Trend Analysis
Sally Mohamed, 5 August 2011
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
Purpose
The purpose of this paper is to assess the value of trend analysis based on a set of criteria and an
experimental case. The criteria I have selected to evaluate this method are the following:
1.
2.
3.
4.
5.
Easy to understand
Applicability to question
Number of sources cited
Examples used to convey the idea
Expertise level in field
I am using these criteria because trend analysis bases its estimations on historical data and
patterns over time. By using these criteria, the analytical question can be examined for this
specific question. The analytical question I will use to test this method is “How likely is it that
the Democrats will win the 2012 Presidential Election?
I will collect my data for the analytical question using online sources, such as lexis nexus for
newspaper issues, financial sources, political web sites, and web blogs that have information
about previous presidential elections. I will use library resources to find data on trend analysis.
Trend analysis is a great tool for examining past data and forecasting the likelihood that the same
patter will occur in the future. It can’t tell you why that pattern or even happened. It can tell you
when and how often it happened. This can be useful when trying to make forecasts such as for
the weather, the economic cycle or even elections. For my test case, trend analysis works really
well with the data that has already been collected by other sources. It was easy to examine that
data and combine it to create a rich source of data that covered many areas of the social world
which may or may not influence elections in the United States.
Review of the Literature
Past research using trend analysis on a wide variety of research topics shows a high confidence
in this technique’s ability to predict future reoccurrences of the patterns under study. They’re
lack of confidence in the ability for it to show cause of the events is also shared. The method
they employ to gain that predictive knowledge of future occurrences is different.
There is debate within the articles used in this research on whether the use of graphs without
hard statistics is a valid way to assess trends. All of the seven articles’ authors believe graphs are
a useful way to visualize the data points. Some believe that this alone is enough to accurately
predict trends and do not include statistical based methodologies to further their explanation of
trends. Some, however, base their trend analysis on statistical methodology, like regression
analysis, before implementing graphic representations of their data.
Each article has key points that are slightly different than the other articles. Feng-Shang, et al.
believe that trend analysis is a very useful tool to use when you have short time frames in which
you want to analysis a trend within. They warn, however, that some results can be misleading.
John Ely MD, et al. believes that trend analysis is a good tool to use to find out if something
needs further investigation, but does not believe that you can forecast future trends. Rather, you
can see what’s happened in the past. The Texas State Auditor’s Office emphasizes the belief
that results one gets from trend analysis can be misleading because of the way items are recorded
over time may have changed. Therefore, what one is measuring longitudinally may not be
accurate. However, within that, they assert that one can spot changes over time through this
technique.
Patrick Dattalo, Earl Babbie and John Snyder have a broader overview of trend analysis in the
articles they produced. Snyder claims that trend analysis can tease out important information
about things that are not normally studied. With that however, trend analysis can be riddled with
human error whether it is poor record keeping or lack of understanding of the way items are
defined over time. Dattalo purports that trend analysis can simply complex pieces of information
and make comparisons of data within graphs easy to understand. As well, he believe that any
missing data can have an impact on the accuracy of the interpretation of your trend. Babbie
focuses on the expense of trend analysis and claims that the use of human recall can taint the data
because recall after an event can be different or changed by the human brain.
Overall, all of the authors’ stress that trend analysis cannot tell you the cause or reason
something happened. It can only tell you that it did happen and with some degree it can tell you
when it will likely happen in the future. Prediction is debatable among the authors in the sources
selected for this research.
Description of Trend Analysis
Trend analysis is a proven scientific method to assess patterns over time. It is one of several
longitudinal types of studies. A longitudinal study is a study that makes observations over an
extended period of time (Babbie, 1995). This method can be used in a wide range of fields of
study and everyday situations as a methodology for data analysis. Whether a person wants to
understand a retail sale cycle or determine what time of year a particular city is safest to visit,
trend analysis can be a very useful method to make those types of forecasts.
Through the selection of a beginning point and an end point, observations can be collected as
they occur in time rather than rigidly in interval points. Due to that, trend analysis is more
flexible in regards to data collection. However, it has the potential to be a very expensive
method to undertake. Collection of data over a long period of time requires resources to not only
collect the data, but to store and analyze it as well. Also, it cannot tell you the reason something
happened, just that it did and it happened at different intervals within time.
In trend analysis, an analyst’s observations can be viewed graphically or computed
mathematically to discern any specific patterns. If an analyst has very few observations, a
statistical computation may not be the best way to discern a pattern. Graphically, data can be
2
understood more easily. Graphics can show where data points are clustered, how data points
relate to each other over time and specific points in time where spikes occur.
Strengths of Trend Analysis
The following are strengths of trend analysis that are commonly provided by the sources used
to understand this methodological technique.





This technique allows one to approximate conclusions about something over time in
order to make a forecast for the future.
It allows a complex question or pieces of information to be simplified.
Graphic illustrations of trend data allow comparisons of data understandable.
It can expose areas that need further investigation, such as outliers that skew the results
Logical inferences can be made from trend data.
These strengths have been found through experience of using this technique in my daily work as
well as learning about them through numerous research methods courses.
Weaknesses of Trend Analysis
The following are weaknesses of trend analysis that are commonly provided by the
sources used to understand this methodological technique.





An issue with this technique is that it cannot tell you why something happened, but that it
has occurred over time.
The results can be misleading if the way items are operationalized over time have
changed.
Depending on the length of the study, it can be an expensive form of data collection
Human recall can change over time and change a person’s recall on events, which may be
different than what actually happened.
Statistical analysis in trend analysis can be complicated and missing data can have an
impact on accurate trend interpretation.
These weaknesses have been found through experience of using this technique in my daily work
as well as learning about them through numerous research methods courses.
Steps involved in proper Trend Analysis
Trend analysis is one of the easier methodologies one can use to assess information over time. It
can be useful if one needs to forecast what might occur in the future. It cannot tell you the cause
of a problem or situation. It can tell you the likelihood of when the situation may arise again and
how often it has happened in the past. It can tell you if things happen in seasonal cycles, annual
cycles or other time driven patterns.
The following steps will guide you through a simple trend analysis. It will give you the basic
steps one needs to have a successful trend analysis study.
3
1. Step #1: Assess the question
You must define the question. You need to be specific about what it is you want to asses.
2. Step #2: Define your time line
You need to set a boundary of time that your data points will fall into. Select your starting point
and your end point. For example, you chose a twenty year period from 1950 to 1970.
3. Step #3: Research your item of inquiry
Due to societal changes over time, language and meaning of words often change. Be certain that
your item of inquiry is defined the exact same throughout your time line. For example, do not
assume that a baseball cap in the 1950s is called a cap in the 1970s.
4. Step #4: Collect your data
You’ll want to begin collecting information about your item of inquiry and store it in a program like
Excel. Storage of data in one place makes it easier to visually see your data and make
assessments of trends.
5. Step #5: Graph your data
If you’re using a program that has graphics, it will be easy to create graphs with the data that you
have inputted. With Excel, you’ll want to use the graph function by highlighting your data and
selecting the type of graph you want from the graphics menu.
6. Step #6: Statistical analysis
For more complex items of inquiry, statistical analysis may be necessary. You’ll want to use a
statistical package such as SAS or SPSS to run regression analysis on your data. If you aren’t
familiar with statistical analysis or the use of statistical programs, hiring a statistician would be
7.
wise.
Step #7: Resulting analysis
You’ll want to look at your graphs or statistical output to assess what type of trend exists in your
data. It might lead you to investigate a particular area further.
4
For Further Information:
Dattalo, Patrick. 1998. Time Series Analysis: Concepts and Techniques for Community
Practitioners. Journal of Community Practice. 5(4), 67-85.
Ely, John W, MD, MSPH, Jeffery D Dawson, ScD, Jon H. Lemke, PhD, and Jon Rosenberg,
MD. 1997. An introduction to Time-trend analysis. Statistics for Hospital Epidemiology. 18(4),
267-274.
5
Texas State Auditor’s Office. 1995. Data Analysis: Analyzing Data – Trend Analysis.
Methodology Manual. www.sao.state.tx.us/resources/manuals/method/data/data-toc.html
Personal Test
The use of trend analysis to access the likelihood that the democrats would win the 2012 election
was a good technique to use. I was able to look at multiple historical data points to see how
elections are affected or not affected by certain things, such as the state of the economy, the
approval rating of the current president, the number of party members have been in office and
what happens when on party holds the presidency and the other controls Congress.
Like the articles I read about this technique, it cannot tell you why one person wins an election or
why they lose. It just tells you which party is more likely to win based on historical data. The
lesson learned from this technique is there is information and there’s good information. Once
you get the data into plot points, it’s easier to tell which data will give you the best information.
However, because you’re looking at trends, too much data can be overwhelming and confusing.
It’s best to select 1 criterion instead of multiple criteria to track a trend.
The steps involved in determining the likelihood that the democrats would win the 2012 election
were the basic steps in trend analysis. First I collected the information that would determine
what might impact an election. I gathered data from historical records that were available online.
I sorted the information based on relevancy and accuracy. I examined the charts that had already
been made about the data from the sources in which I retrieved the information. I analyzed those
charts to make an assessment of the trend.
The criteria I used to evaluate this method are the following:
1.
2.
3.
4.
5.
Easy to understand
Applicability to question
Number of sources cited
Examples used to convey the idea
Expertise level in field
Overall, this method was very easy to understand, plotting points on a graph or examining data
that was already graphed by someone else using trend analysis. While there are some aspects
that require more statistical knowledge, the use of statistical methods in trend analysis is not
necessary in cases where the information is not quantifiable or if the data is in graphic form
already. Trend analysis was very applicable to the question in my personal use case. When
assessing which technique to use for this question, trend analysis stood out as the best fit because
future forecasting of a presidential election is all based on historical knowledge and data.
Trend analysis is a widely used tool in data analysis. There were numerous examples on how to
apply this technique. Most of the experts on trend analysis for this paper were in statistical
analytical fields. However, some were in the medical fields. It makes sense that both of these
fields use trend analysis because so much of, for example medicine, their data varies across time.
6
There were not any roadblocks or challenges that I encountered using this technique. As far as
forecasting how likely the Democrats will win the 2012 election, the data collected for this
analysis shows there is high likelihood that the Democrats will win the 2012 election.
Analytical Data
The data used to assess whether the likelihood the Democrats will win the 2012 election is listed
on the next page.
7
Data Table
Trend Analysis of Data Related to the 2012 US Presidential Election
How likely is it that the Democrats will win the 2012 Presidential Election?
Estimated
Data
Data Date
Source Name or Location
Reliability of
Other Comments
Data/Source
1948 Democrats
President,
Republicans control
Democrats
won the next
election
July 15,
2011
http://fivethirtyeight.blogs.nytimes.com
Medium
Credibility
This blog discusses the
impact the economy has on
past presidential elections
Democrats
won the next
election
July 15,
2011
http://fivethirtyeight.blogs.nytimes.com
Medium
Credibility
This blog discusses the
impact the economy has on
past presidential elections
Democrats
won the next
election
July 15,
2011
http://fivethirtyeight.blogs.nytimes.com
Medium
Credibility
This blog discusses the
impact the economy has on
past presidential elections
Democrats
won the next
election
July 15,
2011
http://fivethirtyeight.blogs.nytimes.com
Medium
Credibility
This blog discusses the
impact the economy has on
past presidential elections
Democrats
won the next
election
July 15,
2011
http://fivethirtyeight.blogs.nytimes.com
Medium
Credibility
This blog discusses the
impact the economy has on
past presidential elections
More
democrats
have held
office
January
28, 2009
http://www.apples4theteacher.com/holidays
/presidents-day/past-presidents-of-usa.html
Low
Credibility
This is an educational site
for children in grammar
school
congress
1960 Republican
President,
Democrat control
congress
1976 Republican
president,
Democrat control
congress
1992 Republican
president,
democrats control
congress
2008 Republican
President,
Democrat Control
congress
19 Democrats
16 Republicans
4 Whigs
1 Federalist
Stock market
always does
poorly the first
year of a term and
does well the 3rd
year.
Unemployment
rate rises after
republicans leave
office and go
down during
democrat term
Presidential
approval ratings
go down during
their term
No real
effect on the
election
December
10, 2010
http://www.nytimes.com/2010/12/11/busi
ness/economy/11charts.html
Medium
Credibility
This article examines how
the stock market changes
depending on the year of
the presidential term
Speculative
effect on
election
January
22, 2009
http://macroblog.typepad.com/macroblog/2
009/01/a-look-back-at-the-economy-inpresidential-terms.html
Medium
Credibility
This article does a good job
of presenting data in charts
No real
effect on
elections
July 7,
2009
http://www.usatoday.com/news/washington/ High
presidential-approval-tracker.htm
Credibility
This company has a high
reputation for the data they
produce
9
Annex 1. Detailed Literature Review
The following is a critique of the literature reviewed for this project. It contains a review of the
articles, a critique of the articles, information about the authors, a comparison of the articles and
any references they cited in their article.
Source Article 1:
Dattalo, Patrick. 1998. Time Series Analysis: Concepts and Techniques for Community
Practitioners. Journal of Community Practice. 5(4), 67-85.
Technique: Trend Analysis
Description of Purpose:
The purpose of this article was an information and teaching tool. The author laid out the steps
involved in time series analysis and provided a real world example on how it can be used.
Strengths and weaknesses of method:
The following are strengths listed by the author:
Strengths
 The sampling can show important patterns
 One can determine a trend with as little as 3 data points
 Graphics in trend analysis is easy to understand
 Trend graphs aid in making comparisons of data understandable
 It can simplify the most complex pieces of information
The following are weaknesses listed by the author:
Weakness





Missing data can have an impact on accurate trend interpretation
Statistical analysis in trend analysis can be complicated
It’s often recommended that between 100 and 200 data points be collected which might
be more than someone has
It assumes that the trend will continue in the future
It is not based on probability sampling
Description of how to apply the method:
In order to do time series analysis, you have to identify the trend in the data. You plot points in a
graph with the data in order to see the trend in the data. If you have a lot of data points, it is best
to find the mean in a series of points to narrow down the data in to a more workable set. Once
you identified the trend, you should look at seasonal variation. Seasonal variation can aid you in
determining if the trend is more likely to occur in certain point in time repeatedly.
Types of problems it can be applied to:
This technique can be used to forecast almost anything if you have data which is longitudinal. If
you have several months or years of data, you can discern a trend.
Comparison to other articles in the literature review:
N/A
What I found most informative:
I thought the way that he laid out his article of describing time series analysis first and then
showing how to apply the technique in a real world scenario was an excellent way of explaining
a complex methodology.
Who is the author: Patrick Dattalo is an associate professor at Virginia Commonwealth
University in the field of Social Work. He’s written several books and articles on sampling and
methodology.
Source reliability: Very High Credibility
Article Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
June 19, 2011
Other sources this source cited:
Armstrong, J.S. 1985. Long-range forecasting from crystal ball to computer. NY: John Wiley.
Barlow, D.H. & Hersen M. 1973. Single case experimental designs: use in applied clinical
research. Archives of General Psychology, 29, 319-325.
Bloomfield, P. 1976. Fourier analysis of time series: an introduction. NY: John Wiley & Sons.
Box, G.E.P., & Jenkins, G.M. 1970. Time-series analysis: forecasting and control. San
Francisco: Holden-Day.
Chatfield, C. 1984. The analysis of time series: an introduction. London: Chapman and Hall.
Crosbie, J., & Sharpley, C.F. 1989. DMITSA-A simplified interrupted time-series analysis
program. Behavior Research Methods, Instruments, and Computers, 21, 639-642.
Gottman, J.M. 1981. Time-series analysis: a comprehensive introduction for social scientists.
New York: Cambridge University Press.
Greenwood, K.M. & Matyas, T.A. 1990. Problems with the application of interrupted time series
analysis for brief single-subject data. Behavioral Assessment, 12, 355-370.
Harrop, J.W. & Vellicer, W.F. 1985. A comparison of alternative approaches to the analysis of
interrupted time-series. Multivariate Behavioral Research, 20, 27-44.
Harvey, A.C. 1990. The econometric analysis of time series. Oxford: Philip Allan.
Hill, G., & Fildes, R. 1984. The accuracy of extrapolation methods: an automatic Box-Jenkins
package sift. Journal of Forecasting, 3, 319-323.
Hoff, J.C. 1983. A practical guide to Box-Jenkins forecasting. Belmont CA: Lifetime Learning.
Kendall, M.G. & Ord, J.K. 1990. Time series. London, England: Edward Arnold.
Miller, G. 1986. Investing public funds. Chicago, Ill: Government Finance Officers Association.
Mills, T.C. 1990. Time series techniques for economists. Cambridge, MA: Cambridge
University Press.
11
Naxim, S. M. 1988. Applied time series analysis for business and economic forecasting. NY:
Marcel Dekker.
Ostrom, C.W. Jr. 1978. Time series analysis: regression techniques. Beverly Hills, CA: Sage
Publishing.
Patton, C.V. & Sawicki, D.S. 1986. Basic methods of policy analysis and planning. Englewood
Cliffs, NJ: Prentice-Hall.
Poister, T.H., McDavid, J.C., & Hoagland, A. 1979. Applied program evaluation in local
government. Lexington, MA: Lexington Books, DC Health and Company.
Teasley, C.E. 1989. When a picture is worth more than a thousand words: Graphic versus
algebraic sensitivity analysis. Evaluation Review, 13, 91-103.
Wheelwright, S.C. & Makridakis, S. 1980. Forecasting methods for management. New York,
NY: John Wiley & Sons.
Source Article 2:
Babbie, Earl. 1995. Research Design. The Practice of Social Research. New York: Wadsworth
Publishing Company, 95-99.
Technique: Trend Analysis
Description of Purpose:
The purpose of this chapter was an information and teaching tool. The author discussed many
different research designs and focused on trend analysis in a few pages of the chapter.
Strengths and weaknesses of method:
The following are a list of strengths mentioned by the author.
Strengths




Provides information across time
Can draw approximate conclusions about things over time
Graphics in trend analysis is easy to understand
Logical inferences can be made
The following are a list of weaknesses mentioned by the author.
Weakness



Can be expensive form of data collection
Requires a longer length of time
Time can change a person’s recall on events
Description of how to apply the method:
Trend analysis is a longitudinal type of study. It does not matter if the researcher pulls data
when the events occur or reaches back and looks at historical data. The observations captured
may or may not be a true account of the event one is looking at.
Types of problems it can be applied to:
This method can be used for various types of data that needs to be looked at as a trend. It is a
part of a larger set of methodologies that are used to determine intervals of occurrences over
time.
12
Comparison to other articles in the literature review:
This author simplified the method of trend analysis in a few pages of text. He did not have a
detailed and descriptive example on how to use trend analysis as Patrick Dattalo did.
What I found most informative:
I thought the way that he laid out his article of describing time series analysis first and then
showing how to apply the technique in a real world scenario was an excellent way of explaining
a complex methodology.
Who is the author:
Earl Babbie is professor emeritus at Campbell University in Sociology. He has published
numerous books on research methodology, which are used to teach research methodology across
the world of universities.
Source reliability: Very High Credibility
Article Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
June 25, 2011
Other sources this source cited:
Bart, Pauline, and Linda Frankel. 1986. The Student Sociologist Handbook. Morristown, NJ:
General Learning Press.
Casley, D.J. and D.A. Lury. 1987. Data Collection in Developing Countries. Oxford: Clarendon
Press.
Cooper, Harris M. 1989. Integrating Research: A Guide for Literature Reviews. Newbury Park,
CA: Sage.
Hunt, Morton. 1985. Profiles of Social Research: The Scientific Study of Human Interactions.
New York: Basic Books.
Iversen, Gudmund R. 1991. Contextual Analysis. Newbury Park, CA: Sage.
Menard, Scott. 1991. Longitudinal Research. Newbury Park, CA: Sage.
Miller, Derbert. 1991. Handbook of Research Design and Social Measurement. Newbury Park,
CA: Sage.
Source Article 3:
Texas State Auditor’s Office. 1995. Data Analysis: Analyzing Data – Trend Analysis.
Methodology Manual. www.sao.state.tx.us/resources/manuals/method/data/data-toc.html
Technique: Trend Analysis
13
Description of Purpose:
The purpose of this chapter was an informative type of article. It laid out the information of
needed to understand trend analysis. It included an example of a simplistic form of trend
analysis of watching fund equity grow over time.
Strengths and weaknesses of method:
The following is a list of strengths mentioned in the article.
Strengths




Spot changes over time
Is easy to understand
Can be used in a wide variety of items to be analyzed
Expose areas that need further investigation
The following is a list of weaknesses mentioned in the article.
Weakness



Cannot tell you why something happened
Results can be prejudiced by base points
Can be misleading if the way items are recorded are changed over time
Description of how to apply the method:
Trend analysis is useful when you want to forecast the future or visualize expected occurrences.
One must have historical data collected in order to do this type of analysis.
Types of problems it can be applied to:
It can be applied to anything that you want to trend.
Comparison to other articles in the literature review:
In comparison to the other two articles, this one is a very cut and dry type of piece. It’s very
succinct and offers no references.
What I found most informative:
The article restated what the other two articles described about this technique. There was not
any new information gleaned from this article. However, the example used was the most easily
understood and graphically pleasing of the three articles.
Who is the author:
The author of this paper is unknown. The Texas State Auditor’s Office published this.
Source reliability: Low Credibility
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
July 2, 2011
14
Other sources this source cited:
None
Source Article 4:
Ely, John W, MD, MSPH, Jeffery D Dawson, ScD, Jon H. Lemke, PhD, and Jon Rosenberg,
MD. 1997. An introduction to Time-trend analysis. Statistics for Hospital Epidemiology. 18(4),
267-274.
Technique: Trend Analysis
Description of Purpose:
This article discussed the purpose of using time-trend analysis with healthcare data. Time trend
analysis is one type of trend analysis which is better suited for particular data than others. It
presented information for informative purposes rather than teaching. It discussed statistical
methodology that should be used with healthcare data.
Strengths and weaknesses of method:
The following are a list of strengths listed by the author.
Strengths


Good tool to use to determine if further investigation needs to be done
Statistically sound
The following are a list of weaknesses listed by the author;
Weakness



Cannot tell you the cause of the trend
It cannot forecast future trends
Fails to see anything other than linear trends
Description of how to apply the method:
This method when used with statistical analysis can tell a researcher whether or not the particular
trend or trends have statistical significance. When looking at data you must assume that the data
is linear and no deviation occurs. You apply assumption-free thinking to the data, which means
you don’t assume you know the cause of the data. Then you plug your data points into statistical
calculations such as regression analysis to test the rate of change over time. After you run
statistical analysis and identify areas that need more investigation, you need to determine if the
trend was legitimate or due to change.
Types of problems it can be applied to:
This type of trend analysis is best used on data that is linear and has no deviations.
Comparison to other articles in the literature review:
This article has some good information about trend analysis as a general topic. However, time
trend analysis, as discussed in this article, is not good at forecasting future trends. The other
articles did not cover this specific type of trend analysis. So, it was a good article to review
15
against the others to see how trend analysis can be applied to a wide variety of topics in different
ways.
What I found most informative:
It helped clarify the difference between time-series trend analysis and time trend analysis, which
can often be confused.
About the author(s):
John W. Ely, MD, MSPH is a professor at the University of Iowa Healthcare. Jeffrey D Dawson,
ScD, is a professor at the University of Iowa in the department of Biostatistics. Jon H. Lemke,
Ph.D. is the Chief of Biostatistics for the Genesis Health System in Davenport, Iowa. Jon
Rosenberg, MD is chief of the Healthcare-Associated Infections (HAI) Program for the State of
California, Department of Public Health, Center for Health Care Quality.
Source reliability: High Credibility
Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
July 10, 2011
Other sources this source cited:
Birnbaum, D. 1984. Analysis of hospital infection surveillance data. Infect Control. 5,332-338.
Childress, JA, Childress, JD. 1981. Statistical test for possible infection outbreaks. Infect
Control. 2,247-249.
Freeman, J, McGowan JEJ. 1981. Methodologic issues in hospital epidemiology, I: rates, case
finding, and interprestation. Rev Infect Dis, 3, 658-667.
Kleinbaum, DG, Kupper, LL, Muller KE. 1988. Applied Regression Analysis and Other
Multivariable Methods. 2nd ed. Belmont, CA: Duxbury Press.
Sellick, JA Jr. 1993. The use of statistical process control charts in hospital epidemiology. Infect
Control Hosp Epidemiol. 14, 649-656.
Stroup, DF, Thacker SB., Herndon, JL. 1988. Application of multiple time series analysis to the
estimation of pneumonia and influenza mortality. Stat Med. 7,1045-1059.
Source Article 5:
Wu, Feng-Shang, Chun-Chi Hsu, Pei-Chun lee, and Hsin-Ning Su. 2011. A Systematic
Approach for Integrated Trend Analysis – The Case of Etching. Technological Forecasting &
Social Change 78:386-407.
Technique: Trend Analysis
16
Description of Purpose:
This article gives a good informative example of trend analysis with a research question. It
shows how the use of graphs can explain where the trends are. It also introduces a new type of
analysis, text mining analysis, which was developed recently.
Strengths and weaknesses of method:
The following are strengths of the technique according to the authors:
· It gives quantifiable data points
· It is very accurate for short time frames
The weaknesses of the technique the authors cite are listed below:
· It requires you data to be good and plenty
· Forecasts made from trend analysis may be misleading
· It doesn’t explain the causal portion of the question
Description of how to apply the method:
Like the other articles, this article gave a broad over view of how to do trend analysis. According
to the authors, for looking at technology, one would first look at your topic from the macro level.
Then you would take a look at some specific places that your data would be stored. Then you
would do some sort of data mining to find the information you’re looking for. Then you pull the
data points out and graph them. Your analysis would include graphs of the data as well as some
statistical models to help support your graphs.
Types of problems it can be applied to:
They believe that trend analysis is the best type of technique to use with examining technological
advancement.
Comparison to other articles in the literature review:
This article was full of charts, more so than any of the other articles. It did not use statistical
analysis to make any conclusions. Rather the graphs the authors used to illustrate the answer to
their research question was far different than the few graphs in the other articles. Unlike the
others, these graphs could stand on their own.
What I found most informative:
This article really put the application of trend analysis to use through their look at etching. It’s
not something that I would normally read about. So the information contained in the article was
informative on applying trend analysis on something like etching.
About the author(s):
Feng-Shang Wu, PhD, teaches at the Graduate Institute of Technology and Innovation
Management at the National Chengchi University. Chun-Chi Hsu has a MBA and a MS in
computer science. Pei-Chun Lee is a graduate student under Feng-Shang Wu, PhD. Hsin-Ning
Su, PhD is an associate researcher at the National Applied Research Laboratories in Taiwan.
Source reliability: High Credibility
17
Article Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
July 16, 2011
Other sources this source cited:
Bright, JR 1968. Technological forecasting for industry and government: methods and
applications. Prentice-Hall: NJ.
Cetron, MJ., T.I. Monahan. 1993. An evaluation and appraisal of various approaches to
technological forecasting. Technological forecasting for industry and government:
methods and applications. Prentice-Hall: NJ. Pp144-182.
Martino, J.P. 2003. A review of selected recent advances in technological forecasting.
Technological Forecasting & Social Change 70:719-734.
Martino, JP. 1993. Technological forecasting for decision makers. McGraw Hill: NY.
Porter, A.L., A.T. Roper, T.W. mason, F.A. Rossini, J Banks. 1991 Forecasting and management
of technology. Wiley-Interscience.
Source Article 6:
Snyder, John E. 2001. Trend Analysis of Medical Publications about LGBT Persons: 1950-2007.
Journal of Homosexuality 58(2):164-188.
Technique: Trend Analysis
Description of Purpose:
This article is an example of how trend analysis is done with medical publications.
Strengths and weaknesses of method:
The following are strengths of the technique according to the author:
 It gives quantifiable data points
 Can tease out important information about things that are not studied
The weaknesses of the technique the author cite are listed below:
 Human error
 It doesn’t explain the causal portion of the question
Description of how to apply the method:
Similar to the other articles, this one gave a broad over view of how to do trend analysis through
the use of actual data examination. It did not provide detail about the steps involved in
performing trend analysis. It did provide quite a lot of graphs to show the data they collected.
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Types of problems it can be applied to:
For this author, while he saw the greatest limitation to trend analysis is human error, he believes
this can be used to find areas of research that are missing or overlooked.
Comparison to other articles in the literature review:
This article was the least informative about the strengths and weaknesses compared to the other
articles. As well, it doesn’t include the steps he took to do the trend analysis. Rather, he does a
good job of showing how graphs can be used to understand data and make inferences.
What I found most informative:
I didn’t really find anything informative about trend analysis that hasn’t already been covered in
the other articles I’ve read.
About the author(s):
Dr. John E. Snyder is a faculty member at the University of North Carolina School of Medicine
at Chapel Hill.
Source reliability: High Credibility
Article Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
July 23, 2011
Other sources this source cited:
APAHelp Center. (2008), Sexual orientation and homosexuality. Retrieved May 27, 2008,from
http://www.apahelpcenter.orglarticles/article.php?id=31
Bailey, M., Pillard, R. C., Neale, M. c., & Agyei, Y. (1993). Heritable factors influence sexual
orientation in women. Archives of General Psychiatry, 50(3), 217-223.
Baron, M. (1993). Genetic linkage and male homosexual orientation: reasons to be cautious.
British Medical journal, 307(6900), 337-338.
Black, D., Gates, G., Sanders, S., & Taylor, L. (2000). Demographics of the gay and lesbian
population in the United States: Evidence from available systematic data sources.
Demography, 37(2), 139-154.
Brandenburg, D.L., Matthews, A. K., Johnson, T. P., & Hughes, T. L. (2007). Breast cancer risk
and screening: a comparison of lesbian and heterosexual women. Women &Health, 45(4),
109-130.
Byne, W., & Parsons, B. (1993).Human sexual orientation. The biologic theories reappraised.
Archives of General Psychiatry, 50(3), 228-239.
Case, P.,Austin, S. B., Hunter, D. J., Manson, J. E., Malspeis, S., Willett, W. c., & Spiegelman,
D. (2004). Sexual orientation, health risk factors, and physical functioning in the Nurses'
Health Study II. Journal of Women's Health (Larchmont), 13(9), 1033-1047.
19
Centers for Disease Control and Prevention. (2008). Trends in reportable sexually transmitted
diseases in the United States, 2003-National data on chlamydia, gonorrhea and syphilis.
Retrieved October 13, 2008, from: http://www.cdc.gov/std/stats03/trends2003.htrn
Chin-Hong, P. V, Vittinghoff, E., Cranston, R D., Browne, L., Buchbinder, S., Colfax, G.,
Palefsky,M. (2005). Age-related prevalence of anal cancer precursors in homosexual
men: the EXPLORE study. Journal of the National Cancer Institute, 97(2), 89~90S.
Cohen, L. (2006). Making headway under hellacious circumstances. Science, 313, 470-473.
Daling, j. R, Weiss, N. S., Hislop, G, Maden, C., Coates, R J, Sherman, K L., Corey. (1987).
Sexual practices, sexually transmitted diseases, and the incidence of anal cancer. New
England Journal of Medicine, 317, 973-977.
Dean, Meyer,.H., Robinson, K, Sell, Sember, R, Silenzio, VM.B., White, J (2000). Lesbian, gay,
bisexual, and transgender health: Findings and concerns. Journal of the Gay and Lesbian
Medical Association, 4(3), 101-151.
Denenberg, R. (1995).Report on lesbian health. Women's Health Issues, 5(2): 181-191.
Dibble, S., Roberts, S. A., Robertson, P. A., & Paul, S. M. (2002). Risk factors for ovarian
cancer: lesbian and heterosexual women. Oncology Nursing Forum, 290:EI-E7.
dictionary.com.(n.d.) Culture. Retrieved July 29, 2007, from
http://dictionary.reference.com/browse/culture
Egleston, B. Dunbrack, R., & Hall, M. (2010). Clinical trials that explicitly exclude gay and
lesbian patients. New England Journal of Medicine, 362(11), l054-1055.
Ferdinand K C., & Armani, A. (2006). Cardiovascular risk and disease in ethnic & racial groups.
In Gotto, A.M. & Toth, P.P. (Eds.), Comprehensive management of high-risk
cardiovascular patients. New York, NY: Informa Health.
Glynn, M., & Rhodes, P. (2005). Estimated HIV prevalence in the United States at the end of
2003. National HIV Prevention Conference; June 2005; Atlanta. Abstract No. TI-B1101.
Retrieved December 21, 2010 from http://www.aegis.com/conferences/nhivpc/200S/+11101.html
Goldstone, S. E., Hundert, S., & Huyett, W. (2007). Infrared coagulator ablation of high-grade
anal squamous intraepitheliallesions in HIV-negative males who have sex with males.
Diseases of the Colon &Rectum, 50(S):S6S-S7S.
Hamer, D. H., Hu, S., Magnuson, Hu, N., & Pattatucci, A. M. (993). A linkage between DNA
markers on the X chromosome and male sexual orientation. Science, 261(SI19), 321-327.
Hurley, B. W. (2003). Race and medical publications. Journal of the National Medical
Association, 95(4), 307-308.
Kendler, K S., Thornton, M., Gilman, S. E., & Kessler, R C. (2000). Sexual orientation in a U.S.
national sample of twin and nontwin sibling pairs. American Journal of Psychiatry,
157(1): 1843-1186.
Kung, H. C., Hoyert, D. Xu, Q., & Murphy, S. 1. (2008). Deaths: Final data for 200S. National
vital statistics reports, 56(10), 1-120. Lidz, T. (1993). Reply to "A genetic study of male
sexual orientation." Archives of General Psychiatry, 50(3), 240-241.
Linet, M. S. (2008). Postmenopausal unopposed estrogen and breast cancer risk in the women's
health initiative-Before and beyond. American of Journal of Epidemiology, 167(2), 14161420.
Pruitt, M. V. (2002). Size matters: a comparison of anti-and pro-gay organizations' estimates of
the size of the gay population. Journal of Homosexuality, 42(3), 21-29.
20
Risch, N., Squires-Wheeler, E., & Keats, B. J. (1993). Male sexual orientation and genetic
evidence [comment]. Science, 262(5142), 2063-2065.
Robertson, P., & Schacter, J. (981). Failure To identify venereal disease in a lesbian population.
Sexually Transmitted Diseases, 8(2), 75-76.
Ryan,H.,Wortley, P.M.,Easton, A,Pederson, L.,& Greenwood, G. (2001). Smoking among
lesbians, gays, and bisexuals: a review of the literature. American Journal of Preventive
Medicine, 21, 142-149.
Sheldon, T. A., & Parker, H. (992). Race and ethnicity in health research. Journal of Public
Health Medicine, 14(2), 104-110.
Smedley, B. D., Stith, A Y, &Nelson, A. R. (Eds.).The Institute of Medicine. (1999). Unequal
treatment: Confronting racial and ethnic disparities in health care. Washington, DC: The
National Academic Press.
U.S. Department of Health and Human Services, Office on Women's Health. Lesbian Health.
(2008). Fact sheet. Retrieved May 22, 2008,from http://www.
gIma.orgl_data/n_0001/resources/live/lesbian1.pdf
Source Article 7:
Aboagye-Safro, James Cross & Ute Mueller. 2010. Trend Analysis and short-term forecast of
incident HIV infection in Ghana. African Journal of AIDS Research. 9(2):165-173.
Technique: Trend Analysis
Description of Purpose:
This article is an example of how trend analysis forecasts incidents of AIDS in Ghana. They use
different modeling methods, such as univariate time-series and moving average modeling, to
assess the trend.
Strengths and weaknesses of method:
The following are strengths of the technique according to the authors:
 It gives quantifiable data points through statistical tools
 Using long term growth rates, one can use moving averages to assess a trend and
assess seasonality
The weaknesses of the technique the author cite are listed below:
 Data is not always linear and can vary by population or age groups
 Causation is not available through trend analysis
Description of how to apply the method:
The article did not directly provide steps on how to do trend analysis. Rather it explained that
they used statistical software tools called the estimation and projection package and Spectrum to
review their data and make predictions.
Types of problems it can be applied to:
These authors applied trend analysis to look at the trends of AIDS in Ghana. Their use shows
how trend analysis can be used for forecasting through the use of statistical software packages.
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Comparison to other articles in the literature review:
Compared to the other articles, this article was a hard core research methods article that applied
different statistical means to assess the forecasting of AIDS in Ghana. It did not provide simple
steps like some of the previous articles. It did show different types of models one can use in
trend analysis that had not been covered in other articles.
What I found most informative:
I didn’t really find anything informative about trend analysis that hasn’t already been covered in
the other articles I’ve read.
About the author(s):
Patrick Aboagye-Safro, PhD, is a lecturer at Kwame Nkrumah University of Science and
Technology in Ghana. Ute Mueller, PhD, is an associate professor in mathematics in the
School of Engineering at Edith Cowan University in Western Australia. James Cross, PhD, is an
associate professor in mathematics and the Associate Dean (International) for the Faculty of
Computing, Health and Science at Edith Cowan University.
Source reliability: High Credibility
Article Critiqued by:
Sally Mohamed
smohamed@lakers.mercyhurst.edu
Mercyhurst College, Erie PA
Advanced Analytic Techniques Course
July 30, 2011
Other sources this source cited:
Abraham, B. & Ledolter, J. (1983) Statistical Methods for Forecasting. New York, John Wiley
and Son.
Anderson, R.M. (1988) The epidemiology of HIV infection: variable incubation plus infection
periods and heterogeneity in sexual activity. Journal of the Royal Statistical Society
Series A (Statistics in Society) 151, pp. 66–98.
Box, G.E.P. & Cox, D.R. (1964) Analysis of transformations. Journal of the Royal Statistical
Society Series B 26, pp. 211–252.
Box, G.E.P. & Jenkins, G. M. (1976) Time-Series Analysis: Forecasting and Control (Revised
edition). San Francisco, California, Holden Day.
Box, G.E.P., Jenkins, G.M. & Reinsel, G.C. (1994) Time-Series Analysis: Forecasting and
Control (3rd edition). San Francisco, California, Holden Day.
Brookmeyer, R. & Gail, M.H. (1986) Minimum size of the AIDS epidemic in the United States.
The Lancet 2, pp. 1320–1322.
Brookmeyer, R. & Gail, M.H. (1988) A method of obtaining short-term prediction and lower
bound on the size of the HIV epidemic. Journal of the American Statistical Association
83, pp. 301–308.
Brookmeyer, R. & Liao, J. (1990) Statistical modeling of the AIDS epidemic for forecasting
healthcare needs. Biometrics 46, pp. 1151–1163.
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Chan, K.S. (2008) Time-Series Analysis Package in R (0.9-3edition). R-Project, Department of
Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa.
Chatfield, C. (2004) The Analysis of Time Series: An introduction. New York, Chapman and
Hall/CRC Press.
Garcia-Calleja, J.M., Mvondo, J.L., Zekeng, L., Louis, J.P., Trebucg, A., Salla, R., Owana, R. &
Kaptue, L. (1992) A short-term projection of HIV infection and AIDS cases in
Cameroon. Transactions of the Royal Society of Tropical Medicine and
Hygiene 86(4), pp. 435–437.
Granger, C.W.J. & Newbold, P. (1976) Forecasting transformed series. Journal of the Royal
Statistical Society Series B (Methodological) 38, pp. 189–203.
Healy, M.R.J. & Tillett, H.E. (1988) Short-time extrapolation of the AIDS epidemic. Journal of
the Royal Statistical Society Series A 151, pp. 50–65.
Ljung, G.M. & Box, G.E.P. (1978) On a measure of lack-of-fit in time-series models. Biometrika
65, pp. 553–564.
Loytonen, M. (1991) The Box-Jenkins forecast of the HIV-seropositive population in Finland,
1991–1993. Geografiska Annaler, Series B, Human Geography 73, pp. 121–131.
Nagelkerke, N.J.D., Jha, P., De Vlas, S.J., Korenromp, E.L., Moses, S., Blanchard, J.F. &
Plummer, F.A. (2002) Modeling HIV/AIDS epidemics in Botswana and India: impact of
interventions to prevent transmission. Bulletin of the World Health Organization
80(2), pp. 89–96.
National HIV/AIDS Control Programme (NACP) & Ghana Health Service (GHS) (2005)
HIV/AIDS Surveillance Report: Reported AIDS in Ghana. Accra, Ghana, NACP/GHS.
National HIV/AIDS Control Programme (NACP) & Ghana Health Service (GHS) (2006) 2005
HIV Sentinel Surveillance Report.
Accra, Ghana, NACP/GHS. National HIV/AIDS Control Programme (NACP) & Ghana Health
Service (GHS) (2008) HIV Sentinel Survey Report. Accra, Ghana, NACP/GHS.
National HIV/AIDS Control Programme (NACP), Ghana Health Service (GHS) & Ministry of
Health [Ghana] (MOH) (2008) National HIV Prevalence and AIDS Estimates Report,
2007–2012. Accra, Ghana, NACP/GHS/MOH.
Salomon, J.A. & Murray, C.J.L. (2000) Modeling the HIV/AIDS epidemic in sub-Sahara Africa
using seroprevalence data from antenatal clinics. Bulletin of the World Health
Organization 79(7), pp. 596–607.
Shumway, R.H. & Stoffer, D.S. (2005) Time-Series Analysis and its Applications (2nd edition).
New York, Springer Science and Business Media. Stover, J. [for the POLICY Project]
(2007) AIM: A Computer Program for Making HIV/AIDS Projections and Examining the
Social and Economic Impacts of AIDS. December 2007.
Washington, D.C., USAID, Health Policy Initiative. Stover, J. & Kirmeyer, S. (2007) DemProj
Version 4: A Computer Program for Making Population Projections. March 2007.
Washington, D.C., POLICY Project. Stover, J., Walker, N., Grassly, N.C. & Marston, M. (2006)
Projecting the demographical impact of AIDS and the number of people in
need of treatment: updates to the Spectrum Projecting Package. Sexually Transmitted
Infections 82, pp. 45–50.
UNAIDS (2003) Estimating and Projecting National HIV/AIDS Epidemics: The Models and
Methodology of the UNAIDS/WHO Approach to Estimating and Projecting National
HIV/AIDS Epidemics. January 2003.
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