“The reliability of social science studies has been thrown into
question, after an attempt to replicate project 21 high profile
experiments yielded only 13 successful reproductions. The two
year research project centered on studies that were published
between 2010 and 2015 in highly respected journals Science and
Nature…” THE WEEK, September 14, 2018, 20.
In a working paper entitled “Multiple Regression: Evolution
and Analysis” SOCIALVIBES.NET/ Numerous critics indicate that
Multiple Regression that emerged in general academia in the
“Big Data Days” (early to mid 70’s) was basically” junk science.”
Promoters indicated that the formula was a sign that the soft social
sciences had grown closer to the hard sciences. A computer could
do the work of numerous clerks, handling portions of a problem
back in the 50’s and 60’s. Now the computer could solve the
problem in minutes. However, not all numbers gathered were of the
necessary interval level which is needed for the formula. However,
there is overwhelming support for Multiple Regression. Numerous
articles describe and inadvertently or directly support it. It is not
the research strategy Multiple Regression that is at fault, but the
quality of the data that is used. The researcher, reviewer, or editor
misses the poor quality of the data that is analyzed.
It is now more important than ever. From a new book Andersen
(2017) approximates that whatever was “normal” deconstructed
into chaos around the year 2000 or the turn of the century... This
New York Times bestseller is written by a decorated social science
writer with numerous books, periodicals, and media reporting
suggests that from the Left strategies and research is of poor
quality. However, he does not say all is wrong. From the Right, the
Evangelical movement has had overwhelming growth dealing with
their own academic appearing studies and Christian published
books. Entire stores are devoted to this information. Further, they
can partner with large corporations that appear to be destroying the
planet. Additionally, out right “fakes news” has become normalized.
Again, he does not say all of their information is wrong. Thus, the
average citizen does not know what to believe. However, the least
strong points should be noted. Thus, two computations emerge...
1) to make nominal numbers, into interval integers a researcher
arbitrarily takes the formula of nominalism and added a zero.
Therefore, 0+1=1/2=.05. So, the division of two numbers by
2 makes it an interval number S Trochim [1] Multiple regression
needs all interval numbers or greater (ratio numbers) to complete
the task. In number 2, numerous attempts were made, but two
researchers in 1984, found that making an ordinal number into
an interval number did not disturb the outcomes. Winship C et al.
[2]. Thus, all numbers used in multiple regressions are interval or
ratio. Until a better measure is introduced it would appear to this
author that a few strategies can monitor and increase validity. One
is to allow lesser measures into manuscripts and the publication
“counts” toward tenure. Two, make a transition in the social
sciences, that a refereed manuscript found acceptable must then go
to a “Replicator.” This person has an excellent reputation and is paid
or given last authorship on a paper. He then retests the manuscript.
Other strategies include using triangulation where the multiple
regressions are also complimented by original observations and
exhaustive related literature review [3]. Last, create a longitudinal
study (not a panel study) in which descriptive statistics are used to
see certain variables are more prevalent than others to “explain” the
dependent variable (identity groups and/or demographic groups)
may also be useful. Further, get the word out to quality periodicals
that Multiple Regression works and that criticism has been helpful
to improve the strategy. It is a great story and can also be splintered
into small articles sent to national and local distributors. Therefore,
mainstream social sciences and related are doing something
about this issue. Last invite Replicators to analyze a quality article
to discover if there are some false positives that make multiple
regressions in error. The Replicator should be known to the editor
and journal reviewers and must be given attribution with the other
writers.