873
Difference              6.63                          1     0.010

Group1                 Group2
Sample Size                                   345       278
Regression Weight                          -0.213 -0.584        <-- Enter Data Here
Standard Error (S.E.)                          0.1    0.104
t-statistic                             2.557
p-value (2-tailed)                      0.011
<-- View Results Here

(m-1)^2                                   118336
(m+n-2)                                       621
(n-1)^2                                    76729
sqrt(1/m+1/n)                        0.080595738
1st half denom                       1.905571659
2nd half denom                       1.336394306
sqrt(1st half + 2nd half)            1.800546018
Full denom                           0.145116336
numerator                                   0.371
vide chi-square and df for unconstrained
ined models, and provide the number of
thresholds (green cells) will be updated
automatically.

different at the model level. Check path
differences.

quare more than the threshold (Green
be variant for a path by path analysis
Group 1: paste regression weights table in A2                            Group 2: paste regression
Paste data first, then
click this button.
Group 2: paste regression weights table in J2   Critical Ratios Matrix: paste in S2
atios Matrix: paste in S2
Included       Excluded        f-squared     Effect size
R-squared        0.4            0.3            0.1667    Medium

0          0.02           0.15             0.35
None        Small        Medium            Large

Instructions
Type your R-square for some endogenous variable into the "Included"
yellow cell. Then remove the path from the indicator/explanatory variable.
Rerun the analysis and then type the new R-squared into the "Excluded"
yellow cell.
This worksheet plots two-way interaction effects for standardized variables. For further information see www.jeremydawso

Enter information from your regression in

Variable names:
Independent variable:              Hiding
Moderator:            TmClimate                   5
Dependent variable              Burnout
Unstandardized Regression Coefficients:                                       4.5
Independent variable:                0.208
Moderator:                -0.376                  4

Burnout
Interaction:                0.134
3.5
Intercept / Constant:                3
3

2.5
The independent variable is the one whose
relationship with the DV is being moderated. The                                  2
moderator is the other IV doing the moderating. The
Interaction is the product variable. The                                         1.5
intercept/constant, is just the virtical position for the
graph (pick anything you want, but 3 makes the most                               1
sense). You'll need to change the label of the y axis
yourself (should be the dependend variable name).                                      Low Hiding

Do not type below this line
Low Hiding High Hiding
Low TmClimate        3.302        3.45
High TmClimate       2.282       2.966
s. For further information see www.jeremydawson.co.uk/slopes.htm.

Moderator
Low TmClimate

High TmClimate

Low Hiding              High Hiding
Group 1 Group 2
Female Male      z   2-tailed p 1-tailed p
indirect effect (Not standardized)    0.05    0.198 1.702   0.089      0.044
standard error                        0.062   0.061

Enter values into the ORANGE cells
<--Paste Mahalanobis table from AMOS into A1
Then press the button -->
Create Syntax for new Outlier
ax for new Outlier variable
1. Paste Data Here
2. Make sure all the        Remove Missing Data
data is still selected
3. Click the button
Rows
4. Smile :)
This tool removes any rows where there is missing data for
Processing       any variable.
Row          6
Column       4
Removed      0
Impute Missing Data

Imputing Instructions:
1. Paste data into Cell A1
2. Click the Impute button
3. The button automatically recopied the dataset after
making imputations, so now all you have to do is paste it
into SPSS over the current data set.
4. The pasting may take a few seconds.
ing Data

opied the dataset after
you have to do is paste it
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The Stats tools package is a collection of tools that I've either developed or adapted for making statistical analysis less painful
The current version was last updated on             3/1/2012
doctoral candidate
Case Western Reserve University
Information Systems Department

The tabs to the right of this tab are unfinished/unrefined tools that I have made for my own convenience, but are not yet use
ng statistical analysis less painful.

onvenience, but are not yet user friendly.
Group1                 Group2
Sample Size     1387 er               1784    er           t           p           (m-1)^2 (m+n-2)
0.111      0.025       0.126        0.021       0.462       0.644    1920996    3169
0.09      0.028       0.106        0.024       0.436       0.663    1920996    3169
0.301      0.032       0.321        0.027       0.480       0.631    1920996    3169
0.129      0.031       0.135        0.027       0.146       0.884    1920996    3169
0.134      0.025       0.146        0.023       0.351       0.725    1920996    3169
0.163      0.032       0.145        0.024       0.459       0.646    1920996    3169
-0.018     0.021       0.018        0.014       1.476       0.140    1920996    3169
-0.029     0.023      -0.027        0.018       0.070       0.945    1920996    3169
0.105      0.024       0.022        0.016       2.978       0.003    1920996    3169
0.068      0.027       0.039         0.02       0.882       0.378    1920996    3169
0.036      0.025       0.076        0.021       1.233       0.218    1920996    3169
0.116      0.023       0.115        0.019       0.034       0.973    1920996    3169
0.065      0.026       0.075        0.022       0.295       0.768    1920996    3169

This tool takes the sample size (B2 and D2), mean of subsamples (Column B an
groups, on every relationship, and produces a t-statistic (Column F and p-valu
their difference. I use this with
(n-1)^2               1st         2nd       sqrt(1st half + 2nd half)numerator
sqrt(1/m+1/n) half denom half denom         Full denom
3179089    0.035798 0.378865 0.442404 0.906239 0.032442               0.015
3179089    0.035798 0.475248 0.577834 1.026198 0.036736               0.016
3179089    0.035798 0.620732 0.731321 1.162778 0.041625                0.02
3179089    0.035798 0.582542 0.731321 1.146239 0.041033               0.006
3179089    0.035798 0.378865 0.530684 0.953703 0.034141               0.012
3179089    0.035798 0.620732 0.577834 1.09479 0.039192                0.018
3179089    0.035798 0.267327 0.196624 0.681139 0.024384               0.036
3179089    0.035798 0.320671 0.325032 0.803556 0.028766               0.002
3179089    0.035798 0.349162 0.256815 0.778445 0.027867               0.083
3179089    0.035798 0.441908 0.401273 0.918249 0.032872               0.029
3179089    0.035798 0.378865 0.442404 0.906239 0.032442                0.04
3179089    0.035798 0.320671 0.362149 0.82633 0.029581                0.001
3179089    0.035798 0.40978 0.485541 0.946214 0.033873                 0.01

mean of subsamples (Column B and D), and standard error (Columns C and E) between two
s a t-statistic (Column F and p-value (Column G) to determine the statistical significance of
their difference. I use this with plsgraph

```
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