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Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
Unit 10:
Data Analysis Project
Version 2.2
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
Contents
10.1 Aims ............................................................................................................ 389
10.2 Learning Outcomes ................................................................................... 389
10.3 Description of Study .................................................................................. 389
10.4 Data ............................................................................................................ 389
10.5 Questions to Explore ................................................................................. 390
10.6 CA5 Data Analysis Project ......................................................................... 391
10.6.1 Description of Study ......................................................................... 391
10.6.2 Data .................................................................................................. 391
10.6.3 Questions .......................................................................................... 392
10.6.4 Report ............................................................................................... 393
Unit 10: Data Analysis Project 389
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
10.1 Aims
In this unit, we demonstrate exploratory analysis and statistical procedures for ad-
dressing specific research questions relating to a data set.
10.2 Learning Outcomes
When you have completed this unit, you should be able to:
1. Construct a data analysis strategy.
2. Perform appropriate descriptive analysis of data.
3. Conduct relevant statistical tests.
4. Evaluate associated assumptions.
5. Utilise SPSS to obtain required graphs, summary statistics and to conduct the ap-
propriate analysis.
6. Identify elements of SPSS output which should be used in the written report.
7. Collate a written report addressing the specified research questions.
10.3 Description of Study
A study was conducted to investigate the relationship between current smoking sta-
tus and respiratory function in 654 youths aged 3-19 years (Rosner, B. 1999. Funda-
mentals of Biostatistics, 5th Ed., Pacific Grove, CA: Duxbury). Respiratory function was
measured by forced expiratory volume (FEV) which is the amount of air an individual
can exhale in the first second of a forceful breath. For each individual FEV (liters), age
(years), height (inches), gender and current smoking status were recorded.
10.4 Data
The data was obtained from the Journal of Statistics Education data archive located
under fev at http://www.amstat.org/publications/jse/jse_data_archive.htm.
390 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
Variables recorded are:
ID: Identification Number
Age: Age measured in years
FEV: Forced expiratory volume measured in liters
HT: Height measured in inches
Gender: Female coded 0,
Male coded 1
Smoke: Current smoking status
Nonsmoker coded 0,
Smoker coded 1
The data is stored in fev.sav
10.5 Questions to Explore
Using this data address the following questions of interest:
1. Assign appropriate variable names and labels as detailed in “Data”. Where appro-
priate, assign value labels to the numeric codes used for each categorical variable.
For each variable set the number of decimal places appropriate to the variable. Set
the appropriate measure for the variable.
2. In the data set for this project, age, in years, was recorded for each individual. Cat-
egorise the numeric variable age into the following age groups: 0-5, 6-10, 11-15
and 16-20 years. Assign an appropriate label to this new variable. Assign appropri-
ate value labels to the numeric codes of this new variable. Set the number of dec-
imal places appropriate to this new variable. Set the appropriate measure for this
new variable.
3. Compute the frequency and percentage distribution for current smoking status.
Construct an appropriate graphical representation for current smoking status.
4. Compute the frequency and percentage distribution for age groups. Construct an
appropriate graphical representation for age groups.
5. Provide a breakdown of current smoking status by age groups and find the fre-
quency and percentage of each age group amongst smokers and nonsmokers.
Graphically represent the percentage breakdown of current smoking status by age
group. Is the age distribution of smokers different to that of nonsmokers?
6. Separately for FEV and age, construct appropriate graphical representations.
7. Graphically represent the relationship between FEV and age. Note: FEV is the de-
pendent variable of interest.
Unit 10: Data Analysis Project 391
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
8. Conduct an appropriate test to examine if the mean FEV for smokers differs from
that for nonsmokers. Use the output to answer the following questions. Interpret
the summary statistics. What is the P-value for the test of means? Interpret this
value. What is your conclusion? Provide a 95% confidence interval for the differ-
ence in the mean FEV between smokers and nonsmokers. Interpret this confi-
dence interval. Construct an appropriate graphical representation of the 95% con-
fidence intervals for the mean FEV for smokers and nonsmokers. Provide an ex-
planation for your finding.
10.6 CA5 Data Analysis Project
10.6.1 Description of Study
A study was conducted by researchers at Purdue University to examine the effect of
three different methods of instruction namely, Basal, DRTA and Strat, on reading
comprehension in children. In the study, 66 children were randomly assigned to one of
these three groups. They were given a reading comprehension test before and after
receiving the instruction. Several different measures of reading comprehension were
recorded before receiving the instruction and again after receiving the instruction. Ba-
sal (Basal Reading Texts) is a non-interactive strategy and involves using texts that are
written to teach reading (instructed control). In the Basal group, students engaged in
a non-interactive, guided reading of stories. DRTA (Directed Reading Thinking Activi-
ty) is where the teacher guides the students through the text by asking what they
think the story is about, and then throughout the text asking them to review their
predictions. In the DRTA group, students were taught a predict-verify strategy for
reading and responding to stories. Strat (Think Aloud Strategies) is a method where
students are taught to ask questions of and 'think aloud' about the text. In the Strat
group, students were taught various comprehension monitoring strategies for reading
stories (e.g. self-questioning, retelling, rereading) through the medium of thinking
aloud.
10.6.2 Data
The data for one of the three comprehension measures are given in the dataset
data_assignment.sav.
Variables recorded are:
ID: Subject ID
Instruction: Type of instruction that student received
1=Basal, 2=DRTA, 3= Strat
PRE1: Pre-test score on first reading comprehension measure
PRE2: Pre-test score on second reading comprehension measure
POST1: Post-test score on first reading comprehension measure
392 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
POST2: Post-test score on second reading comprehension measure
The first reading comprehension measure was an error detection test designed to
evaluate students' ability to monitor their comprehension. Students were presented
with a basal reader story within which 16 sentences were intruded. The score on this
measure was the number of correctly identified intruded sentences. The second read-
ing comprehension measure was a comprehension monitoring questionnaire which
queried students about the strategies they believed to be useful in promoting their
understanding of stories. Students were given a multiple-choice items questionnaire.
Each item’s multiple-choice included one response indicative of comprehension moni-
toring behaviours. The score on this measure was the number of times a student se-
lected the response that was indicative of comprehension monitoring behaviours.
10.6.3 Questions
Using this data (located in data_assignment.sav) address the following questions of
interest:
1. Provide a descriptive analysis of the variables, Pre2 and Post2. What are the find-
ings in relation to each variable?
2. Did the pre-test on the second reading comprehension measure (Pre2) differ be-
tween the three methods of instruction? Conduct an appropriate descriptive anal-
ysis and statistical test. Conclusions must be supported by statistical evidence. The
solution must include appropriate summary statistics, graphical representation
and significance tests.
3. For each method of instruction, did the second reading comprehension score
(Pre2) change between pre and post-testing? Conduct an appropriate descriptive
analysis and statistical test. Conclusions must be supported by statistical evidence.
The solution must include appropriate summary statistics, graphical representa-
tion and significance tests.
4. Did the mean changes on the second reading comprehension measure differ sig-
nificantly between methods of instruction? Conduct an appropriate descriptive
analysis and statistical test. Conclusions must be supported by statistical evidence.
The solution must include appropriate summary statistics, graphical representa-
tion and significance tests.
5. Are the pre-test and post-test scores for the second reading compression measure
related? Conduct an appropriate descriptive analysis and statistical test. Conclu-
sions must be supported by statistical evidence. The solution must include appro-
priate summary statistics, graphical representation and significance tests.
Unit 10: Data Analysis Project 393
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
10.6.4 Report
You are required to write a report on your study which must contain the following
headings:
Title
State the title of the project
State your name, student ID and Department/School
Abstract
You should summarise the research questions and the key findings for each. State
your main conclusions while considering any caveats. Include relevant material from
articles or other sources
Data Analysis
For each question, state the statistical techniques used including any technique used
to verify assumptions, specify the variables used with each technique and provide a
justification for each of the techniques including assumption checks applied to your
data. Explain why the statistical methodology is appropriate. Do not present findings
or results from your analyses.
Results
Present the results of your analyses conducted to address each question of interest.
The Results section should give the findings in a logical fashion. In this section, report
your findings as text stating appropriate statistical evidence to support your narrative.
You should introduce and refer to the relevant table, figure or graph that contains the
details. To do this, you need to think about, for example, the content of the relevant
table or the relevant figure and describe this for the reader. Reporting on the results
should include interpreting relevant estimated statistical quantities. For example, for
a reported R2=0.56, what does this tell the reader? How should the reader interpret it?
In the Results section, do not define these statistical quantities (You should do this in
the Statistical Analysis section) moreover interpret the estimated value from your
analysis. Tables, figures and graphs should be used as methods of summarising the
output from SPSS. All tables should have titles. Figures should have titles and explan-
atory legends. Both tables and figures should be numbered (Examples of titles and
legends; usually of the format Figure 1: Title; Table 1: Title). Tables and figures should
be grouped together and presented in a logical order in an Appendix.
Statements about the findings should have enough information (statistical evidence)
such that a reader does not have to constantly lookup tables or graphics. However, all
tables and graphics should be connected to the text. It is important that tables and
graphics included in your report are of good quality, are relevant, add value, necessary
and not redundant (in that they contain no additional information already given in
text).
Discussion
For each question, summarise and discuss your results found. Discuss the limitations
of the study, data and analysis. If appropriate, provide suggestions for further analysis
394 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
or collection of additional data. Summarise your conclusions about the issues of scien-
tific and statistical concern.
References
List all books and articles you consulted and are reflected in your report.
Other
Typed A4 page
3cm margins
1.5 line spacing
Numbered pages
Your report should be typed and pages numbered. It should be well organised with
clarity, and accuracy. Simple telegraphic prose is preferred to sentences which are
convoluted or otherwise confusing.
Resources
These two articles contain the same information, they differ in layout. I have included
both as you may find the layout more appealing.
Lang and Altman (2016) Statistical analyses and methods in the published literature
The SAMPL guidelines.
http://journal.emwa.org/statistics/statistical-analyses-and-methods-in-the-published-
literature-the-sampl-guidelines/
Lang and Altman (2013) Basic Statistical Reporting for Articles in Biomedical Journals:
“Statistical Analyses and Methods in the Published Literature” or The SAMPL Guide-
lines”.
http://www.equator-network.org/wp-content/uploads/2013/03/SAMPL-Guidelines-3-
13-13.pdf
Unit 10:
Data Analysis Project
Version 2.2
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
Contents
10.1 Aims ............................................................................................................ 389
10.2 Learning Outcomes ................................................................................... 389
10.3 Description of Study .................................................................................. 389
10.4 Data ............................................................................................................ 389
10.5 Questions to Explore ................................................................................. 390
10.6 CA5 Data Analysis Project ......................................................................... 391
10.6.1 Description of Study ......................................................................... 391
10.6.2 Data .................................................................................................. 391
10.6.3 Questions .......................................................................................... 392
10.6.4 Report ............................................................................................... 393
Unit 10: Data Analysis Project 389
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
10.1 Aims
In this unit, we demonstrate exploratory analysis and statistical procedures for ad-
dressing specific research questions relating to a data set.
10.2 Learning Outcomes
When you have completed this unit, you should be able to:
1. Construct a data analysis strategy.
2. Perform appropriate descriptive analysis of data.
3. Conduct relevant statistical tests.
4. Evaluate associated assumptions.
5. Utilise SPSS to obtain required graphs, summary statistics and to conduct the ap-
propriate analysis.
6. Identify elements of SPSS output which should be used in the written report.
7. Collate a written report addressing the specified research questions.
10.3 Description of Study
A study was conducted to investigate the relationship between current smoking sta-
tus and respiratory function in 654 youths aged 3-19 years (Rosner, B. 1999. Funda-
mentals of Biostatistics, 5th Ed., Pacific Grove, CA: Duxbury). Respiratory function was
measured by forced expiratory volume (FEV) which is the amount of air an individual
can exhale in the first second of a forceful breath. For each individual FEV (liters), age
(years), height (inches), gender and current smoking status were recorded.
10.4 Data
The data was obtained from the Journal of Statistics Education data archive located
under fev at http://www.amstat.org/publications/jse/jse_data_archive.htm.
390 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
Variables recorded are:
ID: Identification Number
Age: Age measured in years
FEV: Forced expiratory volume measured in liters
HT: Height measured in inches
Gender: Female coded 0,
Male coded 1
Smoke: Current smoking status
Nonsmoker coded 0,
Smoker coded 1
The data is stored in fev.sav
10.5 Questions to Explore
Using this data address the following questions of interest:
1. Assign appropriate variable names and labels as detailed in “Data”. Where appro-
priate, assign value labels to the numeric codes used for each categorical variable.
For each variable set the number of decimal places appropriate to the variable. Set
the appropriate measure for the variable.
2. In the data set for this project, age, in years, was recorded for each individual. Cat-
egorise the numeric variable age into the following age groups: 0-5, 6-10, 11-15
and 16-20 years. Assign an appropriate label to this new variable. Assign appropri-
ate value labels to the numeric codes of this new variable. Set the number of dec-
imal places appropriate to this new variable. Set the appropriate measure for this
new variable.
3. Compute the frequency and percentage distribution for current smoking status.
Construct an appropriate graphical representation for current smoking status.
4. Compute the frequency and percentage distribution for age groups. Construct an
appropriate graphical representation for age groups.
5. Provide a breakdown of current smoking status by age groups and find the fre-
quency and percentage of each age group amongst smokers and nonsmokers.
Graphically represent the percentage breakdown of current smoking status by age
group. Is the age distribution of smokers different to that of nonsmokers?
6. Separately for FEV and age, construct appropriate graphical representations.
7. Graphically represent the relationship between FEV and age. Note: FEV is the de-
pendent variable of interest.
Unit 10: Data Analysis Project 391
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
8. Conduct an appropriate test to examine if the mean FEV for smokers differs from
that for nonsmokers. Use the output to answer the following questions. Interpret
the summary statistics. What is the P-value for the test of means? Interpret this
value. What is your conclusion? Provide a 95% confidence interval for the differ-
ence in the mean FEV between smokers and nonsmokers. Interpret this confi-
dence interval. Construct an appropriate graphical representation of the 95% con-
fidence intervals for the mean FEV for smokers and nonsmokers. Provide an ex-
planation for your finding.
10.6 CA5 Data Analysis Project
10.6.1 Description of Study
A study was conducted by researchers at Purdue University to examine the effect of
three different methods of instruction namely, Basal, DRTA and Strat, on reading
comprehension in children. In the study, 66 children were randomly assigned to one of
these three groups. They were given a reading comprehension test before and after
receiving the instruction. Several different measures of reading comprehension were
recorded before receiving the instruction and again after receiving the instruction. Ba-
sal (Basal Reading Texts) is a non-interactive strategy and involves using texts that are
written to teach reading (instructed control). In the Basal group, students engaged in
a non-interactive, guided reading of stories. DRTA (Directed Reading Thinking Activi-
ty) is where the teacher guides the students through the text by asking what they
think the story is about, and then throughout the text asking them to review their
predictions. In the DRTA group, students were taught a predict-verify strategy for
reading and responding to stories. Strat (Think Aloud Strategies) is a method where
students are taught to ask questions of and 'think aloud' about the text. In the Strat
group, students were taught various comprehension monitoring strategies for reading
stories (e.g. self-questioning, retelling, rereading) through the medium of thinking
aloud.
10.6.2 Data
The data for one of the three comprehension measures are given in the dataset
data_assignment.sav.
Variables recorded are:
ID: Subject ID
Instruction: Type of instruction that student received
1=Basal, 2=DRTA, 3= Strat
PRE1: Pre-test score on first reading comprehension measure
PRE2: Pre-test score on second reading comprehension measure
POST1: Post-test score on first reading comprehension measure
392 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
POST2: Post-test score on second reading comprehension measure
The first reading comprehension measure was an error detection test designed to
evaluate students' ability to monitor their comprehension. Students were presented
with a basal reader story within which 16 sentences were intruded. The score on this
measure was the number of correctly identified intruded sentences. The second read-
ing comprehension measure was a comprehension monitoring questionnaire which
queried students about the strategies they believed to be useful in promoting their
understanding of stories. Students were given a multiple-choice items questionnaire.
Each item’s multiple-choice included one response indicative of comprehension moni-
toring behaviours. The score on this measure was the number of times a student se-
lected the response that was indicative of comprehension monitoring behaviours.
10.6.3 Questions
Using this data (located in data_assignment.sav) address the following questions of
interest:
1. Provide a descriptive analysis of the variables, Pre2 and Post2. What are the find-
ings in relation to each variable?
2. Did the pre-test on the second reading comprehension measure (Pre2) differ be-
tween the three methods of instruction? Conduct an appropriate descriptive anal-
ysis and statistical test. Conclusions must be supported by statistical evidence. The
solution must include appropriate summary statistics, graphical representation
and significance tests.
3. For each method of instruction, did the second reading comprehension score
(Pre2) change between pre and post-testing? Conduct an appropriate descriptive
analysis and statistical test. Conclusions must be supported by statistical evidence.
The solution must include appropriate summary statistics, graphical representa-
tion and significance tests.
4. Did the mean changes on the second reading comprehension measure differ sig-
nificantly between methods of instruction? Conduct an appropriate descriptive
analysis and statistical test. Conclusions must be supported by statistical evidence.
The solution must include appropriate summary statistics, graphical representa-
tion and significance tests.
5. Are the pre-test and post-test scores for the second reading compression measure
related? Conduct an appropriate descriptive analysis and statistical test. Conclu-
sions must be supported by statistical evidence. The solution must include appro-
priate summary statistics, graphical representation and significance tests.
Unit 10: Data Analysis Project 393
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
10.6.4 Report
You are required to write a report on your study which must contain the following
headings:
Title
State the title of the project
State your name, student ID and Department/School
Abstract
You should summarise the research questions and the key findings for each. State
your main conclusions while considering any caveats. Include relevant material from
articles or other sources
Data Analysis
For each question, state the statistical techniques used including any technique used
to verify assumptions, specify the variables used with each technique and provide a
justification for each of the techniques including assumption checks applied to your
data. Explain why the statistical methodology is appropriate. Do not present findings
or results from your analyses.
Results
Present the results of your analyses conducted to address each question of interest.
The Results section should give the findings in a logical fashion. In this section, report
your findings as text stating appropriate statistical evidence to support your narrative.
You should introduce and refer to the relevant table, figure or graph that contains the
details. To do this, you need to think about, for example, the content of the relevant
table or the relevant figure and describe this for the reader. Reporting on the results
should include interpreting relevant estimated statistical quantities. For example, for
a reported R2=0.56, what does this tell the reader? How should the reader interpret it?
In the Results section, do not define these statistical quantities (You should do this in
the Statistical Analysis section) moreover interpret the estimated value from your
analysis. Tables, figures and graphs should be used as methods of summarising the
output from SPSS. All tables should have titles. Figures should have titles and explan-
atory legends. Both tables and figures should be numbered (Examples of titles and
legends; usually of the format Figure 1: Title; Table 1: Title). Tables and figures should
be grouped together and presented in a logical order in an Appendix.
Statements about the findings should have enough information (statistical evidence)
such that a reader does not have to constantly lookup tables or graphics. However, all
tables and graphics should be connected to the text. It is important that tables and
graphics included in your report are of good quality, are relevant, add value, necessary
and not redundant (in that they contain no additional information already given in
text).
Discussion
For each question, summarise and discuss your results found. Discuss the limitations
of the study, data and analysis. If appropriate, provide suggestions for further analysis
394 Unit 10: Data Analysis Project
Kathleen O’Sullivan, Statistics, School of Mathematical Sciences, UCC; ST6013
or collection of additional data. Summarise your conclusions about the issues of scien-
tific and statistical concern.
References
List all books and articles you consulted and are reflected in your report.
Other
Typed A4 page
3cm margins
1.5 line spacing
Numbered pages
Your report should be typed and pages numbered. It should be well organised with
clarity, and accuracy. Simple telegraphic prose is preferred to sentences which are
convoluted or otherwise confusing.
Resources
These two articles contain the same information, they differ in layout. I have included
both as you may find the layout more appealing.
Lang and Altman (2016) Statistical analyses and methods in the published literature
The SAMPL guidelines.
http://journal.emwa.org/statistics/statistical-analyses-and-methods-in-the-published-
literature-the-sampl-guidelines/
Lang and Altman (2013) Basic Statistical Reporting for Articles in Biomedical Journals:
“Statistical Analyses and Methods in the Published Literature” or The SAMPL Guide-
lines”.
http://www.equator-network.org/wp-content/uploads/2013/03/SAMPL-Guidelines-3-
13-13.pdf
