Hello, {{ user.first_name }}!
Here are the most recent conversations on the Forum
-
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- {{ t.subject }} - new post on {{ t.last_post|date:'D d F' }} at {{ t.last_post|date:'P' }} {% endfor %}
Here are the most recent updates from mariposa.coop
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{{ entry.summary|safe|linebreaksbr }} read more
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This graphic will show how many of a customers orders had one or more ad touchpoints for this publisher where the intent matched the order bought. Needs to be pre-configered to be used (uses dimensions). These are conversions where the publisher was influential than just a brand . Charts are Google, Facebook, Marin (Both + other pubs.)


| Group A |
|---|
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Turkey
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Italy
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Wales
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Switzerland
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| Group B |
|---|
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Denmark
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Finland
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Belgium
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Russia
|
| Group C |
|---|
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Netherlands
|
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Ukraine
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Austria
|
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North
Macedonia
|
| Group D |
|---|
|
England
|
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Croatia
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Czech
Republic
|
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Scotland
|
| Group E |
|---|
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Spain
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Sweden
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Poland
|
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Slovakia
|
| Group F |
|---|
|
Portugal
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France
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Germany
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Hungary
|
|
Drop the first team here
|
VS
|
Drop the second team here
|
| Position | Player | Rating |
|---|
| Position | Player | Rating |
|---|


@DeveloperApi
public interface DriverPlugin
SparkPlugin
.| Modifier and Type | Method and Description |
|---|---|
default java.util.Map<String,String> |
init(SparkContext sc,
PluginContext pluginContext)
Initialize the plugin.
|
default Object |
receive(Object message)
RPC message handler.
|
default void |
registerMetrics(String appId,
PluginContext pluginContext)
Register metrics published by the plugin with Spark's metrics system.
|
default void |
shutdown()
Informs the plugin that the Spark application is shutting down.
|
default java.util.Map<String,String> init(SparkContext sc, PluginContext pluginContext)
This method is called early in the initialization of the Spark driver. Explicitly, it is called before the Spark driver's task scheduler is initialized. This means that a lot of other Spark subsystems may yet not have been initialized. This call also blocks driver initialization.
It's recommended that plugins be careful about what operations are performed in this call, preferably performing expensive operations in a separate thread, or postponing them until the application has fully started.
sc
- The SparkContext loading the plugin.pluginContext
- Additional plugin-specific about the Spark application where the plugin
is running.ExecutorPlugin.init(PluginContext,Map)
method.default void registerMetrics(String appId, PluginContext pluginContext)
This method is called later in the initialization of the Spark application, after most
subsystems are up and the application ID is known. If there are metrics registered in
the registry (PluginContext.metricRegistry()
), then a metrics source with the
plugin name will be created.
Note that even though the metric registry is still accessible after this method is called, registering new metrics after this method is called may result in the metrics not being available.
appId
- The application ID from the cluster manager.pluginContext
- Additional plugin-specific about the Spark application where the plugin
is running.default Object receive(Object message) throws Exception
Plugins can use Spark's RPC system to send messages from executors to the driver (but not the other way around, currently). Messages sent by the executor component of the plugin will be delivered to this method, and the returned value will be sent back to the executor as the reply, if the executor has requested one.
Any exception thrown will be sent back to the executor as an error, in case it is expecting a reply. In case a reply is not expected, a log message will be written to the driver log.
The implementation of this handler should be thread-safe.
Note all plugins share RPC dispatch threads, and this method is called synchronously. So performing expensive operations in this handler may affect the operation of other active plugins. Internal Spark endpoints are not directly affected, though, since they use different threads.
Spark guarantees that the driver component will be ready to receive messages through this handler when executors are started.
message
- The incoming message.Exception
default void shutdown()
This method is called during the driver shutdown phase. It is recommended that plugins not use any Spark functions (e.g. send RPC messages) during this call.


Leaderboard
Space Battle
Nick must be alphanumeric characters only!
Choose your ship
Frigate - classic ship
Cruiser - strong but slow
Corvette - fast and agile
Choose the color of your ship
Play
Gameplay
Steer with left/right arrow
Accelerate with the up arrow
Fire with 'F'
Collect cryptocurrency Monero


| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
84 |
dz = 1.02 ± .49 |
Bonfiglio et al. (2016, Ashland)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #1 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
119 |
dz = .90 ± .40 |
Hermann et al. (2016, Bradley)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #2 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
44 |
dz = 1.11 ± .70 |
Capaldi et al. (2016, Carleton)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #3 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
91 |
dz = 1.22 ± .49 |
Vaughn (2016, Ithaca)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #4 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
96 |
dz = .80 ± .44 |
Belanger et al. (2016, Miami)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #5 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
318 |
dz = .70 ± .24 |
Johnson et al. (2016, MichiganSU)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #6 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
124 |
dz = .99 ± .40 |
Allen (2016, MontanaSU)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #7 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
131 |
dz = .89 ± .38 |
Banks et al. (2016, NovaSU)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #8 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
158 |
dz = 1.08 ± .36 |
Wirth (2016, OSUNewark)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #9 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
101 |
dz = 1.01 ± .44 |
Grahe et al. (2016, PacificLU)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #10 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
117 |
dz = .82 ± .40 |
Bernstein (2016, PSAbington)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #11 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
142 |
dz = .87 ± .36 |
Devos et al. (2016, SDSU)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #12 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
178 |
dz = .88 ± .32 |
Davis & Hicks (2016, TexasA&M)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #13 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
119 |
dz = .83 ± .40 |
German et al. (2016, UCDavis)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #14 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
242 |
dz = 1.09 ± .29 |
Baranski et al. (2016, UCRiverside)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #15 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
137 |
dz = .75 ± .37 |
Brown et al. (2016, UofF)
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #16 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
89 |
dz = .90 ± .47 |
Fletcher et al. (2016, UofSM)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #17 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
81 |
dz = .66 ± .47 |
Re et al. (2016, UofT)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #18 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
194 |
dz = .87 ± .31 |
Ebersole et al. (2016, UofV) Study 1
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #19 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
95 |
dz = .96 ± .45 |
Cairo et al. (2016, VirginiaCU)
|
signal - inconsistent, smaller (imprecise) |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #20 |
|
|
|
|
| Stroop effect |
Stroop (1935) Study 2
|
100 |
dz = 2.04 |
620 |
d = .81 ± .17 |
Ebersole et al. (2016, mTurk) Study 2
|
signal - inconsistent, smaller |
congruent vs. incongruent (color name-font word) |
reaction time (RT) |
close |
ML3 #21 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
44 |
d = .77 ± .66 |
Bonfiglio et al. (2016, Ashland)
|
signal - consistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #22 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
58 |
d = .07 ± .53 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #23 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
23 |
d = .09 ± .89 |
Capaldi et al. (2016, Carleton)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #24 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
39 |
d = .58 ± .68 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #25 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
52 |
d = .08 ± .56 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #26 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
159 |
d = .09 ± .31 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #27 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
64 |
d = .65 ± .53 |
Allen (2016, MontanaSU)
|
signal - consistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #28 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
68 |
d = .68 ± .52 |
Banks et al. (2016, NovaSU)
|
signal - consistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #29 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
73 |
d = .03 ± .47 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #30 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
52 |
d = .64 ± .59 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #31 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
53 |
d = .42 ± .57 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #32 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
65 |
d = .54 ± .52 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #33 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
109 |
d = .16 ± .38 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #34 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
63 |
d = .42 ± .52 |
German et al. (2016, UCDavis)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #35 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
110 |
d = .11 ± .38 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #36 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
72 |
d = .22 ± .48 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #37 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
39 |
d = .05 ± .66 |
Fletcher et al. (2016, UofSM)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #38 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
37 |
d = .49 ± .70 |
Re et al. (2016, UofT)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #39 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
104 |
d = .04 ± .39 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #40 |
|
|
|
|
| metaphoric restructuring effect |
Boroditsky (2000) Study 1
|
56 |
d = .63 ± .56 |
51 |
d = -.12 ± .57 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent (imprecise) |
ego-priming vs. control vs. object-priming |
ambiguous temporal statement task |
very close |
ML3 #41 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
77 |
d = -.29 ± .46 |
Bonfiglio et al. (2016, Ashland)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #42 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
112 |
d = .00 ± .38 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #43 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
37 |
d = .27 ± .68 |
Capaldi et al. (2016, Carleton)
|
no signal - consistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #44 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
86 |
d = -.23 ± .43 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #45 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
91 |
d = .42 ± .43 |
Belanger et al. (2016, Miami)
|
no signal - consistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #46 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
303 |
d = -.01 ± .23 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #47 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
116 |
d = .07 ± .37 |
Allen (2016, MontanaSU)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #48 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
122 |
d = -.03 ± .36 |
Banks et al. (2016, NovaSU)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #49 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
148 |
d = -.05 ± .33 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #50 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
99 |
d = .35 ± .41 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #51 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
103 |
d = -.14 ± .39 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #52 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
133 |
d = .01 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #53 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
171 |
d = -.03 ± .30 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #54 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
114 |
d = .39 ± .38 |
German et al. (2016, UCDavis)
|
signal - inconsistent, smaller (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #55 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
217 |
d = .21 ± .27 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #56 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
129 |
d = -.08 ± .35 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #57 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
80 |
d = .10 ± .45 |
Fletcher et al. (2016, UofSM)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #58 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
78 |
d = .15 ± .46 |
Re et al. (2016, UofT)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #59 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
190 |
d = .15 ± .29 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
letters in first vs. third position |
word frequency |
very close |
ML3 #60 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
91 |
d = .24 ± .42 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent (imprecise) |
letters in first vs. third position |
word frequency |
very close |
ML3 #61 |
|
|
|
|
| availability heuristic (word frequency) |
Tversky & Kahneman (1973) Study 3
|
152 |
d = .82 ± .35 |
591 |
d = .18 ± .16 |
Ebersole et al. (2016, mTurk) Study 2
|
signal - inconsistent, smaller |
letters in first vs. third position |
word frequency |
very close |
ML3 #62 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
84 |
d = -.01 ± .45 |
Bonfiglio et al. (2016, Ashland)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #63 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
112 |
d = -.41 ± .38 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #64 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
32 |
d = .28 ± .72 |
Capaldi et al. (2016, Carleton)
|
no signal - consistent (imprecise) |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #65 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
88 |
d = .23 ± .43 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #66 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
89 |
d = .19 ± .42 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #67 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
240 |
d = -.12 ± .26 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #68 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
118 |
d = -.12 ± .37 |
Allen (2016, MontanaSU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #69 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
126 |
d = -.24 ± .36 |
Banks et al. (2016, NovaSU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #70 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
125 |
d = -.30 ± .36 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #71 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
96 |
d = .16 ± .41 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #72 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
100 |
d = .03 ± .40 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #73 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
133 |
d = .01 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #74 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
170 |
d = -.07 ± .30 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #75 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
109 |
d = -.18 ± .38 |
German et al. (2016, UCDavis)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #76 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
232 |
d = -.02 ± .26 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #77 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
129 |
d = -.21 ± .35 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #78 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
82 |
d = .39 ± .44 |
Fletcher et al. (2016, UofSM)
|
no signal - consistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #79 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
64 |
d = .11 ± .51 |
Re et al. (2016, UofT)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #80 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
177 |
d = -.03 ± .30 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #81 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
91 |
d = -.20 ± .42 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #82 |
|
|
|
|
| power decreases perspective taking effect |
Galinsky et al. (2006) Study 2a
|
42 |
d = .77 ± .65 |
591 |
d = .06 ± .16 |
Ebersole et al. (2016, mTurk) Study 2
|
no signal - inconsistent |
high vs. low power (situation recall) |
perspective-taking (perceived sarcasm) |
very close |
ML3 #83 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
72 |
d = .12 ± .47 |
Bonfiglio et al. (2016, Ashland)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #84 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
109 |
d = .20 ± .38 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #85 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
39 |
d = -.10 ± .65 |
Capaldi et al. (2016, Carleton)
|
no signal - inconsistent (imprecise) |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #86 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
64 |
d = .06 ± .50 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #87 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
82 |
d = .12 ± .44 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #88 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
274 |
d = .02 ± .24 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #89 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
109 |
d = -.11 ± .38 |
Allen (2016, MontanaSU)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #90 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
85 |
d = .42 ± .44 |
Banks et al. (2016, NovaSU)
|
no signal - consistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #91 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
127 |
d = -.25 ± .36 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #92 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
103 |
d = -.02 ± .40 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #93 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
118 |
d = .19 ± .36 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #94 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
131 |
d = -.11 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #95 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
153 |
d = .14 ± .32 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #96 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
105 |
d = .02 ± .38 |
German et al. (2016, UCDavis)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #97 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
212 |
d = -.03 ± .27 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #98 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
121 |
d = .10 ± .36 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #99 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
79 |
d = .05 ± .45 |
Fletcher et al. (2016, UofSM)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #100 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
73 |
d = -.34 ± .47 |
Re et al. (2016, UofT)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #101 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
171 |
d = .09 ± .30 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #102 |
|
|
|
|
| weight-importance effect |
Jostmann, Lakens et al. (2009) Study 2
|
51 |
d = .59 ± .58 |
89 |
d = .12 ± .42 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent |
heavy vs. light clipboard |
perceived importance |
very close |
ML3 #103 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
79 |
d = .27 ± .45 |
Bonfiglio et al. (2016, Ashland)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #104 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
116 |
d = .11 ± .38 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #105 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
37 |
d = .06 ± .68 |
Capaldi et al. (2016, Carleton)
|
no signal - inconsistent (imprecise) |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #106 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
83 |
d = -.26 ± .44 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #107 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
94 |
d = .09 ± .42 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #108 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
298 |
d = -.07 ± .22 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #109 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
122 |
d = -.05 ± .36 |
Allen (2016, MontanaSU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #110 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
129 |
d = .09 ± .36 |
Banks et al. (2016, NovaSU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #111 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
153 |
d = .17 ± .32 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #112 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
93 |
d = .17 ± .41 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #113 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
112 |
d = .08 ± .38 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #114 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
134 |
d = .06 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #115 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
172 |
d = -.05 ± .30 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #116 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
115 |
d = -.37 ± .37 |
German et al. (2016, UCDavis)
|
signal - inconsistent, opposite |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #117 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
237 |
d = .14 ± .26 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #118 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
132 |
d = -.01 ± .35 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #119 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
87 |
d = -.23 ± .43 |
Fletcher et al. (2016, UofSM)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #120 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
70 |
d = -.25 ± .48 |
Re et al. (2016, UofT)
|
no signal - inconsistent (imprecise) |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #121 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
189 |
d = -.08 ± .29 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #122 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
92 |
d = .30 ± .42 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #123 |
|
|
|
|
| warmer-heart-warmer-room effect |
Szymkow, Chandler, IJzerman et al. (2013) Study1
|
80 |
d = .86 ± .46 |
575 |
d = -.10 ± .16 |
Ebersole et al. (2016, mTurk) Study 2
|
no signal - inconsistent |
communal vs. agentic person description |
ambient room temperature |
very close |
ML3 #124 |
|
|
|
|
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
85 |
d = .17 ± .44 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #125 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
120 |
d = .12 ± .36 |
Fletcher et al. (2016, UofSM)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #126 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
45 |
d = -.06 ± .60 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #127 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
92 |
d = .05 ± .42 |
Vaughn (2016, Ithaca)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #128 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
96 |
d = -.20 ± .41 |
Banks et al. (2016, NovaSU)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #129 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
318 |
d = .16 ± .22 |
German et al. (2016, UCDavis)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #130 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
128 |
d = .19 ± .36 |
Re et al. (2016, UofT)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #131 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
134 |
d = .07 ± .34 |
Capaldi et al. (2016, Carleton)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #132 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
158 |
d = .00 ± .32 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #133 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
103 |
d = .09 ± .39 |
Brown et al. (2016, UofF)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #134 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
120 |
d = .10 ± .36 |
Grahe et al. (2016, PacificLU)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #135 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
143 |
d = -.02 ± .33 |
Bernstein (2016, PSAbington)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #136 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
179 |
d = -.09 ± .30 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #137 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
120 |
d = .07 ± .36 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #138 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
245 |
d = -.15 ± .26 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #139 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
137 |
d = -.08 ± .34 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #140 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
89 |
d = .01 ± .42 |
Bonfiglio et al. (2016, Ashland)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #141 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
84 |
d = -.07 ± .44 |
Allen (2016, MontanaSU)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #142 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
195 |
d = -.14 ± .28 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #143 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| NFC amplifies strong argument persuasion effect |
Cacioppo et al. (1983) Study 1
|
114 |
d = .91 ± .40 |
96 |
d = -.31 ± .42 |
Cairo et al. (2016, VirginiaCU)
|
no signal - inconsistent (imprecise) |
strong vs. weak arguments; need for cognition (NFC) |
persuasion (argument quality & persuasiveness) |
very close |
ML3 #144 |
|
|
|
Interaction effect: Strong arguments more persuasive than weak arguments (on average), which is amplified at high levels of Need for Cognition (NFC) |
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
82 |
d = -.02 ± .44 |
Bonfiglio et al. (2016, Ashland)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #145 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
118 |
d = -.14 ± .37 |
Hermann et al. (2016, Bradley)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #146 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
42 |
d = -.76 ± .68 |
Capaldi et al. (2016, Carleton)
|
signal - inconsistent, opposite (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #147 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
90 |
d = .21 ± .42 |
Vaughn (2016, Ithaca)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #148 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
95 |
d = .23 ± .42 |
Belanger et al. (2016, Miami)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #149 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
307 |
d = .13 ± .22 |
Johnson et al. (2016, MichiganSU)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #150 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
121 |
d = -.13 ± .36 |
Allen (2016, MontanaSU)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #151 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
130 |
d = -.11 ± .35 |
Banks et al. (2016, NovaSU)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #152 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
149 |
d = -.07 ± .32 |
Wirth (2016, OSUNewark)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #153 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
99 |
d = .19 ± .40 |
Grahe et al. (2016, PacificLU)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #154 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
112 |
d = .31 ± .38 |
Bernstein (2016, PSAbington)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #155 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
137 |
d = -.12 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #156 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
177 |
d = .05 ± .30 |
Davis & Hicks (2016, TexasA&M)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #157 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
110 |
d = -.04 ± .38 |
German et al. (2016, UCDavis)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #158 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
229 |
d = .38 ± .26 |
Baranski et al. (2016, UCRiverside)
|
signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #159 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
134 |
d = -.16 ± .34 |
Brown et al. (2016, UofF)
|
no signal - inconsistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #160 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
86 |
d = -.16 ± .43 |
Fletcher et al. (2016, UofSM)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #161 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
77 |
d = -.10 ± .46 |
Re et al. (2016, UofT)
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #162 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
178 |
d = .00 ± .30 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #163 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
92 |
d = .59 ± .44 |
Cairo et al. (2016, VirginiaCU)
|
signal - consistent (imprecise) |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #164 |
|
|
|
|
| self-esteem and event valence on psychological distance |
Ross & Wilson (2002) Study 2
|
357 |
d = .21 ± .21 |
574 |
d = .07 ± .16 |
Ebersole et al. (2016, mTurk) Study 2
|
no signal - consistent |
event valence (positive vs. negative); self-esteem |
subjective temporal distance |
very close |
ML3 #165 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
84 |
d = .54 ± .45 |
Bonfiglio et al. (2016, Ashland)
|
signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #166 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
120 |
d = -.08 ± .36 |
Hermann et al. (2016, Bradley)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #167 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
44 |
d = -.14 ± .62 |
Capaldi et al. (2016, Carleton)
|
no signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #168 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
92 |
d = .37 ± .42 |
Vaughn (2016, Ithaca)
|
no signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #169 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
93 |
d = -.37 ± .42 |
Belanger et al. (2016, Miami)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #170 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
316 |
d = .14 ± .22 |
Johnson et al. (2016, MichiganSU)
|
no signal - inconsistent |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #171 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
124 |
d = -.22 ± .36 |
Allen (2016, MontanaSU)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #172 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
130 |
d = -.09 ± .34 |
Banks et al. (2016, NovaSU)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #173 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
155 |
d = -.07 ± .32 |
Wirth (2016, OSUNewark)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #174 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
103 |
d = .45 ± .40 |
Grahe et al. (2016, PacificLU)
|
signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #175 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
119 |
d = .27 ± .36 |
Bernstein (2016, PSAbington)
|
no signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #176 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
138 |
d = -.14 ± .34 |
Devos et al. (2016, SDSU)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #177 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
178 |
d = .04 ± .30 |
Davis & Hicks (2016, TexasA&M)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #178 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
117 |
d = -.18 ± .37 |
German et al. (2016, UCDavis)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #179 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
243 |
d = .00 ± .25 |
Baranski et al. (2016, UCRiverside)
|
no signal - inconsistent |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #180 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
136 |
d = .02 ± .34 |
Brown et al. (2016, UofF)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #181 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
89 |
d = .36 ± .43 |
Fletcher et al. (2016, UofSM)
|
no signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #182 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
80 |
d = -.14 ± .45 |
Re et al. (2016, UofT)
|
no signal - inconsistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #183 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
192 |
d = .00 ± .29 |
Ebersole et al. (2016, UofV) Study 1
|
no signal - inconsistent |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #184 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
94 |
d = .13 ± .41 |
Cairo et al. (2016, VirginiaCU)
|
no signal - consistent (imprecise) |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #185 |
|
|
|
|
| moral licensing effect |
Monin & Miller (2001) Study 1
|
198 |
d = .43 ± .29 |
639 |
d = -.06 ± .16 |
Ebersole et al. (2016, mTurk) Study 2
|
no signal - inconsistent |
moral credentials (disagreeing with sexist statements) vs. control |
prejudice (favoring man for job) |
very close |
ML3 #186 |
|
|
|
|


You can change the orientation of a Menu Bar widget from horizontal to vertical, and vice versa. To do so, alter the HTML code for the menu bar and make sure you have the correct CSS file in your website.
The following procedure changes a horizontal Menu Bar widget to a vertical Menu Bar widget.


Refunds
Each time a charge is refunded a refund is created.
No refunds have been created yet.
ID
Amount
Charge
${page.pageNumber}
${refund.id}
${refund.amountFormatted} ${refund.currency}
${refund.charge}


Before Change
def test_duplicate_key(self):
Test simple dict.
conf = "key: thing1\nkey: thing2"
try:
with io.StringIO(conf) as f:
yaml.yaml.safe_load(f)
except Exception:
pass
else:
assert 0
def test_enviroment_variable(self):
Test config file with enviroment variable.
os.environ["PASSWORD"] = "secret_password"
conf = "password: !env_var PASSWORD"
After Change
def test_duplicate_key(self):
Test duplicate dict keys.
files = {YAML_CONFIG_FILE: "key: thing1\nkey: thing2"}
with self.assertRaises(HomeAssistantError):
with patch_yaml_files(files):
load_yaml_config_file(YAML_CONFIG_FILE)


This sample shows how to extract some data from Banana document and load it into the worksheet using the Excel JavaScript API.
1) Update the list with the current opened Banana documents
2) Select a Banana document file
3) Select a table (*)
4) Generate the selected table
(*)
- Tables should be added on different sheets.
- It is possible to overwrite an existing table: be sure to select the right sheet.
- It is not possible to create a new table if it already exists.


--- layout: page title: About header-img: "img/about-bg.png" ---
I've taught all around the world for the past 9 years. I love information and presenting that information to others in a way that sparks understanding.


{% extends "extend/base.html" %} {% block content %} {% if user.is_authenticated %} {% csrf_token %}
Your username and password didn't match. Please try again.


scikits.learn.decomposition.KernelPCA¶
- class scikits.learn.decomposition.KernelPCA(n_components=None, kernel='linear', sigma=1.0, degree=3, alpha=1.0, fit_inverse_transform=False)¶
Kernel Principal component analysis (KPCA)
Non-linear dimensionality reduction through the use of kernels.
Parameters : n_components: int or None :
Number of components. If None, all non-zero components are kept.
kernel: “linear” | “poly” | “rbf” | “precomputed” :
kernel Default: “linear”
sigma: float :
width of the rbf kernel Default: 1.0
degree: int :
degree of the polynomial kernel Default: 3
alpha: int :
hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True) Default: 1.0
fit_inverse_transform: bool :
learn the inverse transform (i.e. learn to find the pre-image of a point) Default: False
Attributes
lambdas_, alphas_: Eigenvalues and eigenvectors of the centered kernel matrix dual_coef_: Inverse transform matrix X_transformed_fit_: Projection of the fitted data on the kernel principal components Methods
- __init__(n_components=None, kernel='linear', sigma=1.0, degree=3, alpha=1.0, fit_inverse_transform=False)¶
- fit(X, y=None, **params)¶
Fit the model from data in X.
Parameters : X: array-like, shape (n_samples, n_features) :
Training vector, where n_samples in the number of samples and n_features is the number of features.
Returns : self : object
Returns the instance itself.
- fit_transform(X, y=None, **params)¶
Fit the model from data in X and transform X.
Parameters : X: array-like, shape (n_samples, n_features) :
Training vector, where n_samples in the number of samples and n_features is the number of features.
Returns : X_new: array-like, shape (n_samples, n_components) :
- inverse_transform(X)¶
Transform X back to original space.
Parameters : X: array-like, shape (n_samples, n_components) : Returns : X_new: array-like, shape (n_samples, n_features) :
- transform(X)¶
Transform X.
Parameters : X: array-like, shape (n_samples, n_features) : Returns : X_new: array-like, shape (n_samples, n_components) :


Rolled Ice Cream is not made like your traditional ice cream you would see in your freezer. It starts off as a sweet milk base and is poured onto a flat steel surface of as low as -35 degrees. The base is constantly chopped and scraped to create cool air around its surface. As the base starts to freeze, all of your favorite flavors can be added to make your favorite creation. Finally, watch as your ice cream folds into perfect little rolls!


CrimPapers
Articles published on 26 May 2018
On 26 May 2018, 5 new articles were published in 4 journals.
Dyadic Analysis of Childhood Emotional Maltreatment and Marital Satisfaction During the Transition to Parenthood: the Mediating Effects of Emotion Regulation Strategies and Psychological Distress by Siman Liu, Zhengyan Wang, Shan Lu, Jiahui Shi
Describing the Process and Quantifying the Outcomes of the Cuyahoga County Sexual Assault Kit Initiative
A Brief History of Personality Assessment in Police Psychology: 1916–2008
Politicized Nominations and Public Attitudes Toward the Supreme Court in the Polarization Era by Miles T. Armaly
Judicial Demeanor: Oral Argument in the High Court of Australia by Jordan Tutton, Kathy Mack, Sharyn Roach Anleu


Basic Slider Pagination circles with the height relative to the width
Basic HTML
This is the basic html structure:

Initialization
This is the main function to start your slideshow:
jQuery('your_object').camera();
Essential CSS
You have to call "camera.css" file.
By editing this CSS file you can create your own personal skin.
Slider with thumbnails Thumbnails with fixed height
Basic HTML
This is the html structure:

Initialization
Add some options to the main function.
jQuery('your_object').camera({ //the height and the presence of the thumbnails height: '41%', pagination: false, thumbnails: true });


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| Ad Soyad | Durum | Tarih | Ayarlar |
| joe Doe | Alıcı Okudu | 01.02.2019 18:25:04.427 | |
| MR. Kirk | Alıcı Okudu | 01.02.2019 18:25:04.427 | |
| MR. Topatan | Alıcı Okumadı | 01.02.2019 18:25:04.427 | |
| Metin Ali Feyyaz | Alıcı Okudu | 01.02.2019 18:25:04.427 |
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Aug 10, 201805:01 PM
DESTROYEDPER SHIPPER INSTRUCTIONKUALA LUMPUR (LOGISTICS HUB), MALAYSIA, MALAYSIA
Aug 10, 201811:19 AM
SHIPMENT DELAYSHIPPER INSTRUCTION TO DESTROYSHENZHEN, CHINA, PEOPLE'S REPUBLIC SHIPMENT DELAYSHIPPER INSTRUCTION TO DESTROYSHENZHEN, CHINA, PEOPLE'S REPUBLIC SHIPMENT DELAYSHIPPER INSTRUCTION TO DESTROYSHENZHEN, CHINA, PEOPLE'S REPUBLIC SHIPMENT DELAYSHIPPER INSTRUCTION TO DESTROYSHENZHEN, CHINA, PEOPLE'S REPUBLIC


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gameanalysis.regret module
A module for computing regret and social welfare of profiles
-
gameanalysis.regret.
max_pure_social_welfare
(game, *, by_role=False)[source]
Returns the maximum social welfare over the known profiles.
If by_role is specified, then max social welfare applies to each role
independently. If there are no profiles with full payoff data for a role,
an arbitrary profile will be returned.
-
gameanalysis.regret.
mixed_social_welfare
(game, mix)[source]
Returns the social welfare of a mixed strategy profile
-
gameanalysis.regret.
mixture_deviation_gains
(game, mix)[source]
Returns all the gains from deviation from a mixed strategy
The result is ordered by role, then strategy.
-
gameanalysis.regret.
mixture_regret
(game, mix)[source]
Return the regret of a mixture profile
-
gameanalysis.regret.
pure_social_welfare
(game, profile)[source]
Returns the social welfare of a pure strategy profile in game
-
gameanalysis.regret.
pure_strategy_deviation_gains
(game, profile)[source]
Returns the pure strategy deviations gains
-
gameanalysis.regret.
pure_strategy_deviation_pays
(game, profile)[source]
Returns the pure strategy deviation payoffs
The result is a compact array of deviation payoffs. Each element
corresponds to the payoff of deviating to strategy i from strategy j for
all valid deviations.
-
gameanalysis.regret.
pure_strategy_regret
(game, prof)[source]
Returns the regret of a pure strategy profile
If prof has more than one dimension, the last dimension is taken as a set
of profiles and returned as a new array.


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JABAWS MANUAL
About
- What is JABAWS?
- JABAWS Benefits
- JABA Web Services Programs
- What is new in JABAWS 2?
- What is JABAWS Client?
- JABAWS versions compatibility and Jalview support
- Programmatic access to JABAWS
What is JABAWS?
JABAWS stands for JAva Bioinformatics Analysis Web Services. As the name suggests, JABAWS is a collection of web services for bioinformatics, and currently provides services that make it easy to access well-known multiple sequence alignment and protein disorder prediction programs (see the list of currently supported programs) from Jalview. Future versions of JABAWS will incorporate many other tools.
Getting JABAWS
JABAWS consists of a server and client, but unlike most bioinformatics web service systems, you can download and run both parts on your own computer! If you want a server just for yourself, then download and install the JABAWS Virtual Appliance. It requires no configuration and is simple to install. If you want to install JABAWS for your lab or institution then download the JABAWS Web Application aRchive. It is slightly more complicated to configure but is very straightforward too. Finally, if you want to script against any version of JABAWS or are interested in writing your own client, the JABAWS command line client is what you need.
JABAWS Benefits
- Can be deployed on most operating systems, as a VMware or other compatible Virtual Appliance or a Tomcat Java Web Application.
- Comes complete with sources and binaries for all the bioinformatics programs that it runs.
- Can operate as a stand alone server or one that submits jobs to a cluster via DRMAA.
- Easy to access from Jalview using its graphical client, or using the JABAWS command line client.
- Clients can submit jobs to any JABAWS servers that you might want to access, such as the one running on your local computer, your lab's server, or the publicly available services at the University of Dundee.
- Local or intranet installation eliminates any security concerns you might have about sending sensitive data over the internet.
- Wide range of configuration options to control size of jobs accepted by a server and the command line options available for the program run by a service.
JABA Web Services Programs
JABAWS currently provides access to the following programs:
Multiple Sequence Alignement
- Clustal Omega (version 1.0.2)
- ClustalW (version 2.0.12)
- Mafft (version 6.857)
- Muscle (version 3.8.31)
- Tcoffee (version 8.99)
- Probcons (version 1.12)
Protein disorder prediction
Amino Acid conservation
- AACon (1.0)
What is new in JABAWS 2?
Comparing to previous version of JABAWS JABAWS 2 offers a greater number of diverse web services, Amazon EC 2 integration and improved easy of use. It contains
- updates for all multiple sequence alignment services
- four new protein disorder prediction services
- Clustal Omega multiple sequence alignment web service
- amino acid conservation service
- web services execution statistics visialization
- web services status check from a web page
- VirtualBox support was dropped in favour of VMware
- new WAR package for Mac users
- Amazon Machine Image (AMI) distributive to enable users to use JABAWS on the EC2 cloud
- Improved web services client API
- Simplified WAR package installation
What is JABAWS client?
A JABAWS client is a program that lets you run the bioinformatics methods for which a JABAWS server provides web services. The most basic JABAWS client is a command line Java application which can call any of the JABAWS web services methods on any instance of JABAWS Server that it can reach over the web. It is useful if you want to test, or execute the programs provided by a JABAWS server in your own scripts, and do not want to handle any web service specific details. The client is open source, so you can also use its source code to find out how to work with JABA Web Services if you would like to write your own client software. Jalview, which is a multiple sequence alignment and analysis application, provides a graphical JABAWS client. This client has the same functionality as the command line client, but instead allows JABAWS services to be accessed in a more user-friendly manner, through a graphical user interface.
JABAWS versions compatibility and Jalview support
JABAWS version 2 is fully backward compatible with JABAWS version 1. That means that all JABAWS 1 clients should be able to use JABAWS 2 instead. However, they will be limited to the multiple sequence alignment web services only. To benefit of new web services introduced in JABAWS 2 the clients have to be updated.
Jalview since version 2.6.1 integrates fully with JABAWS 1. Therefore it will be possible to use any versions of Jalview after 2.6.1 with JABAWS 1. However, the full support for JABAWS 2 is introduced in Jalview version 2.8 or later.
Programmatic access to JABAWS
JABA Web Services are WS-I basic profile compliant, which means they can be accessed using any programming language or system that can utilize standard SOAP web services. The WSDL for each service is published on the JABAWS home page, and you can use this to automatically generate service bindings for your program. If you use Java, however, then you may wish to use our client package to access JABAWS. This package is based on the autogenerated source code produced by wsimport, which is the Java tool for creating web service bindings; but in addition, offers some additional methods which simplify working with JABAWS. For more information please refer to the data model javadoc.


UK metal detectorists found guilty of hiding golden Anglo-Saxon haul
LONDON (Reuters) - Two British metal detectorists were found guilty on Thursday of hiding a hoard they had discovered of more than 300 Anglo-Saxon gold coins, ingots and jewelry, some dating back 1,100 years, which was worth around 3 million pounds ($3.8 million).
George Powell, 38, and Layton Davies, 51, made the find, which included some coins believed to date from the reign of King Alfred the Great and described as being of “immense historical value”, while scanning farmland in Leominster, central England.
British law requires that discovery of “treasure”, which includes metal items over 300 years old, must be declared to the landowner and reported to the local coroner.
However, the men kept the find to themselves and began to release the coins to the market through two specialist dealers. Police said they were still hunting for other coins.
“The coins Davies and Powell found were more than 1,100 years old and bore the inscriptions of Aelfred and Ceolwulf from the Saxon-Viking period,” said Lesley Milner of the Crown Prosecution Service.
“This find had immense historical value and should have been disclosed to the relevant authorities. But Davies and Powell actively hid their haul for their own selfish gain.”
Alfred ruled the southern area of Wessex from 871 until 899 while Ceowulf II was king of Mercia, north of Wessex, from 874-9.
Milner said the treasure would have been worth at least 2.9 million pounds.
The two men were both found guilty of theft and, along with the two dealers, conspiracy to convert or conceal criminal property. They will be sentenced on Friday at Worcester Crown Court.


Master Selsia:
Recently, several high-ranking Human officials and Priests were killed by monsters while traveling through Death Pass to Oren Castle.
There have also been cases of monsters nearby engaging in pillage and bandits attacking traders between Oren and Kiran...
That is the official story, in any case...
"Really? Do you think there is anything strange going on?"


At least 7 die in Bangladesh gas pipeline explosion
by The Associated PressDHAKA, Bangladesh — Police say at least seven people have been killed and another 25 injured after a gas pipeline exploded in southeastern Bangladesh.
Local police chief Mohammed Mohsin says the explosion took place Sunday morning at Patharghata area in Chittagong district. Portions of a wall of a nearby building collapsed because of the impact of the blast.
He says at least 25 people have been hospitalized.
Firefighters have joined the rescue operation.
It was not immediately clear what caused the explosion.
Chittagong is 216 kilometres (134 miles) southeast of the capital, Dhaka.
The Associated Press


Objective-C has the concept of “blocks”, which are basically anonymous inline functions. The syntax for them is like this:
^{ printf("x is %d\n", 42); }
This is a literal for a block that takes no arguments and prints a value when called.
Blocks are only suppored when PyObjC is compiled using an Objective-C compiler that also supports blocks.
The Python representation for a block is a callable object, that is you can call the block just like you call any other function object.
PyObjC manages the memory for blocks, it is not necessary to manage the reference counts of blocks in your code.
It is not possible to call arbitrary blocks because PyObjC needs to store some additional metadata for a block. This means it is only possible to call blocks where the bridge knows the call signature, which means:
It is possible to retrieve and set the call signature of a block using the __block_signature__ attribute on blocks.
It is very easy to use Objective-C methods that have a block as one of their arguments: just pass an arbitrary callable. PyObjC will automaticly wrap your callable in the right low-level datastructure.
One of the side-effects of this is that the variour storage classes that are defined for block-related variables are not relevant for Python users. Blocks behave just like regular functions.
The current implementation of blocks doesn’t allow for full introspection, which means that PyObjC must be taught about the signatures of blocks. This is done using the metadata system.
Changed in version 2.5: For basic blocks and (Objective-)C code compiled using a recent enough compiler the bridge can extract the block signature from the runtime.


[§103B-4] Conflict with federal law. This chapter shall not apply if the application of this chapter is in conflict with any federal law, or if the application of this chapter will disqualify any state or county agency from receiving federal funds or aid. [L 2010, c 68, pt of §2]


or search Apprenticeships by your interests
If you’re not entirely sure where to start looking for an apprenticeship - perhaps take a look at the interests pages and see if any of the organisations or areas of interest are appealing to you.
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Survey Questions
About You
Name (Required)
Link to Photo Image (Required)
Need a photo?
Question 1
Your mind is always buzzing with unexplored ideas and plans.
Please Choose...
1 (Strongly Disagree)
2
3
4
5 (Strongly Agree)
Question 2
Generally speaking, you rely more on your experience than your imagination.
Please Choose...
1 (Strongly Disagree)
2
3
4
5 (Strongly Agree)
Question 3
You find it easy to stay relaxed and focused even when there is some pressure.
Please Choose...
1 (Strongly Disagree)
2
3
4
5 (Strongly Agree)
Question 4
You rarely do something just out of sheer curiosity.
Please Choose...
1 (Strongly Disagree)
2
3
4
5 (Strongly Agree)
Question 5
People can rarely upset you.
Please Choose...
1 (Strongly Disagree)
2
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5 (Strongly Agree)
Question 6
It is often difficult for you to relate to other people’s feelings.
Please Choose...
1 (Strongly Disagree)
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5 (Strongly Agree)
Question 7
In a discussion, truth should be more important than people’s sensitivities.
Please Choose...
1 (Strongly Disagree)
2
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5 (Strongly Agree)
Question 8
You rarely get carried away by fantasies and ideas.
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You think that everyone’s views should be respected regardless of whether they are supported by facts or not.
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Question 10
You feel more energetic after spending time with a group of people.
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Polynesia
Polynesia is a subregion of Oceania, made up of more than 1,000 islands scattered over the central and southern Pacific Ocean. The indigenous people who inhabit the islands of Polynesia are termed Polynesians, and share many similar traits including language family, culture, and beliefs. Historically, they had a strong tradition of sailing, using stars to navigate at night.
The term Polynesia was first used in 1756 by French writer Charles de Brosses, and originally applied to all the islands of the Pacific. In 1831, Jules Dumont d'Urville proposed a restriction on its use during a lecture to the Geographical Society of Paris. Historically, the islands of the South Seas have been known as South Sea Islands, and their inhabitants as South Sea Islanders, even though the Hawaiian Islands are located in the North Pacific. Another term, the Polynesian Triangle, explicitly includes the Hawaiian Islands, as they form its northern vertex.
In Wikipedia. Retrieved January 24, 2018, from https://en.wikipedia.org/wiki/Polynesia


This tour of the falstad circuit simulator was developed for Jeff Gerfen's EE-346 Laboratory Final Project by
This simulator was originally developed by Paul Falstad and requires a java runtime.
After working through this walkthrough try the simulator on falstad.com
Good Luck!
The purpose of this tutorial is to provide a basic understanding of the various tools available to you with the Falstad Circuit simulator.
A negative voltage is indicated by a red color, where a positive is green. Moving yellow dots indicate current.
To speed up the simulation, if you want to see the steady state response, you simply need to slide the Simulation speed bar to the right. If you want to slow the simulation down, when viewing a higher frequency AC input, slide the Simulation speed bar to the left.
To speed up or slow down the current animation, move the current bar to the right or left.
When the simulator starts up it defaults to the move/drag mode. Ensure that the simulator is in drag mode by right clicking on a black part of the simulator and verify that the checkmark is next to the menu item titled 'Select/Drag Selected...'
Try dragging one of the resistors to connect the right branch to ground.
Note that the current begins to flow once the circuit is completed. Also, the current displayed passing through the wire satisfies the Ohm's Law relationship V=I*R
Next, drag the two remaining resistors into the circuit to complete the left branch of the circuit.
Confirm that the current passing through both the left and the right branch sum to equal the current provided by the source.
Feel free to experiment with different resistor and source values before moving on
Here is a demonstration of how to add components to your circuit
Your screen should be blank, now to add some components:
Here is a demonstration of how to edit component values with an Integrator Circuit
As we can see from the graphs at the bottom of the circuit, the integrator outputs the integral of the input AC voltage. Editing component signals is a fairly simple process.
All of the components in the simulator can be edited in this way.
Here is a demonstration of how to read component values with a Wave Rectifier.
To the left we see a basic diode clipping circuit.
To display a graph:
Now a graph should have appeared on the bottom of the simulator displaying the voltage of the output node.
The top right corner of the graph displays the peak value which in this case is the clipping point.
Try manipulating the voltage with the slider to the right of the simulator area. Note the effects on the ouput waveform.
Add another graph of the current through the diode using the same method as described above except this time select the diode.
To display current right-click on graph and ensure that only 'Show Current' is checked.
Stack the graphs by right-clicking on a graph and selecting 'Stack'
Note when the current in the diode flows compared to the clipping of the ouput waveform.
Here is a demonstration on how to export the circuits you have made.
We will export this example JFET Amplifier.
After you are done with this circuit (adding components, moving pieces around, etc...), you are ready to export:
Now that you have the exported information copied as described in the last slide, you are ready to import:
We are now going to use this imported JFET Amplifier circuit and modify it to make a BJT Amplifier:
Thanks for exploring our tour of the falstad simulator. If you enjoyed the simulator or you thought it was useful please consider exploring more at falstad.com
The code for this site was derived from go-tour.golang.com and may be found on github


Welcome to our store where you’ll find everything you want! Your presence is our motivation to do better!
We know the value of each penny you spend on buying our products. We appreciate you for trusting us! Satisfying your needs is our mission. Welcome to our store shop!
We get better with every customer we deal with. We grow by satisfying your needs. We welcome you to be a part of our proud family where every customer is a king!
We get better with every customer we deal with. We grow by satisfying your needs. We welcome you to be a part of our proud family where every customer is a king!


| Name | {{element.name}} | Exchange rate | {{element.exchangeRate}} | Edit |
|---|
| Type | Multicurrency Standard Student | Transaction comission | {{element.transactionComission}}% | Currency exchange commission | {{element.exchangeCurrencyCommission > 0 ? element.exchangeCurrencyCommission + '%' : '-'}} | Edit |
|---|
Bank accounts
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Web security
Z. Cliffe Schreuders
Web security
- As time has gone on, more and more infrastructure has moved to Web-based technologies
- Web sites / Web apps are very popular
- Often they are bespoke / customised for organisations
- Often based on various frameworks and content management systems (CMS)
- Web apps have particular kinds of security pitfalls, and have become targets of specialised attacks and assessments
Web basics
- Three core technologies make up web pages, as they are displayed client side:
HTML
- Hypertext Markup Language (HTML) is the markup language that defines the content of web pages
html_code:<!DOCTYPE html>

<title>This is a title</title>

This is a heading

Hello world!

CSS
- Cascading Style Sheets (CSS) defines the presentation of (typically) HTML content
- Inline:
css_code:
Red heading

css_code:
Javascript (JS)
- Javascript makes web pages interactive
- All modern web browsers contain a Javascript engine, based on the ECMAScript specification
- JS is a weakly and dynamically typed programming language
- Can execute statements in strings at run-time
- JS can modify the environment/DOM...
Document Object Model (DOM)
- HTML document internally modelled and accessed as a tree structure
- Modifying the DOM alters the website that is rendered
- JavaScript can add, change, and remove DOM elements and attributes (and CSS styles)
Web architecture
- Client/server
- In the clear (port 80, http://)
- Encrypted via PKI (port 443, https://)
Web architecture
- Hypertext Transfer Protocol (HTTP) is a request-response protocol
- Application layer (on top of TCP)
- Client (user agent, eg. web browser) connects to server
- Client makes request
- GET /index.html HTTP/1.1
- Host: www.example.com
- Server responds...
Web architecture
- Server responds:
- HTTP/1.1 200 OK
Date: Mon, 23 May 2005 22:38:34 GMT
Server: Apache/<IP_ADDRESS> (Unix) (Red-Hat/Linux)
Last-Modified: Wed, 08 Jan 2003 23:11:55 GMT
ETag: "3f80f-1b6-3e1cb03b"
Content-Type: text/html; charset=UTF-8
Content-Length: 138
Accept-Ranges: bytes
Connection: close

<title>An Example Page</title>
Hello World, this is a very simple HTML document.

Web architecture
Web architecture
- Originally the Web was made up of static HTML pages
- Web 2.0: interactive websites
- Code on server side (PHP, Java, etc)
- Code on client side (Javascript, Flash, etc)
- Databases are used to store and retrieve data
- Typically server side via SQL
- HTML5 makes it possible to store on the client
Javascript (JS) frameworks
- JavaScript libraries, such as jQuery add extra features and ways of coding websites
- Frameworks such as Angular, React, and Vue.js change the way web apps are developed and drive more logic to the client side (such as GUI/Widget based)
- Single page webapps rely heavily on JS frameworks
Ajax
- “Asynchronous JavaScript And XML”
- Enables web pages to change dynamically without reloading the entire page
- Typically JS is used to make an XMLHttpRequest
- The resulting response is typically JSON or XML data, which JS receives and then updates the DOM
Javascript security
- JS can modify the environment/DOM
- Can only access web resources; not files on the local system, except through user interaction
- Same-origin policy: scripts from one Web site do not have access to data from other sites
- Lots can go wrong
Content Management Systems (CMS)
- CMS are server-side
- A framework for building websites
- Wordpress (29% of the Web), Joomla, Drupal, SiteFinity
- Most have been written in PHP
- Designed to enable non-technical people to update content
- Extendable
- CMS and extensions can have vulnerabilities
WPScan
- Wordpress and it’s extensions have included critical security vulnerabilities
- WPScan is a WordPress vulnerability scanner
- Tests for misconfigurations and vulnerable versions
Web services
- In addition to web sites / apps, web services are used for machine-to-machine interactions
- For example, an interface used to query an online database, which is called by mobile apps and also other websites or software
Web services: REST
- One of the most common kinds of interfaces are RESTful APIs (based on Representational State Transfer)
- Basically make individual requests to a service via standard HTTP methods (GET, PUT, POST, DELETE)
- Typically content of messages is JSON or XML
Web vulnerabilities
- Vulnerabilities in frameworks and CMS
- Vulnerabilities in bespoke code are common
- Misplaced trust in the client, server, or users
- Browser/sandbox implementation errors
- There are types of security mistakes that are commonly found in websites...
Types of Web vulnerabilities
Web proxies
- Web proxies enable security testing to tamper with the behaviour of clients and servers
Web vulnerability scanners
- Many network vulnerability scanners (including Nessus and Nexpose) have Web scanning features
- Specialist tools also exist for various kinds of website testing (manual and automated):
- Burp suite
- OWASP ZAP
- w3af (web application attack and audit framework)
- Nikto
Crawling spiders
- Many tools crawl a site to download a copy of web pages and content
- Typically configurable: depth, max pages, etc
- May identify versions of frameworks, interesting content, etc
Finding vulnerable Web apps
- Automated scans may find obvious issues, but manual testing may be required to find less obvious flaws
- Because detecting coding flaws often involves interacting with the server with malicious code, there is a high risk of some impact on servers (vs other kinds of network scanning)
- Metasploit Pro and Nexpose both have web scanning features; MSP has more offensive/exploitation capabilities and delivers a payload
Conclusion
- Web security is of ever-growing importance
- Many technologies involved: HTTP/S, HTML, CSS, JS, CMS, PHP, Java, REST, ...
- Automated tools exist to scan for obvious issues
- More complex issues typically require more sophisticated approaches to testing
- Awareness of security issues in websites, and testing using web proxies