re:[tips] Effects, Affects, Independent and Depentent Variables

2017-07-21 Thread Mike Palij 

A few points:

(1)  I think that there is a conceptual confusion of using the
terms "to effect" as a substitute for "related to", the latter
can be either causal or correlational or both.

(2) One could argue that the fundamental goal of science
is to identify and define causal relationships among
variables (both empirical and latent) -- Humanistic psychologists
and others in that camp might disagree (at least they did
back in the 1960s and 1970s).  Causal relationships
can be observed in two general situations:

(a) Systematic Observational Research:  if we accept
that causal relationships actually exist in the physical world
(and not just in our minds), then systematic observation
can help to locate and identify such causal relationships.
Astronomy is the classical example of how the relationships
in systematic observations can be mathematically modeled
and these model imply explanations and theories.  Similar
situations exist in economics, political science, sociology,
and other areas where general mathematical modeling
and structural equation modeling (SEM) have been used.
Astronomy, however, is boosted by developments in physics,
making it more rigorous and provide guidance on how
choose among competing models of the same phenomenon.
The more "squishy" social and biomedical sciences have
additional difficulties due to problems of construct validity,
measurement model issues, and, the problem of "complexity"
because these phenomena are observed in what has
once been called "open systems", that is, an infinite number
of variable are present in the phenomena being studied but
only a few of them are really relevant.  Deciding which variables
are relevant is an ongoing process between collecting observations,
model testing, and repeat ad infinitum or one has "good enough"
model/theory.

In the case of astronomy, would anyone really quibble if
one asked "What is the effect of large planetary mass on
other nearby planets and objects?"  I grant that one might
have to understand how gravitational forces operate in order
to appreciate the question.

(b) Experimental designs, when conceived and implemented
correctly, represent "closed systems" where variables that might
be  involved in a cause-effect relationship are clearly defined
allow the causal relationship to be detected and measured
while controlling for all other variables that may or may not
be involved in the causal relationship (i.e., situations where
moderation and/or mediation may be operating).  This is
the classic situation of "independent variables" which are
selected and manipulated by the research (though participant
attributes like gender, age, degree of illness, skill, knowledge,
etc., might also be used as "independent variables" though
the mechanisms that are involved may be unclear and it would
be better to refer to these as "quasi-independent variables")
and "dependent variable(s)" which are suppose to manifest
the "effect" of the independent variable -- this is best represented
by the equation "dependent variable" = f("independent variable").

The function f(x) can take a variety of different forms though
there is the old saying that is relevant "causation implies correlation"
but correlation does not necessarily refer to the Pearson r, rather,
it refers to the general mathematical relationship relating the
independent variables to the dependent variable(s).  Note that
in (b) whether or not one is actually detecting a causal relationship
or merely a correlational relationship depends upon the quality
of the research design being used, how well the procedures
were executed, and other factors.  The recent problems with
replications highlight these points.

Experimental designs have been developed to establish causal
relationship and it may be proper to say the "Effect of X on Y"
though this may not apply to quasi-independent variables or
when relevant 3rd variables have not be included (in SEM
terms we have "model misspecification" regardless of whether
we are referring to an ANOVA structural model or a regression
model or an SEM model that uses both empirical and latent
variables to represent that cause-effect relationships.
NOTE:  In SEM modeling, especially of open systems, a
variable in one part of the model might be a "dependent
variable' but may also be an "independent variable" in relationship
to other variables.

So, perhaps one should ask for the mathematical model that
the "effect" embodies to better understand what it means,
as well as the degree that 3rd variables have been controlled.

More below.

Thu, 20 Jul 2017 16:53:19 -0700, Karl Louis Wuensch wrote:
 When using the word "effect," as in "effect-size," I 
sometimes

warn my students that I am using it in the "soft" sense (not causal).


I think that one should probably make clear how "effect sizes"
for causal relationships differ from "effect sizes" in correlational
relationships.  The former directly represents how changes in
the causal variable produce changes

Re: [tips] Effects, Affects, Independent and Depentent Variables.

2017-07-21 Thread Joan Warmbold 
APS has challenged members and others to read "Degrees of Maybe: How We
Can All Make Better Predictions" on NPR and then to leave a comment.  This
challenge can be found at:

https://www.psychologicalscience.org/news/degrees-of-maybe-how-we-can-all-make-better-predictions.html

The story is at:

http://www.npr.org/2017/06/26/534120962/degrees-of-maybe-how-we-can-all-make-better-predictions

Our concern with being more precise in our use of language ties in with
our concern with general scientific literacy, does it not?  And this story
concerning the type of mindset that enables people to make better
predictions very relevant.  Enjoy I hope.

Joan
jwarm...@oakton.edu


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Re: [tips] Effects, Affects, Independent and Depentent Variables.

2017-07-21 Thread Michael Scoles 
I'll tell ya, I don't see it happening.


On Fri, Jul 21, 2017 at 9:23 AM, Christopher Green <chri...@yorku.ca> wrote:

>
> Perhaps (he said wearily), we should end the long-futile effort to enforce
> conceptual distinctions by legislating the use of mere words and, instead,
> educate people rigorously enough that they are capable and, indeed,
> desirous, of respecting and expressing important conceptual distinctions in
> the flexible vocabulary of the true sophisticate.
>
> Chris
> …..
> Christopher D Green
> Department of Psychology
> York University
> Toronto, ON M3J 1P3
> Canada
> 43.773895°, -79.503670°
>
> chri...@yorku.ca
> http://www.yorku.ca/christo
> orcid.org/-0002-6027-6709
> ...
>
> On Jul 20, 2017, at 8:42 PM, Stuart McKelvie <smcke...@ubishops.ca> wrote:
>
>
>
>
>
>
>
> I agree with Karl.
>
>
>
> Here are some incomplete thoughts.
>
>
>
> When teaching methods, I would devote time to nomenclature. I suggested
> that the terms “independent variable” and “dependent variable” be reserved
> for experimental designs.
>
>
>
> How, then, do we refer to variables in non-experimental designs? If it is
> correlational, I suggested “predictor variable” and “predicted variable”
> (if the argument was framed in that manner). In some cases, the predictor
> variable might be categorical (perhaps a subject variable) and in others
> it might be continuous. If only a relationship was being examined, without
> any thought of predicting one from the other, we might say that each one is
> simply an associated variable or a correlated variable.
>
>
>
> Trickier is the situation where one variable is manipulated, but
> randomization has not occurred, as in a quasi-experimental design. Perhaps
> the manipulated variable could still be termed “independent”, but it would
> be inappropriate to call the other variable “dependent”. I suggested that
> “predicted variable” is a safer bet.
>
>
>
>
>
>
>
> 
> ___
>
>"*F**loreat* *L**abore*"
>
>
>
>
>
> "*Recti cultus pectora roborant*"
>
>
>
> *Stuart J. McKelvie, Ph.D.*, *Phone*: 819 822 9600 x 2402
> <(819)%20822-9600>
>
> Department of Psychology, *Fax*: 819 822 9661 <(819)%20822-9661>
>
> Bishop's University,
>
> 2600 rue College,
>
> Sherbrooke,
>
> Québec J1M 1Z7,
>
> Canada.
>
>
>
> E-mail: stuart.mckel...@ubishops.ca (or smcke...@ubishops.ca)
>
>
>
> Bishop's University Psychology Department Web Page:
>
> http://www.ubishops.ca/ccc/div/soc/psy
>
>
>
>          *F**loreat* *L**abore*"
>
>
>
>  
>
>
>
> 
>
> 
> ___
>
>
>
>
>
>
>
>
>
> *From:* Wuensch, Karl Louis [mailto:wuens...@ecu.edu <wuens...@ecu.edu>]
> *Sent:* July-20-17 7:52 PM
> *To:* Teaching in the Psychological Sciences (TIPS)
> *Subject:* [tips] Effects, Affects, Independent and Depentent Variables.
>
>
>
>
>
>   When using the word “effect,” as in “effect-size,” I sometimes
> warn my students that I am using it in the “soft” sense (not causal).  A
> related concern of mine is the use of the terms “independent variable” and
> “dependent variable” in research that is not experimental – that is, when
> no variable is manipulated.  There is a tendency to use “independent
> variable” whenever the variable is categorical and “dependent variable”
> when it is continuous.  Once I helped a previous student with his
> dissertation.  No variables were manipulated, but several were
> categorical.  I help him dummy code the categorical variables and use them
> in a multiple correlation analysis, with continuous covariates, to predict
> the focal continuous outcome variable.  His dissertation advisor told him
> no, do an ANOVA instead, because then we have independent and dependent
> variables and thus can make causal inferences.
>
>
>
> Cheers,
>
>  <http://core.ecu.edu/psyc/wuenschk/klw.htm>
>
> *From:* Annette Taylor [mailto:tay...@sandiego.edu <tay...@sandiego.edu>]
> *Sent:* Wednesday, July 19, 2017 10:08 AM
> *To:* Teaching in the Psychological Sciences (TIPS)
> *Subject:* [tips] Opinions needed
>
>
>
>
>
> Back in the good old dayswhen I was in graduate school...I
> specifically being told by my advisor that "effect" could not be used in a
>

Re: [tips] Effects, Affects, Independent and Depentent Variables.

2017-07-21 Thread Christopher Green 
Perhaps (he said wearily), we should end the long-futile effort to enforce 
conceptual distinctions by legislating the use of mere words and, instead, 
educate people rigorously enough that they are capable and, indeed, desirous, 
of respecting and expressing important conceptual distinctions in the flexible 
vocabulary of the true sophisticate. 

Chris
…..
Christopher D Green
Department of Psychology
York University
Toronto, ON M3J 1P3
Canada
43.773895°, -79.503670°

chri...@yorku.ca
http://www.yorku.ca/christo
orcid.org/-0002-6027-6709
...

On Jul 20, 2017, at 8:42 PM, Stuart McKelvie <smcke...@ubishops.ca> wrote:

> 
> 
> 
> 
>  
> I agree with Karl.
>  
> Here are some incomplete thoughts.
>  
> When teaching methods, I would devote time to nomenclature. I suggested that 
> the terms “independent variable” and “dependent variable” be reserved for 
> experimental designs.
>  
> How, then, do we refer to variables in non-experimental designs? If it is 
> correlational, I suggested “predictor variable” and “predicted variable” (if 
> the argument was framed in that manner). In some cases, the predictor 
> variable might be categorical (perhaps a subject variable) and in others  it 
> might be continuous. If only a relationship was being examined, without any 
> thought of predicting one from the other, we might say that each one is 
> simply an associated variable or a correlated variable.
>  
> Trickier is the situation where one variable is manipulated, but 
> randomization has not occurred, as in a quasi-experimental design. Perhaps 
> the manipulated variable could still be termed “independent”, but it would be 
> inappropriate to call the other variable “dependent”. I suggested that 
> “predicted variable” is a safer bet.
>  
>  
>  
> ___
>"Floreat Labore"
>  
>   
> "Recti cultus pectora roborant"
>  
> Stuart J. McKelvie, Ph.D., Phone: 819 822 9600 x 2402
> Department of Psychology, Fax: 819 822 9661
> Bishop's University,
> 2600 rue College,
> Sherbrooke,
> Québec J1M 1Z7,
> Canada.
>  
> E-mail: stuart.mckel...@ubishops.ca (or smcke...@ubishops.ca)
>  
> Bishop's University Psychology Department Web Page:
> http://www.ubishops.ca/ccc/div/soc/psy   
>  
>  Floreat Labore"
>  
>  
>  
> 
> _______________
>  
>  
>  
>  
> From: Wuensch, Karl Louis [mailto:wuens...@ecu.edu] 
> Sent: July-20-17 7:52 PM
> To: Teaching in the Psychological Sciences (TIPS)
> Subject: [tips] Effects, Affects, Independent and Depentent Variables.
>  
>  
> 
>   When using the word “effect,” as in “effect-size,” I sometimes warn 
> my students that I am using it in the “soft” sense (not causal).  A related 
> concern of mine is the use  of the terms “independent variable” and 
> “dependent variable” in research that is not experimental – that is, when no 
> variable is manipulated.  There is a tendency to use “independent variable” 
> whenever the variable is categorical and “dependent variable” when it is 
> continuous.  Once I helped a previous student with his dissertation.  No 
> variables were manipulated, but several were categorical.  I help him dummy 
> code the categorical variables and use them in a multiple correlation 
> analysis, with continuous covariates, to predict the focal continuous outcome 
> variable.  His dissertation advisor told him no, do an ANOVA instead, because 
> then we have independent and dependent variables and thus can make causal 
> inferences.
>  
> Cheers,
> 
> From: Annette Taylor [mailto:tay...@sandiego.edu] 
> Sent: Wednesday, July 19, 2017 10:08 AM
> To: Teaching in the Psychological Sciences (TIPS)
> Subject: [tips] Opinions needed
>  
>  
> 
> Back in the good old dayswhen I was in graduate school...I specifically 
> being told by my advisor that "effect" could not be used in a title unless it 
> was a clearly causal effect. So this does err on the side of emphasizing 
> causal. Nevertheless, I also heard somewhere from someone (???) that the 
> reason that the APA guidelines reduced the maximum number of words for a 
> title in APA style was to focus on the actual variables in the title and 
> eliminate any suggestion of "effect" in the title to reduce the abuse of the 
> term "effect"
>  
> Now, it makes for splashier headlines when your study gets published and 
>

RE:[tips] Effects, Affects, Independent and Depentent Variables.

2017-07-20 Thread Stuart McKelvie 
I agree with Karl.

Here are some incomplete thoughts.

When teaching methods, I would devote time to nomenclature. I suggested that 
the terms “independent variable” and “dependent variable” be reserved for 
experimental designs.

How, then, do we refer to variables in non-experimental designs? If it is 
correlational, I suggested “predictor variable” and “predicted variable” (if 
the argument was framed in that manner). In some cases, the predictor variable 
might be categorical (perhaps a subject variable) and in others  it might be 
continuous. If only a relationship was being examined, without any thought of 
predicting one from the other, we might say that each one is simply an 
associated variable or a correlated variable.

Trickier is the situation where one variable is manipulated, but randomization 
has not occurred, as in a quasi-experimental design. Perhaps the manipulated 
variable could still be termed “independent”, but it would be inappropriate to 
call the other variable “dependent”. I suggested that “predicted variable” is a 
safer bet.



___
   "Floreat Labore"

   [cid:image001.jpg@01D11876.FED84950]
"Recti cultus pectora roborant"

Stuart J. McKelvie, Ph.D., Phone: 819 822 9600 x 2402
Department of Psychology, Fax: 819 822 9661
Bishop's University,
2600 rue College,
Sherbrooke,
Québec J1M 1Z7,
Canada.

E-mail: stuart.mckel...@ubishops.ca<mailto:stuart.mckel...@ubishops.ca> (or 
smcke...@ubishops.ca<mailto:smcke...@ubishops.ca>)

Bishop's University Psychology Department Web Page:
http://www.ubishops.ca/ccc/div/soc/psy<blocked::http://www.ubishops.ca/ccc/div/soc/psy>

 Floreat Labore"

 [cid:image002.jpg@01D11876.FED84950]

[cid:image003.jpg@01D11876.FED84950]
___




From: Wuensch, Karl Louis [mailto:wuens...@ecu.edu]
Sent: July-20-17 7:52 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: [tips] Effects, Affects, Independent and Depentent Variables.



  When using the word “effect,” as in “effect-size,” I sometimes warn 
my students that I am using it in the “soft” sense (not causal).  A related 
concern of mine is the use of the terms “independent variable” and “dependent 
variable” in research that is not experimental – that is, when no variable is 
manipulated.  There is a tendency to use “independent variable” whenever the 
variable is categorical and “dependent variable” when it is continuous.  Once I 
helped a previous student with his dissertation.  No variables were 
manipulated, but several were categorical.  I help him dummy code the 
categorical variables and use them in a multiple correlation analysis, with 
continuous covariates, to predict the focal continuous outcome variable.  His 
dissertation advisor told him no, do an ANOVA instead, because then we have 
independent and dependent variables and thus can make causal inferences.

Cheers,
[Karl L. Wuensch]<http://core.ecu.edu/psyc/wuenschk/klw.htm>
From: Annette Taylor [mailto:tay...@sandiego.edu]
Sent: Wednesday, July 19, 2017 10:08 AM
To: Teaching in the Psychological Sciences (TIPS)
Subject: [tips] Opinions needed



Back in the good old dayswhen I was in graduate school...I specifically 
being told by my advisor that "effect" could not be used in a title unless it 
was a clearly causal effect. So this does err on the side of emphasizing 
causal. Nevertheless, I also heard somewhere from someone (???) that the reason 
that the APA guidelines reduced the maximum number of words for a title in APA 
style was to focus on the actual variables in the title and eliminate any 
suggestion of "effect" in the title to reduce the abuse of the term "effect"

Now, it makes for splashier headlines when your study gets published and people 
can talk about something BY INFERENCE "causing" something else simply because 
it is systematically linked with it.

Finally, on a similar topic, I woke up this morning to a news story about "risk 
factors" for Alzheimer's and my immediate thought was, how are these things 
"risk factors?" Specifically it mentioned hearing loss and sleep apnea. My 
understanding of a "risk factor" when talking about health research is that 
these are things that are either set: a family history of xyz; or something 
we can manage such as obesity or smoking. So hearing loss may be associated 
with Alzheimer's, might predict that some amount of the variance in developing 
Alzheimer's is accounted for by something like hearing loss. But is the use of 
the phrase "risk factor" correct in this instance.

Again, it seems to be a phrase that is being abused, much like "effect" i

[tips] Effects, Affects, Independent and Depentent Variables.

2017-07-20 Thread Wuensch, Karl Louis 
  When using the word “effect,” as in “effect-size,” I sometimes warn 
my students that I am using it in the “soft” sense (not causal).  A related 
concern of mine is the use of the terms “independent variable” and “dependent 
variable” in research that is not experimental – that is, when no variable is 
manipulated.  There is a tendency to use “independent variable” whenever the 
variable is categorical and “dependent variable” when it is continuous.  Once I 
helped a previous student with his dissertation.  No variables were 
manipulated, but several were categorical.  I help him dummy code the 
categorical variables and use them in a multiple correlation analysis, with 
continuous covariates, to predict the focal continuous outcome variable.  His 
dissertation advisor told him no, do an ANOVA instead, because then we have 
independent and dependent variables and thus can make causal inferences.

Cheers,
[Karl L. Wuensch]
From: Annette Taylor [mailto:tay...@sandiego.edu]
Sent: Wednesday, July 19, 2017 10:08 AM
To: Teaching in the Psychological Sciences (TIPS)
Subject: [tips] Opinions needed



Back in the good old dayswhen I was in graduate school...I specifically 
being told by my advisor that "effect" could not be used in a title unless it 
was a clearly causal effect. So this does err on the side of emphasizing 
causal. Nevertheless, I also heard somewhere from someone (???) that the reason 
that the APA guidelines reduced the maximum number of words for a title in APA 
style was to focus on the actual variables in the title and eliminate any 
suggestion of "effect" in the title to reduce the abuse of the term "effect"

Now, it makes for splashier headlines when your study gets published and people 
can talk about something BY INFERENCE "causing" something else simply because 
it is systematically linked with it.

Finally, on a similar topic, I woke up this morning to a news story about "risk 
factors" for Alzheimer's and my immediate thought was, how are these things 
"risk factors?" Specifically it mentioned hearing loss and sleep apnea. My 
understanding of a "risk factor" when talking about health research is that 
these are things that are either set: a family history of xyz; or something 
we can manage such as obesity or smoking. So hearing loss may be associated 
with Alzheimer's, might predict that some amount of the variance in developing 
Alzheimer's is accounted for by something like hearing loss. But is the use of 
the phrase "risk factor" correct in this instance.

Again, it seems to be a phrase that is being abused, much like "effect" is 
being abused.

Early morning musings--so they might be mushy.

Annette

Annette Kujawski Taylor, Ph.D.
Professor, Psychological Sciences
University of San Diego
5998 Alcala Park
San Diego, CA 921210
tay...@sandiego.edu

On Tue, Jul 18, 2017 at 10:00 PM, Teaching in the Psychological Sciences (TIPS) 
digest > wrote:
Subject: Opinions needed
From: Dap Louw >
Date: Tue, 18 Jul 2017 18:27:55 +
Tipsters

I am well aware that (and often frustrated by) all sorts of buzz words, 
concepts, theories, etc become the flavour of the month/year in organizations, 
including universities.  I would therefore appreciate your viewpoint on the 
following, especially as research methodology is not my field of specialization:

To what extent can we measure 'effect'?  In the last 40 years in Psychology 
I've been involved in hundreds of studies on "The effect of . 
(television on ...; poverty on ., etc, etc)".  BTW, when I used ' "the 
effect of" psychology' in Google Scholar search I got 2 460 000 results.  
However:

According to the latest recommendations of our University's Research Committee 
we cannot measure effect unless you make use of especially the longitudinal 
design.  Therefore any title such as  "The effect of . (television on 
...; poverty on ., etc, etc)" is unacceptable and should be replaced by 
"the perceived effect of ." or something similar.  Is this a case of 
methodology or semantics?

I look forward to hearing from you.  It's high time to get the TIPS ball 
rolling again!

Regards from this side of the ocean.

Dap

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