A CONTENT ANALYSIS OF QUANTITATIVE RESEARCHIN JOURNAL OF MAR.docx

A CONTENT ANALYSIS OF QUANTITATIVE RESEARCH
IN JOURNAL OF MARITAL AND FAMILY THERAPY:
A 10-YEAR REVIEW
Elizabeth O. Parker, Jennifer Chang, and Volker Thomas
University of Iowa
We examined the trends of quantitative research over the past
10 years in the Journal of
Marital and Family Therapy (JMFT). Specifically, within the
JMFT, we investigated the
types and trends of research design and statistical analysis
within the quantitative research
that was published in JMFT from 2005 to 2014. We found that
while the amount of peer-re-
viewed articles have increased over time, the percentage of
quantitative research has
remained constant. We discussed the types and trends of
statistical analysis and the implica-
tions for clinical work and training programs in the field of
marriage and family therapy.
In the field of marriage and family therapy (MFT), the role of
research has greatly evolved
over the past several decades. Competing with other mental
health fields, many believe that the
field should continue to provide evidence that supports the
effectiveness of MFT interventions in
improving the clients’ well-being (Hawley, Bailey, & Pennick,
2000). Additionally, Hawley and his
colleagues state that, “research can strengthen the field by

legitimizing [marriage and] family ther-
apy as an approach to other mental health disciplines and third-
party payers and by helping
improve services to our consumers, the clients” (p. 9).
The evolution of MFT has been influenced by the use of
different qualitative and/or quantita-
tive research methods and procedures (Køppe & Dammeyer,
2014). With the advancement of tech-
nology, statistical analyses have become easier to complete and
have become more sophisticated
over time (Køppe & Dammeyer, 2014). In particular, in recent
years, technology has advanced
more quickly than ever before, expanding the possibilities of
ways quantitative research data may
be analyzed. When examining the field of MFT over the past 10
years, how the trends in quantita-
tive research methods and statistical analyses are reflected in
the top journals of the field has not
been investigated. In this study, the authors conducted a content
analysis to examine these trends
in a leading journal of MFT, the Journal of Marital and Family
Therapy (JMFT).
LITERATURE REVIEW
The Research–Practice Gap
According to Sprenkle and Piercy (2005), “Over the course of
its history, the field of MFT has
had an ambivalent relationship with research” (p. 3). While the
founding of MFT was built on
early family therapy pioneers, or “researchers,” who examined
the interactional patterns of prob-
lem families, the field has maintained only a relatively small
empirical research tradition compared

to its clinical component more than 70 years later (Sprenkle &
Piercy, 2005). One reason that may
explain this discrepancy is that a master’s degree was
established since 1970 to be the minimal
degree required to enter into the field (Sprenkle & Piercy,
2005). Additionally, many master’s pro-
grams do not include a rigorous research curriculum in their
training (Crane, Wampler, Sprenkle,
Sandberg, & Hovestadt, 2002; Hawley & Gonzalez, 2005;
Sprenkle & Piercy, 2005). With that, it is
Elizabeth O. Parker, MA, Department of Rehabilitation and
Counselor Education, University of Iowa; Jennifer
Chang, BA, Department of Counseling Psychology, University
of Iowa; and Volker Thomas, PhD, Department of
Rehabilitation and Counselor Education, University of Iowa.
Address correspondence to Elizabeth O. Parker, Department of
Rehabilitation and Counselor Education,
University of Iowa, N338 Lindquist Center, Iowa City, Iowa
52242; E-mail: [email protected]
January 2016 JOURNAL OF MARITAL AND FAMILY
THERAPY 3
Journal of Marital and Family Therapy 42(1): 3–18
doi: 10.1111/jmft.12138
© 2016 American Association for Marriage and Family Therapy
possible that many master’s level therapists may feel that
research is irrelevant to their clinical
work and thus may not feel the need to produce research

(Ko�suti�c, Sanderson, & Anderson, 2012;
Owenz & Hall, 2011).
Although the field of MFT has grown over the last several
decades, a research–practice gap is still
present (Hawley & Gonzalez, 2005; Owenz & Hall, 2011;
Sprenkle & Piercy, 2005). Based on a quali-
tative study by Owenz and Hall, a number of barriers have
posed as difficulties in bridging the gap:
One common stereotype in the literature is that master’s level
students are interested in
only becoming practitioners (Crane et al., 2002; Gelso, 2006).
When program decisions
about student interests are based on this stereotype, students
may not receive the inte-
grated research–practice training they desire or need, which is a
missed opportunity for
the students, their programs, and their fields in general. Results
from the present study
indicate that some master’s students are interested in research,
others are ambivalent
about it, and some were motivated to apply to doctoral
programs.(p. 29)
With varying degrees of interest in research, it is important to
support these interests, especially
encouraging those who express a desire in conducting research.
Major Trends in MFT Research
Sprenkle and Piercy (2005) identified five major trends in the
history of MFT research. The
first trend arose during the founding of the field with “soft”
research conducted by prominent fam-
ily therapy pioneers, such as Gregory Bateson, Murray Bowen,

Don Jackson, and Jay Haley
(Sprenkle & Piercy, 2005). These “researcher–clinicians,”
examined the effects of therapeutic inter-
ventions on clients and their families. In terms of methods, they
discussed their therapy sessions
with one another, reviewed audiotapes of their sessions, and
studied live sessions through a one-
way mirror (Sprenkle & Piercy, 2005).
To add more rigor to MFT research being conducted, the second
trend emerged when family
therapists began conducting quantitative and experimental
research (Sprenkle & Piercy, 2005).
Researchers began operationalizing concepts and developing
reliable and valid measures. During
this time, outcome research was gaining popularity in
comparing family therapy to other treat-
ments, such as inpatient or individually based psychotherapies.
In addition, process research was
on the rise, where researchers began studying the processes that
occur within the therapy sessions
that affect client outcomes (Sprenkle & Piercy, 2005).
While the second trend focused on quantitative research, the
third trend consisted of the con-
duct of qualitative research (Sprenkle & Piercy, 2005).
Deviating from the analysis of numbers,
family therapists instead were concentrating on the thick, rich
data that described the “subtleties
and complexities of therapy” (Sprenkle & Piercy, 2005, p. 6).
The fourth trend combined the sec-
ond and third trends together, emphasizing pluralism, accepting
a wider array of quantitative and
qualitative research. Mixed methods research—the use of both
quantitative and qualitative meth-
ods—were also become increasingly common (Sprenkle &

Piercy, 2005).
Advancement of Technology
Currently, the field of MFT is experiencing the fifth trend, “the
growing sophistication and
multiplicity of family therapy [research] methods” (Sprenkle &
Piercy, 2005, p. 6). Particularly with
quantitative research, the evolution of quantitative methods and
analyses coincides with the
advancement of technology. In more recent decades, the rapid
development of computer technolo-
gies has “revolutionized statistics and its practice” (Hand, 2009,
p. 289). As stated in Køppe and
Dammeyer (2014) on the changes in research in psychology and
child development:
With the development of technology it has become much easier
to complete advanced
statistical procedures on a large amount of data. Before the
invention of the computer it
was difficult–expensive and time consuming–to complete
complex statistical procedures
on a large amount of data. . . . Today the trend is to use more
advanced statistical analyses
due to more powerful analytic computer abilities available.
This, in turn, may demand
an increased attention to how [and which] statistical methods
are used in psychology.
(p. 332–333)
4 JOURNAL OF MARITAL AND FAMILY THERAPY January
2016

While Køppe and Dammeyer (2014) discussed the evolution of
research in psychology, similar
changes in statistical analyses also can be applied to research in
MFT. With MFT research, the
growth of technology has given rise to more sophisticated
statistical software, and in turn,
advanced quantitative analyses, such as different approaches to
prediction and regression, multi-
level growth modeling, and structural equation modeling (Miller
& Johnson, 2014; Sprenkle &
Piercy, 2005). Quantitative research approaches have grown
exponentially so that it is nearly
impossible for an individual to have competence and expertise
in all forms of MFT quantitative
research (Sprenkle & Piercy, 2005).
JOURNAL OF MARITAL AND FAMILY THERAPY
While there is acknowledgment that the field of MFT is
experiencing a rapid growth in quanti-
tative research, are these advanced methods and analyses
actually being applied and published? In
our study, we decided to examine the JMFT because of its large
influence on the field of MFT.
According to the American Association for Marriage and Family
Therapy (AAMFT):
JMFT is a peer-reviewed journal [by the association] that
advances the professional
understanding of marital and family functioning and the most
effective psychotherapeutic
treatment of couple and family distress. . . . JMFT publishes
articles on research, theory,
clinical practice, and training in marital and family
therapy.(“Journal of Marital and
Family Therapy,” 2015)

Because of its prominence in the field of MFT, it is no surprise
that previous content analyses
have been conducted on the journal, along with other leading
family therapy journals. Past content
analyses have focused on the number of trends, such as research
topics of interest, the academic
degrees and/or institutional affiliations of the authors, the
amount and type of research conducted,
trends in qualitative research publications, and trends in mixed
methods (Faulkner, Klock, &
Gale, 2002; Gambrel & Butler, 2013; Hawley & Gonzalez,
2005; Hawley et al., 2000). However,
there has not been a content analysis that specifically analyzed
the trends of quantitative research
published in JMFT, particularly in more recent years.
PURPOSE OF STUDY
The main purpose of this study was to identify the quantitative
research trends, over the past
10 years, in the JMFT. More specifically, from 2005 to 2014:
(1) Was there an increase of quantita-
tive methods used over time? (2) What trends were present in
research design methods of quantita-
tive research? and (3) What trends were present in statistical
analytic methods used in quantitative
research?
METHODS
Sample
Our sample included published articles in the JMFT from 2005
to 2014. Of the 538 articles
published in JMFT during those years, 376 were identified as

peer-reviewed articles. Of the 376
articles identified as peer reviewed, 142 were classified as
employing quantitative research methods.
Procedure
Our analysis process was conducted in multiple steps. The
authors searched through all issues
of JMFT from the years of 2005 to 2014 and excluded
obituaries, book reviews, comment/replies,
and editorials from the analysis. The remaining titles included
theoretical articles, qualitative,
mixed methods, and quantitative research. These remaining
articles were categorized as peer-re-
viewed articles. These peer-reviewed articles were used as our
remaining N to analyze how many
quantitative articles were being published out of the total
number of peer-reviewed articles.
The titles and abstracts of the remaining 376 peer-reviewed
articles were read, and 142 were
identified as containing quantitative statistical analytic
methods. In other words, 38% peer-re-
viewed articles published in JMFT during the reporting period
used quantitative research methods.
THERAPY 5
If the type of research used in the article was unidentifiable
from the title and abstract, the authors
read the methods section. As a cross-check, the authors used the
“Quantitative Study” option
under the Methodology section in PsycINFO to verify the

classification of articles.
Once we identified the articles as quantitative studies, we
followed the data analytic plan. We
recorded the type of research design and statistical analysis
used. We categorized research design
into the following: experimental design, randomized clinical
trial, survey research, observational
design, and other. We assigned each article to one category.
Experimental design included studies
where the researchers gave participates measures before the
treatment, conducted the treatment,
and then administered the measures again. Randomized clinical
trial is a design that randomly
assigns participants into an experimental group or control
group. Survey research included studies
where the researchers gave the participants surveys to complete.
Observational design researchers
used observational measures in their research design. An other
category was created for data ana-
lytic plans that were not represented by the previous categories
but were not big enough in number
to warrant a new category.
The categories for the type of statistical analyses include the
following: comparison of means,
regression, path analysis, multilevel growth modeling, measure
development, and other. These cate-
gories emerged from the data as categorically distinct methods
of statistical analyses. We combined
similar types of analyses to identify themes (Figure 1). For
example, we combined ANOVAs,
ANCOVAs, t-tests, and Mann–Whitney U-tests into the
comparison of means category. Similarly,
we placed different types of regressions, that is, hierarchical
regressions or multiple regressions,

under the category of regression. Path analysis included
structural equation modeling, exploratory
and confirmatory factor analysis, and canonical correlation
analysis, which is a similar path analy-
sis to structural equation modeling but can be used on smaller
sample sizes. Multilevel growth mod-
eling included growth curve modeling, linear growth models,
and multilevel growth models.
Measure development included analyses that used reliability and
validity to either create or test sta-
tistical measures. The other category included cost analysis,
effect size, Delphi method, and corre-
lations. The other category was created because these analyses
were not better represented by any
of the other categories yet were so few in number as to not
provide enough information to create a
new category. Creating broader categories generated larger Ns
for each category and allowed for
trends to emerge from the data.
If a study conducted one type of analysis multiple times, the
type of analysis was recorded
once. For example, if the researchers had three research
questions and conducted an ANOVA for
each question, we marked one tally for ANOVA. If a study
conducted multiple types of analyses
to answer different research questions, then each type of
analysis was recorded. For example, if
regression was used for the first research question and structural
equation modeling was used for
the second research question, then both methods were recorded.
However, we did not record any
analyses that were used as a preliminary analysis for a more
sophisticated analysis. For example, if
structural equation modeling was used, we did not include the
correlations that were conducted as

it was a required primary step for the structural equation model
test. We conducted the content
analysis this way to get a more accurate picture of how many
studies used a particular technique.
If we had tallied each time a type of analysis was used in a
particular study, those methods could
have been overrepresented in the content analysis.
Research design and statistical analysis were recorded for each
quantitative article identified.
Research design and statistical analysis were grouped into
2005–2009 and 2010–2014. Chi-square
different tests were then conducted to test for significant
changed between the time periods of
2005–2009 and 2010–2014. Chi-squares were conducted for
overall quantitative articles as well as
for each category for research design and statistical analysis
(Table 1).
RESULTS
Of a total of 376 peer-reviewed articles, 142 (38%) were
classified as quantitative research.
From 2005 to 2009, 170 articles published in JMFT were
identified as peer reviewed. Of those, 64
(38%) were classified as quantitative research. From 2010 to
2014, 206 articles were identified as
peer reviewed. Of those, 78% or 38% were classified as
quantitative research. A breakdown in per-
centages, by year, of articles identified as quantitative is
represented in Figure 1.
2016

We grouped research design procedures of the articles into the
following categories: experi-
mental design (n = 45), randomized clinical trial (n = 16),
survey research (n = 66), and observa-
tional design (n = 8). We then divided the categories in years
2005–2009 and 2010–2014 and
calculated percentages for each type of research design (Figure
2). From 2005 to 2009, out of the
total quantitative research articles (n = 64), we categorized 30%
as experimental design, 19% as
randomized clinical trials, 44% as survey research, and 8% as
observational design. From 2010 to
2014, out of the total quantitative research articles (n = 78), we
categorized 35% as experimental
design, 6% as randomized clinical trials, 49% as survey
research, 4% as observational design, and
5% other. Chi-square analysis found no significant difference
between the two time periods
Table 1
Examples of Statistical Analyses per Category
Category Examples
Comparison of Means Analysis of variance
Analysis of covariance
Multiple analysis of variance
Multiple analysis of covariance
Mann–Whitney U-test
T-test
Regression Hierarchical regression
Multiple regression
Logistical regression

Path Analysis Canonical correlation
Confirmatory factor analysis
Exploratory factor analysis
Structural equation modeling
Multilevel Growth
Modeling
Growth curve modeling
Linear growth model
Multilevel growth model
Measure Development Analyses using reliability and validity to
create or test statistical
measures
Other Cost analysis
Effect size
Delphi method
Correlation
0
10
20
30
40
50
60
70

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Quantitative articles per year (percentages)
Quantitative articles
Figure 1. Quantitative articles per year (percentages) from 2005
to 2009.
THERAPY 7
(2005–2009 and 2010–2014) for experimental design (v2 = 0.68,
p = .41), survey design (v2 = 0.35,
p = .55), or observational design (v2 = 1.04, p = .31).
Significant difference was found for random-
ized clinical trials (v2 = 4.1, p = .04).
We combined the type of statistical analyses into six categories:
comparison of means (n = 58),
regression analysis (n = 46), path analysis (n = 23), multilevel
growth modeling (n = 10), measure
development (n = 17), and other (n = 6). We grouped analytic
methods into 2005–2009 and 2010–
2014, and we calculated percentages for each method (Figure
3). From 2005 to 2009, out of the
total quantitative research articles (n = 65), 38% conducted
comparison of means, 26% used
regression analysis, 14% path analysis, 12% employed
multilevel growth modeling, 8% did mea-
sure development, and 2% other. From 2010 to 2014, out of the
total quantitative research articles
(n = 78), 44% conducted comparison of means, 37% used

regression analysis, 18% path analysis,
3% employed multilevel growth modeling, 15% did measure
development, and 5% other. Chi-
square analysis found no significant difference between the two
time periods (2005–2009 and 2010–
2014) for comparison of means (v2 = 0.54, p = .46), regression
(v2 = 1.8, p = .18), path analysis
(v2 = 0.39, p = .53), or measure development (v2 = 1.9, p =
.17). Significant difference was found
for multilevel growth modeling (v2 = 5.3, p = .02).
0
10
20
30
40
50
60
Experimental design Randomized clinical trial Survey research
Observational design
Research design (percentages)
2005-2009 2010-2014
*
Figure 2. Types of research designs (percentages) used in
articles published between 2005–2009

and 2010–2014.
Note. *p < .05.
0
5
10
15
20
25
30
35
40
45
50
Comparison of
means
Regression Path analysis Multilevel
growth modeling
Measure
development
Other
Statistical analysis (percentages)
2005-2009 2010-2014
***
*
Figure 3. Types of statistical analyses (percentages) conducted

in articles published between 2005–
2009 and 2010–2014.
Note. *p < .05; **p < .01; ***p < .001.
2016
We then grouped comparison of means and regression into one
group (n = 104) and path
analysis and multilevel growth modeling (n = 43) into another.
We did this to see whether there
was significant difference between the use of more statistically
sophisticated analysis (path analysis
and multilevel growth modeling) verses less sophisticated
analysis (comparison of means and
regression). Chi-square analysis found that there was a
significant difference between these two
groups (v2 = 52.47, p = .00001) with the less sophisticated
analysis being used more often.
DISCUSSION
In our first research question, we asked if there was an increase
in quantitative methods used
over time. Our results suggest a stable amount of quantitative
articles being published in JMFT.
Results do suggest an increase of peer-reviewed articles being
published in JMFT over the 10-year
period. From 2005 to 2009, 170 were peer-reviewed articles,
while from 2010 to 2014, 206 articles
were peer-reviewed articles. However, the percentage of
quantitative research articles being pub-
lished has remained stable over time. Both time periods had
38% of articles using quantitative sta-

tistical analysis. In other words, while the overall amount of
theoretical articles, qualitative
research, mix methods research, and quantitative research
increased over time, the percentage of
quantitative methods remained consistent.
We believe the increase in overall peer-reviewed articles
represents a positive change for our
profession. The field of family therapy can and does benefit
from a variety of publication types,
including theoretical articles qualitative and mixed methods
research. However, while there has
been an increase in overall peer-reviewed articles, there has not
been an increase in quantitative
research. This finding could be problematic in several ways.
First, as stated above, the field of fam-
ily therapy has had a movement toward evidenced-based
practice. It is important that our field
conducts quantitative research, studying these evidenced-based
practices and continuing to test
their effectiveness in community settings. Many of the readers
of JMFT are clinicians practicing
family therapy and presumably, many of those clinicians are
practicing evidence-based practices.
This lack of quantitative articles seems to give further evidence
to the researcher–practitioner gap,
where there is a misalignment between what clinicians are
practicing and what researchers are
studying.
Second, it is possible that due to the clinical focus of JMFT,
that clinicians may be less able to
interpret or use the quantitative studies in their work. If this is
the case, this may give evidence to a
gap in our master’s level training programs, where clinicians
may not being sufficiently taught how

to read and apply quantitative research.
Third, this lack of increase in publishing quantitative research
articles could indicate a gap in
our doctoral programs, where quantitative methods may not be
emphasized or fully integrated
into research training. Likewise, they may not be fully
embraced by doctoral students, who ulti-
mately choose between using quantitative and qualitative
methods in their own future research.
Researchers in the field of family therapy should be working
toward empirically validating the
foundational theories of our field. To do this, generally requires
using quantitative research meth-
ods to empirically test the efficacy of the field’s theories and
practices. While some research exists
in this area, our results seem to indicate that there is room for
growth. In order to continue to
strengthen the field of family therapy, it is essential that our
training programs, both on the mas-
ter’s and doctoral-level, make understanding, implementing, and
conducting quantitative research
a priority.
In our second research question, we asked what trends were
present in research design meth-
ods of quantitative research. As seen from the content analysis,
data were more commonly col-
lected through experimental design (30% from 2005 to 2009;
35% from 2010 to 2014) and survey
research (44% from 2005 to 2009; 49% from 2010 to 2014) than
randomized clinical trials (19%
from 2005 to 2009; 6% from 2010 to 2014) and observational
design (8% from 2005 to 2009; 4%
from 2010 to 2014) over the past 10 years.

Based on our chi-square analyses, the only significant change
over the years was a decrease in
randomized clinical trials. It is possible that fewer randomized
clinical trials are being conducted
because of the concerns over generalizability to broader
populations. As stated by Dattilio, Piercy,
and Davis (2014):
THERAPY 9
We suspect that family therapists are much like professionals in
other fields in the reasons
they place relatively little emphasis on randomized clinical
trials. For one thing, many feel
that what goes on in a laboratory setting comes nowhere close
to capturing what actually
occurs in a clinician’s office. Often, “therapists” in these
research studies are doctoral stu-
dents or neophytes who, although they work under supervision,
still have very little clini-
cal experience. . . .Treatments delivered during the course of
randomized clinical trials
may differ from treatment provided in a routine clinical setting
because protocols may be
artificially constrained to what a therapist does and when the
therapist does it.(p. 7)
So, the lower proportion of randomized clinical trials compared
to other types of research design
may reflect concerns around the researcher–clinician gap
(Dattilio et al., 2014). Instead, other
research design methods are being used, such as experimental

designs and survey research, which
may encompass more diverse samples. By collecting data from
more diverse samples, the results
may have greater external validity and thus be more
generalizable and applicable to client popula-
tions’ that marriage and family therapists may actually see.
Although experimental design and sur-
vey research were not have significantly different between
2005–2009 and 2010–2014, it may be
more important to note that the results did not show a
significant decrease in these research
designs. Instead, the results indicate that the use of
experimental design and survey research was
stable across both time periods. This may suggest that
researchers, and clinicians, find utility in
these research designs over randomized clinical trials and
observational design. However, as obser-
vational methods develop, and as more research sites pool their
resources, it may be that we will
see an increase in both these research methods.
Survey research was found to be used more than other research
designs. When looking at the
growth over time, both experimental design and survey research
increased by five percent. Due to
the need for a large sample size for the more sophisticated
quantitative analysis, the use of survey
research seems to be an understandable trend to increase the
feasibility of using certain analyses.
In our third research question, we asked what trends were
present in statistical analytic meth-
ods used in quantitative research. Overall, the most common
type of analysis used was the compar-
ison of means (n = 58), regression analysis (n = 46), path
analysis (n = 23), measure development

(n = 17), and multilevel growth modeling (n = 10). The only
statistically significant change over
time was the decrease in the use of multilevel growth modeling.
The use of comparison of means,
regression, path analysis, and multilevel growth modeling did
not significantly change over time.
However, when grouped together, comparisons of means and
regression were used more often over
the last 10 years than more sophisticated analysis of path
analysis and multilevel growth modeling.
These results seem to indicate that while more sophisticated
analysis are available to answer
research questions, they are being underutilized in family
therapy research.
Our findings show no differences in any statistical analyses,
except for a decrease in one of
the more sophisticated analyses. These findings are troubling
for several reasons. As statistical
analyses become more sophisticated, this allows for an increase
in our ability as researchers to
study certain phenomena in new ways. For example, path
analyses, such as structural equation
modeling, allow us to create and test models that could
potentially add to our understanding of
basic family therapy concepts, such as differentiation (Parker &
Priest, under review). Similarly,
growth curve modeling can help the field move forward in
process research by deepening our
understanding of potential trajectories of client process in
therapy (Pinsof & Wynne, 2000).
However, based on our results, we are not seeing these kinds of
innovations being fully imple-
mented in the JMFT. One possible reason could again be related
to our training programs,
where students are not being taught how to conduct path

analysis or multilevel growth model-
ing. Another reason could be that researchers are using these
more sophisticated analyses but
are not framing their research question in a way that is
applicable to clinicians and therefore are
not getting accepted into JMFT.
One possible reason multilevel growth modeling specifically is
not being used more could be
related to feasibility. While both structural equation modeling
and multilevel growth modeling
require large sample sizes, multilevel growth modeling requires
an additional requirement of longi-
tudinal data with at least three time points. It is possible that
the lack of availability to large longi-
tudinal data sets could explain the discrepancy of use between
multilevel growth modeling and
2016
structural equation modeling. Another possible explanation
could be that while the precedent for
looking at trajectories of growth has been set in other fields
(i.e., child development), it has not
been set in MFT. For MFT researchers, it could be important to
encourage the use of multilevel
growth modeling, in order to better understand change patterns
over time.
While our results have shown trends in research design and
statistical analyses that were statis-
tically significant as well as not significant, it is beneficial to
examine all of the results of the content

analysis. Not only do our results provide insight into how the
trends of quantitative research have
changed over the past 10 years, but they also appear to reflect
the researcher–practitioner gap that
still needs to be addressed. In addition, with advances in
technology that allow researchers to con-
duct statistical analyses more sophisticated than ever before, it
is imperative that MFT research
continue to strengthen its research methods to move forward as
a field. We hope that the results of
this content analysis may serve as a catalyst toward this end.
Limitations
We would like to point out several limitations in this study. One
limitation was the content
analysis was conducted with just one family therapy journal,
JMFT, and therefore, reported trends
from one journal. As numerous researchers compete to be
published in JMFT, not all of the
methodologically sound quantitative studies submitted can be
accepted into the journal (Piercy,
personal communication, 2015). Thus, it is possible that the
trends presented in the content analy-
sis may not accurately reflect the overall trends in research
design and data analytic methods across
all quantitative research being conducted in the field of MFT. It
may be fruitful to examine the
trends of quantitative studies across other family therapy
journals to gain a more accurate picture
of quantitative trends in family therapy research.
Although there were limitations to the study, the content
analysis provided useful information
about the directions in MFT research, particularly quantitative
research. It will be interesting to

see how MFT research continues to evolve over the next 10
years and how the field addresses the
researcher–practitioner gap as well as how technology and
statistical analyses continue to be
refined.
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2016
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Unit 5 ip
Written assignment on strategies for classifying and treating
offenders (10-12 pages)
Based on the seven (7) types of offender
rehabilitation treatments:
· Psychological
· Behavior
· Social
· Educational/vocational
· Substance abuse
· Sex offender
· Religious
How should offenders and treatment needs be determined and
classified?
These programs have been proven to assist numerous offender
populations, but there are no clear guidelines that address how

or when they should be implemented within correctional
institutions. In this assignment, students will select 5 of the 7
types of rehabilitative treatments to analyze and discuss.
Assignment Guidelines
· Select five (5) of the rehabilitative treatment types from the
list above.
· Write 5-6 pages that address the following:
· The name and purpose of each selected rehabilitative
treatment
· The scope of the treatments
· What conditions or problems do the programs aim to treat?
· What are the limitations of the programs?
· What type(s) of offenders should be treated?
· How you classify offenders and treatment needs?
· The effectiveness of the programs within your rehabilitative
treatment categories
· What types of offenders are ideal candidates for these types of
treatments?
· Be sure to use examples to support your explanation.
In 3-4 additional pages, discuss the impact that one of your
selected rehabilitative treatments has had on the field of
corrections and society's view of offender rehabilitation within
the last 10 years

A CONTENT ANALYSIS OF QUANTITATIVE RESEARCHIN JOURNAL OF MAR.docx

A CONTENT ANALYSIS OF QUANTITATIVE RESEARCHIN JOURNAL OF MAR.docx

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A CONTENT ANALYSIS OF QUANTITATIVE RESEARCHIN JOURNAL OF MAR.docx