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CJ598

Week 3 DQ and 2 Student Responses

Drafting Proposed Methods and Design

Discuss your initial plans for developing the proposed methods section of your Capstone Project, including your proposed method and design. Describe how you are planning to conduct data collection and analysis, identifying specific challenges and ethical considerations that should be taken into account. Be sure that you are providing justification and rationale for your decision. In other words, why is what you propose the most appropriate way to address your research problem? Include supporting citations from scholarly, peer-reviewed sources and provide the complete APA reference for each.

In your response posts, provide your peers with constructive feedback, and suggest alternative approaches and/or considerations that they may not have identified.

*********In this discussion thread, we focus on your initial plans for developing the proposed methods section of your Capstone Project. Many of you likely have ideas/plans identified from your work in CJ525. Discuss whether you plan to continue with what was previously identified, or if you are planning any changes. Keep in mind that the more simple your proposed methods, the easier it will be to develop the section and ensure you are proposing a viable and realistic study. Be sure to include citations to support and justify your choice of method and design from peer-reviewed and methodological sources. ***********

Respond to Kindly to Student #1(Blake Carter)

My proposal has to deal with the use of UAV by the USBP along the Mexico–United States border for safer and more efficient patrolling for Border Patrol Agent (BPA). While a qualitative approach is viable here such as interviewing Border Patrol Agents; a quantitative approach would deliver more accurate results and a better understanding of UAV usage. The data would be complied from past and present reports. For example, I could compare assaults and or injuries on BPA before the introduction of UAV and afterwards to see if there is a correlation between the two. Moreover, data about the number of apprehensions could be utilized to see if they are leading to more efficient patrolling methods by BPA. Reaching out directing to the USBP for this information is also an option; however considering the classification of the data they might not be willing to release it if it is detrimental to their mission or national security.

There are two main ethical considerations when it comes to the use of UAV. They consist of privacy and autonomy. Nelson et al. (2019) explored privacy and UAV by conducting a survey and the results where that 82% agreed that UAV violated private space (p.92). In regard to the proposal this would be the privacy of the BPA and the subjects they happen to apprehend or interact with. BPA are working in official capacities, so this should not affect them. However, the people they apprehend are a different story, especially if they are U.S. citizens. It remains unclear how UAV usage interacts with the fourth amendment.

Autonomy has to deal with UAV and if they are being directly controlled by a human operator or an artificial intelligence (AI). Othman and Aydin (2021) discuss the use of human action recognition (HAR) techniques with the use of UAV. HAR allows an AI to identify someone by human actions such as gestures. While this technique could provide invaluable when it comes to searching through a large database for matches; there is still a chance for misidentification. Due to this factor it would be wise to still assign a human operator to an AI system as a failsafe in case of undesirable results from the AI.

Reference

Nelson, J. R., Grubesica, T. H., Wallace, D., & Chamberlain, A. W. (2019). The view from above: A survey of the public’s perception of unmanned aerial vehicles and privacy. Journal of of urban technology, 83-105.

Othman, N. A., & Aydin, I. (2021). Challenges and limitations in human action recognition on unmanned aerial vehicles: A comprehensive survey. Traitement du Signal, 1403-1411.

Respond to Kindly to Student #2 (Luke Leon)

My current data approach is below:

The mixed-methods approach is the approach that will be used for the research. This is the ideal approach for this because there will be both qualitative and quantitative data to be collected and applied. On the quantitative side, the number of attacks that have occurred in the past and at what rate could be an example of quantitative analysis. Whereas, evaluating cyber-security response plans, interviews, surveys, and similar information with cyber-security operations have a qualitative approach. Another benefit of using the mixed methods approach is that the research is not limiting the opportunity to acquire useful information, some research requires one and not the other sometimes it requires both (Maxfield & Babbie, 2018, p. 39). The mixed-methods approach allows for the best opportunity to obtain data for both variables and make it useful, rather than limiting options that could eliminate potential sources for data.

I feel comfortable I have a good start, but can use some work to make it better. One of the main goals is to keep this simple as possible, just as the professor suggested. I want to work on simplifying what I have now while adding in more of what we are developing in this week’s assignment to ensure the data collection is efficient and not overwhelming for the study.

Reference

Maxfield, M. G., & Babbie, E. R. (2018). Research methods for criminal justice and criminology. Cengage Learning.

contents 157

Qualitative Data

15
Many of us in the social sciences come from arts rather than science backgrounds and find statistics difficult. Hence, we tend to look to qualitative data as the core
of our research. Qualitative data is information that is represented usually as words, not
numbers. If you have pages of text before you, recordings of interviews or notes from
observation, for all practical purposes you have qualitative data. Nevertheless, words
must be analysed as carefully as numbers. Naturalistic inquiry does not guarantee the
meaning of your research any more than statistics guarantee its rigour.

In researching people’s subjective perceptions, we build up scientific knowledge
about their personal knowledge by objectifying their perceptions systematically.
However, the actual perceptions themselves do not suddenly become scientific by
virtue of having been studied scientifically. Just because our research gives us a better
idea of what some people think (and maybe, gives us an emotional attachment to
these people and their ideas), it neither suddenly changes the nature of their thoughts
nor makes them more important than other people’s. Different groups have ethical,
social and political rights in consonance with their own culturally meaningful sets of
social constructs. However, this does not necessarily mean that these groups have
professionally or scientifically informed views, or that their constructs are valid beyond
their own group.

Carefully done qualitative research is just as demanding as other research. However,
it is easy to do badly, by being intellectually lazy and hiding the fact from yourself
and others. The rules of the game are not as transparent as they are in quantitative
research. The result can be a weak report that has neither technical nor intellectual
rigour. Rather, research reports must demonstrate a careful systematic approach to
data analysis.

This chapter will:

1. review the key principles about qualitative data established earlier in this book,
including the key hierarchy for presenting data;

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158 Basic ReseaRch Methods

2. show how to present observational and unstructured interview data in more
detail; and

3. outline some basic techniques for manual and computer analysis of short open-
ended questions.

15.1

Qualitative data principles

First, we need to revise some of the underlying principles that apply to qualitative data:

1. Words are data that express qualities and attributes (Chapter 14).
2. Words particularly come from available sources (Chapter 9), naturalistic

observation (Chapter 10), unstructured interviews (Chapter 11) and open-ended
questions in questionnaires (Chapter 12).

3. Words are most often classified on the nominal measurement scale (Table 14.1).

Although much qualitative research does not analyse data formally using
measurement concepts, understanding the principles will often help solve problems
that arise during analysis. Does something not seem logical? You are possibly confused
about the measurement scale or maybe, the underlying semantic differential is not a
polar opposite. Does a paragraph seem jumbled? Are you not sure about where some
material belongs? Chances are that you are mixing up more than one variable.

A hierarchy drawn from Bloom’s Taxonomy helps present data clearly. First, describe;
then analyse; and later, draw conclusions or interpret.

1. Describe

(a) Write out the ‘facts’ of the situation observed or heard about in open-ended
interviews. This should be clear descriptive reporting, free of adjectival
colour.

(b) Do not present everything. Filter out those matters which are not relevant
to the research problem and themes.

2. Classify

(a) Group the material to identify similarities and differences in the data.
(b) Break paragraphs when they start getting complex so that there is one main

idea in each paragraph.
(c) Use more headings if you have trouble categorising the literature ap-

propriately. Rather than making the headings more abstract, make them
more concrete.

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qualitative data 159

3. Interpret

(a) Present your interpretation of the material separately. Pick out key features
that identify patterns and keep your mind open to new ideas that arise from
the data.

(b) You do not need to be highly conceptual here—that can come later in the
concluding chapter with a wider analysis of the findings in light of
the literature review.

The aim should be reporting that is clear and analytical. It should work from the data
as recorded in observation and interviews to the analysis generated by the literature
review, the fieldwork and interviews.

15.2

Presenting available and observation data

How do we present unstructured qualitative data gained from available sources and
field observation? This can be confusing at first because now you have large chunks
of text, usually written in a note pad. Once writing begins, it is very easy to start com-
mentating, and quickly there is a problem. What is observation or description and what
is commentary or interpretation? The answer to the problem remains, first, describe,
then analyse and, later, draw conclusions or interpret.

Computers can be used to analyse this data, but for decades, qualitative researchers
survived without them. Some manual techniques are quite usable, especially when there
are large volumes of text. Computer processing will be faster than manual processing
once the text is set up, but the trade-off is that learning to use specialist computer
packages and setting up text analysis functions might take more time than you save
in processing. For small amounts of data, you can transcribe from your notes into a
word processor.

In writing up the material, you have two main options:

1. Narrate it as a chronological story, which is usually the most straightforward
for both writer and reader.

2. Analyse it systematically, which will add insight and academic value.

Examples of chronological narration have been given already in Box 9.1 (based
on available documentary data about school inspections classified under appropriate
headings) and Box 11.5 (using observation and interview about highway crime). You
can revisit those examples, so now we will focus on systematic analysis.

The basis for systematic analysis is issues or themes identified in the literature
review or grounded in the data. You can either work straight from a note pad (which

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160 Basic ReseaRch Methods

I did with the 18 interviews in the highway study) or transcribe the material into a
word processing document, which you should do as soon as possible after collecting
it. Entering the whole text into the computer is time consuming, but the entry process
leads to revision of the material and ignites your thought process about patterns and
connections in the data. Then the following steps apply:

1. If a note pad is the source, strike out material in the pad as it is copied. If you
have typed up the material, make a backup document and use another copy as a
source for cutting and pasting. In both cases, keep coming back to the remaining
material.

2. In a new document, insert headings using key themes from the literature review.
Use them as destinations to copy different phrases, sentences or paragraphs from
the notes or document.

3. If using more than one period of observation or more than one interview as
source material, keep a reference to it with each item that is entered. When you
analyse the data, you can comment on who said what and how their different
perspectives might illuminate the topic under discussion.

4. If source material straddles more than one heading or does not fit under an
existing heading, consider making up a new one.

5. If something still does not fit, perhaps it is of minor importance and can be
omitted, but do not leap too quickly to this conclusion. The minor elements might
actually be important and the problem is that you have not woken up to this.

To clarify the data, follow the describe/classify/interpret steps given in the
previous sub-section. Box 15.1 shows an example, being a summary of two days
observing a school inspection visit during which I followed the inspector and took
detailed notes. The first part, taken from eight pages of the original report, is a
straight chronological description of the inspector’s day and his interactions in
the school. However, the description reports only his activities and filters out other
aspects of the school that were secondary to this (for example, details of lessons
observed). The second part is classification of the events to show themes, being key
summary points cross-referenced back to parts of the observation. Broader academic
and professional interpretation about the nature of inspections in a formalistic educa-
tion system was saved for the concluding chapter.

15.3

Presenting open-ended interview data

The techniques for presenting long pieces of data from unstructured interviews are
similar to unstructured observation. Once you have notes or text, you can describe,
classify and interpret it using the five steps listed earlier. Co

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qualitative data 161

Box 15.1 Presenting oBservation data

A School Inspection Visit

An Advisory Visit

‘7.50am. The Inspector arrived at the school, met the Headmaster and introduced the researcher.
Arrangements for the day were discussed and the timetable checked.

‘8.40. A second teacher interview began…The Inspector went through the eight Inspection Report
headings…then the Inspector went over a lesson observed the previous day…

‘10.30. The Inspector and the researcher walked down to the Social Science office to interview the
Subject Master of Expressive Arts and Social Science. The Inspector first checked teachers’ timetables
and whether there was an Expressive Arts syllabus, a problem raised by the third interview. He then
checked how many subject meetings had been held…[and] checked the availability of Expressive
Arts materials…He then turned to Social Science duties…

‘8.50 [the next morning]. A grade 8 Agriculture class lesson on picking coffee cherries was
observed until 9.10 when note-taking from the blackboard began…

‘11.10 The staff meeting now started … The Inspector said that there would be ten points in his
address…The first point concerned assessment…The Inspector’s next point covered the value of
applying for in-service courses…For his fifth point, the Inspector discussed school maintenance…and
praised the improvement in classroom displays…

Several features were apparent from this visit.
‘The first feature apparent was the volume of work covered. The first day spanned about

10 hours, the second 9½ hours…
‘A second and constant feature of the two days of the visit was an emphasis on quality, broadly

defined: the “tone” of the school, the need for thorough preparation and planning, the need to
challenge and extend students…

‘A third feature was the close interrelationship between the three inspectorial roles [advice,
evaluation and administration]. Within the space of a few minutes in an interview each role was
frequently encompassed…Although it is easy in principle to draw a distinction between advising
and evaluating, in practice it is somewhat more difficult…

‘The intermixing of roles parallels a fourth feature of the visit: the Inspector was the system-defined
expert on everything…This was particularly evident in the meeting held with all the staff…

‘Finally, a seventh feature of the visit was the thoroughness of the investigations and the
thoroughness of the cross-checking…’

Source: Adapted from Guthrie (1983b: 19–27).

Box 15.2 has reporting from an investigation of science teachers’ experience
in the Philippines. It reports a trainee teacher’s narrative about the difficulty she
had in relying only on a textbook that contained information contradicted by local
knowledge. The article from which this story is taken then analysed the situation
in more detail, bringing to bear further narrative about the teacher’s life experi-
ences to show how they provided meaning for her teaching career and how that

162 Basic ReseaRch Methods

fitted in with her social background. The researchers also commented on the role of
English-language textbooks that might contain information conflicting with indigenous
knowledge. These layers gave greater depth to the study.

15.4

Computer analysis of text

We now turn to an analysis of short pieces of data from open-ended questions and
semi-structured interviews. The individual answers might be short but there are likely
to be a large number of them so that manual coding is a burden. Computing is the
path to follow.

Box 15.2 rePorting unstructured interviews

A Teacher’s Experience

‘I’m a student teacher of grade 1. As a teacher in science, there are times when unexpected situations
will occur inside the classroom which create dilemmas. My dilemma is not really a big one, but
when you look at it deeply, such a dilemma can create a serious situation that is hard to deal with
in science teaching.

‘One day, I taught a lesson about places where plants grow. I first presented places where specific
plants grow—in soil, water, air, wet and dry places. The plants I used as examples were taken
from the science book that we were using in class. The pupils were confused about whether the
water and wet places were the same? In real life situation there are plants that can grow in both
places—in wet and aquatic places. On a test, I asked students to list two examples of plants that
grow in soil, water, air, dry and wet places. One child wrote “Kangkong” plant under “wet” places;
I marked it wrong because the book implied that Kangkong would be an “aquatic” plant since it
grows in water.

‘The mother of one of our pupils came to the school. She had a correction for an answer her child
had written that I had marked wrong. The mother protested saying that Kangkong can grow also in
wet places. Because I followed what is written in the book, I marked it wrong. Besides, most of my
pupils believed that the book is the source of knowledge. So, if I checked or accepted other answers
that are not found in the book pupils will conclude that books can’t be trusted.

‘Actually, I believe that books are not the only source of knowledge. You can gain knowledge from
other people, and also from real life situations. Answers that can be found in the book are also correct,
but they are limited in the sense that other answers can also be found in other books.

‘My problem is: Am I going to stick to the book or consider other answers which are based on real
life situation? What will happen if I stick or depend only on the book? What should I do to help my
pupils understand our lesson?’

Source: Arellano et al. (2001: 214–15).

qualitative data 163

Your choices are to use whichever package is available on your computer system,
buy a programme and obtain manuals and textbooks that teach how to use it, or use a
spreadsheet. Several software packages are available for text analysis with large volumes
of data, for example, ATLAS.ti, HyperRESEARCH and NVivo. Computer packages can
allow audio, video and photo analysis as well, which are beyond the scope of this
book. In general, the more powerful and flexible a package, the more time required
to learn how to programme it. This can divert time from the primary purpose, which
is the research report.

Instead, you can use word processing and spreadsheet packages with which you are
already familiar. They are not as good as the specialist packages for advanced analysis,
but they can still go a long way. Despite being primarily for quantitative analysis, Excel
and other spreadsheets have basic functions that allow text analysis too. This section
gives you some procedures for searching individual questions using Excel and Word
2003. Despite being elementary, they were sufficient for the text analysis in the 16
crime surveys and they will suffice for many other projects.

First, enter each item of text into a spreadsheet cell. The shorter the text units,
the easier this is. With a structured or semi-structured questionnaire, column head-
ings should be the question numbers that contain open-ended responses. Row names
should be the questionnaire code numbers. If there are 10 open-ended questions
and 50 interviews, there will be 500 cells, but ‘no answers’ on questionnaires will
mean that many cells do not contain text. Those with text will usually contain only
a few sentences of comment from each interviewee, which is quite manageable with
this process.

The next steps involve using the count functions to tally numbers, word search
functions to list cells that contain key words and pasting the table text from the
spreadsheet to the word processor document. Table 15.1 gives procedures for pasting
answers to individual questions direct into the word processor. There you can further
cut and paste the items to give the data a logical flow.

Table 15.2 uses a commentary to illustrate further the procedures. This example
comes from the 2006 crime surveys in Bougainville, reporting on people’s comments
on reasons for changes in crime levels that were found in responses to an open-ended
question. The result was a mixture of descriptive quantitative data (the number and
frequency of responses) and qualitative comments in respondents’ own words.

You can also use the Excel ‘Edit’ > ‘Find All’ function to search the whole spreadsheet
for a key word that might have come up in answers to different questions. However,
the copy function for the search results is extremely cumbersome, so you have to do a
manual selection of individual quotes from the search results in Excel and copy them
one by one into the categories in the document.

164 Basic ReseaRch Methods

15.5 Summary

Qualitative data is information that is usually represented as words, not numbers. Both
must be analysed carefully.

Qualitative data principles

1. Words are text data that express qualities and attributes.
2. This type of data comes particularly from available sources, naturalistic

observation, unstructured interviews and open-ended questions.
3. Words are usually classified on the nominal measurement scale.
4. To help present text clearly, follow a hierarchy drawn from Bloom’s Taxonomy:

describe first, then classify and later interpret.

Presenting available and observation data

1. Some manual techniques are quite practical, especially for small volumes of text.
2. In writing up the material, the two main options are narration as a chronological

story or systematic analysis.

Table 15.1 Text analysis guidance using spreadsheets

Task Operations

Enter text in spreadsheet Columns = questions, rows = questionnaire IDs, cells = responses.

Add number of responses COUNTA(…) underneath first column of data > ‘Copy’ formula to
other columns.

Copy responses to Word
and format

Highlight all data cells in a column > ‘Copy’ > ‘Paste’ cells in word
processor > set ‘Paste Options’ drop-down box to ‘Keep Text Only’ >
format to document quotation style, e.g., bullet points, indented, italics.

Classify text Inspect text > cut and paste like items to adjacent lines > count
number of items in each group and calculate percentage of COUNTA
total > cut and paste groups in order from largest to smallest > the
final classification is ‘Other’ for items that do not fit previous categories.

Comment on
classifications

Write brief commentary describing each classification, including
number and percentage of total cells (from COUNTA).

Delete extraneous text ‘Delete’ repetitive items. Retain number of comments in each
classification in proportion to percentage of total.

Source: Author.

qualitative data 165

Table 15.2 Annotated text analysis

Text Commentary

‘Open-ended responses to Q.2.2 expanded the
reasons for the changes in crime levels that
were believed to have occurred…In Buka, 36
comments were made. The largest number
(47% or 17) was on reasons for peacefulness,
for example:

• As soon as a problem arises, the community
holds meetings to solve it.

• People are behaving.
• This is a mission area and people respect it.
• The new Task Force is doing its duties.

‘Two of this group thought alcohol was under
better control, for example:

• No drinking in public is helping a lot.

‘Of the comments on problems, 17% (6)
related to alcohol, for example:

• Drunkards are getting worse.

‘Five comments (14%) related to youth, for
example:

• A lot of youths don’t have a job.
• Youths are still causing troubles.

‘Two commented on guns still being in the
community.’

1. The open-ended responses were typed
from the questionnaires into the
spreadsheet, taking similar time as any
other package.

2. Q2.1 asked, ‘Do you think the level of
crime in your area has changed since the
last survey 12 months ago?’. Q.2.2 was a
followup, ‘Why’.

3. Searching Q.2.2 following the process in
Table 15.1 found 36 comments using the
COUNTA total.

4. The responses column was copied to the
text document and formatted. Blank lines
were deleted. The remaining responses
were categorised manually into two
small groups (reasons for peacefulness,
including alcohol-related) and problems
(including alcohol, youth and guns).

5. The groups of comments were put in
order from the largest with 17 to the
smallest with 2. Repetitive comments
were deleted.

7. Linking text was written at the start of
each group.

8. Later, in the conclusions chapter,
themes were taken up about community
responses to crime and the perceived
influences of alcohol, unemployment and
guns.

Source: Adapted from Guthrie et al. (2007: 22).

Presenting open-ended interview data

The steps for presenting data from unstructured interviews are similar to observation.

Computer analysis of text

Large volumes of short text from open-ended questions and semi-structured interviews
usually require computing. Techniques are provided for systematic analysis using
spreadsheets.C
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166 Basic ReseaRch Methods

Analysis of qualitative data is deceptive. Just because the data are words does not
mean that they would fall easily into place. Text from naturalistic observation and
interviews does not automatically provide meaningful results. Research reports must
demonstrate a careful and systematic approach to analysis so that the report has both
technical and intellectual rigour. Do this well, however, and you should end up with
an interesting, meaningful and accessible report that is a pleasure to read.

15.6 Annotated references

Babbie, E. (2007). The Practice of Social Research, 11th edition. Belmont: Wadsworth.
This sociology text has chapters on both qualitative and quantitative data analysis.

Best, J. and J. Kahn. (2005). Research in Education, 10th edition. Needham Heights:
Allyn & Bacon.
There is plenty of material in this book on qualitative research and data analysis.

Scheyvens, R. and D. Storey (eds). (2003). Development Fieldwork: A Practical Guide.
London: Sage.
A comprehensive collection on fieldwork in developing countries containing chapters

on both quantitative and qualitative research.

Vulliamy, G., K. Lewin and D. Stephens. (1990). Doing Educational Research in Developing
Countries: Qualitative Strategies. London: Falmer.
This book is heavily based on practical research experience using qualitative

approaches in Malaysia, Papua New Guinea, Sri Lanka and Nigeria, including extensive
discussion of data analysis.

contents 167

Quantitative Data

16
Even the most basic social science data can be expressed numerically and tested statistically. This understanding sees many areas of qualitative research that
historically contained very little quantitative data or analysis (anthropological case
studies, for example) now, sometimes, using statistical tests.

A strength of quantitative research is that detailed rules encourage care. The rules
get very complicated, but every statistical test has procedures that others can replicate.
This provides an intellectual discipline that encourages accuracy. If we pay careful
attention to the procedures and rules, the work will be systematic and thorough. Many
researchers, including myself, are without a strong mathematical background and find
that statistics are difficult. This is not a reason to avoid them; it is a challenge. There
is a steep learning curve, but you can become quite proficient if motivated. The first
data chapter in my first major research project took two months to write because I had
to teach myself statistics; but the last one only took two days.

Care with the maths does not necessarily make the research strong. Overdoing
sophisticated statistics to make minor studies appear important is merely statistical
overkill. A statistically significant relationship between an independent and a dependent
variable is only as useful as the underlying analysis. If we are intellectually sloppy and
overlook valid alternative variables, our research is of little value despite the statistics.
In other words, statistical analysis can add to the reliability of our research, but we need
to establish validity too: we need analytical rigour as well as technical strength.

This chapter will:

1. review the key principles of quantitative data established earlier in this book; and
2. outline some basic techniques for manual and computer analysis of descriptive

and inferential statistics.

With a few exceptions that demonstrate some key principles or help with spreadsheets,
the chapter will not go far into the definitions or mathematics. If you are going to do

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168 Basic ReseaRch Methods

some quantitative analysis, find a textbook on statistics that is appropriate for your
level, and make good use of the internet.

16.1

Quantitative data principles

Like qualitative data, measurement principles will often help solve problems that arise
during analysis. Does a table seem too complicated? Not sure where some material
belongs? Just like words, the chances are that an answer lies in confusion over multiple
variables with different measurement properties or underlaid by inconsistent semantic
differentials. The underlying principles that apply to numbers and tables are:

1. Numerical data expresses quantities and variables (Chapter 14).
2. Numbers come from some types of available data (Chapter 9), structured obser-

vation (Chapter 10), questionnaires (Chapter 12) and tests (Chapter 13).
3. Numbers can be classified on all measurement scales, but in social science we

mainly use the nominal and ordinal scales (Table 14.1).
4. The further up the scales, the more mathematical information is added, the

more precise the measurement and the more powerful the statistical tests that
can be used to test null hypotheses (Chapter 14).

The basic numerical steps to be followed for quantitative data are the same as for
qualitative data. First, describe; then, analyse; and later, draw conclusions. Two types
of statistics do the description and analysis:

1. Descriptive statistics, such as percentages and means, summarise the numbers
and can be represented in graphs.

2. Inferential tests analyse statistical significance for testing hypotheses and drawing
inferences about the strength of the findings.

An inferential test with a probability (p) greater than .05 (for example, .01) shows
a significant difference. This is useful when we are predicting differences. However, if
we are not looking for a difference, a result between p = .99 and p = .06, is the desired
outcome, for example, because it shows the sample is not significantly different from
the population from which it was drawn.

One technical issue is the choice of test types. Inferential tests further divide into
two types. Parametric tests are based on an assumption of a normal distribution in
the data and, technically, they are based on the mathematical properties of interval
or ratio data. Non-parametric tests do not make an assumption of normalcy and, thus,

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quantitative data 169

are especially useful with small samples that are not normally distributed and with
lower level data. Non-parametric statisticians have argued that tests designed for data
at the higher interval and ratio levels should not be used with the ‘weak measurement’
provided by the lower nominal and ordinal levels typically found with social science data.
Subsequently, the proponents of ‘strong statistics’ demonstrated that the mathematical
assumptions of many common parametric statistics are strong enough to allow their
use to extract more information on statistical significance from nominal and ordinal
data than is available from non-parametric tests.

The effect is that many statistical tests based on higher measurement scales can
use data from lower scales. Nonetheless, the results should be interpreted according to
the underlying scale. For example, ordinal data remains ordinal even if tested with a
measure originally designed for interval data. Just because the test gives a significant
result does not mean that the ordinal data now shows exact intervals. It still continues
to show results that are greater than or less than, but not by any particular amount.

Additionally, underlying the use of inferential statistics is an argument that goes
back to the methodological issues in Chapter 4. Post-positivist researchers object
that parametric statistics reflect the law-seeking normative assumptions of positivist
research, and that this is contrary to the effort in naturalistic research to emphasise
the uniqueness of participants. They will often admit non-parametric statistics, which
do not assume normalcy and are useable with small non-normal samples.

The first step in choosing which statistics to use is to identify the data’s measurement
scale. Table 16.1 shows some descriptive and inferential statistics that can be used
with the key measurement scales. For example, binary data should use the mode (the
most common score) as the measure of central tendency, which can be illustrated with
column graphs. The binomial inferential test can be used to test significance. In fact,
there are scores of statistical tests for all five measurement scales, but this book only
identifies a few basic ones that are commonly used with nominal and ordinal data and
are acceptable for much basic research. The table shows non-parametric (NP) statistics as
well as common parametric (P) ones, which can be used with the lower data levels.

For computer analysis of numbers, there is the same choice as for words: use
whichever package is available on your system, buy a programme and learn how to
use it, or use a spreadsheet. Common statistics packages include BMDP, SAS and SPSS,
but they are complex. Spreadsheets are a practical, but sometimes clunky alternative.
I will use examples from Excel 2003, which has extensive capabilities for both descrip-
tive and inferential calculations. To use them, make sure that the statistic functions are
activated at ‘Tools’ > ‘Add-Ins’ > ‘Analysis Tool Pak’. This will activate a ‘Data Analysis’
command in the ‘Tools’ menu, which will give some of the statistical tests available.
You can also use the Excel search function and search ‘statistical analysis’, ‘function’
and ‘formula’ to get overviews, and make use of the ‘Help’ function to search particular

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170 Basic ReseaRch Methods

measures such as ‘mean’ or ‘chi square’. ‘Insert’ > ‘Function’ > ‘Statistical’ also lists the
available functions.

The first step is to input the data into spreadsheet cells. Beforehand, set up a check
of data entry mistakes at ‘Data’ > ‘Validation’, which will alert you to any entries outside
the data range, but check entries anyway as this tool will not alert you to mistakes within
the permissible range. With a questionnaire, column headings should be the question
numbers that contain numerical responses. Row names should be the questionnaire
code numbers. Perform statistical functions on columns (which now contain all the
answers to particular questions) at cells underneath each one.

Table 16.1 Basic statistical measures

Scale Descriptive statistics Function

Binary 1. Mode (NP)
2. Column graphs

1. Most frequent score
2. Visual comparison

Nominal 1. Median (NP)
2. Mean (P)
3. Standard error of the mean (P)
4. Column graphs
5. Line graphs
6. Pie charts

1. Centremost score
2. Arithmetic average
3. Statistical error in the sample mean
4. Comparisons
5. Trends
6. Proportions

Ordinal 1. Mean (P)
2. Standard error of the mean (P)
3. Histograms
4. Pie charts

1. Arithmetic average
2. Statistical error in the sample mean
3. Comparisons
4. Proportions

Inferential statistics Function
Binary Binomial test (NP) Sample vs population proportions

Nominal 1. Chi square (NP)
2. Contingency coefficient (NP)
3. t test (P)
4. F test (analysis of variance or ANOVA) (P)
5. z test (P)

1. Observed vs expected frequency
2. Correlation between two variables
3. Two sample means
4. Three-plus sample means
5. Sample vs population means

Ordinal 1. Chi square (NP)
2. Spearman rank correlation coefficient (NP)
3. Kendall coefficient of concordance (NP)
4. t test (P)
5. F test (ANOVA) (P)
6. z test (P)

1. Observed vs expected frequency
2. Correlation between two rankings
3. Correlation among three-plus rankings
4. Two sample means
5. Three-plus sample means
6. Sample vs population means

Source: Author.

Note: NP: non-parametric; P: parametric.

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quantitative data 171

16.2

Descriptive statistics, tables and charts

Now you are ready to find out what the data says. The following list has several ways
of using descriptive statistics to summarise data. Each of these adds more information,
so check the data against each one:

1. Central tendency or ‘average’ (mean, median or mode).
2. Distribution or indicators of the spread of the data (standard deviation, quartile

deviation).
3. Outliers or extremes (the topmost and bottommost scores).
4. Range (the difference between the top and bottom scores).
5. Non-conforming cases (data that appear not to fit the pattern).

Table 16.2 has Excel functions for common descriptive statistics.

Table 16.2 Guidance on descriptive statistics

Function Purpose

AVERAGE Arithmetic mean
CORRELATION Correlation between two sets of data
COUNTIF Tallies cells with data meeting particular requirements
FREQUENCY Tallies cells containing particular numbers
MEDIAN Middlemost number
MODE Most frequent number
PERCENTILE Percentile values
PERCENTRANK Percentile ranks
QUARTILE Quartile deviation
RANK Ranks
STDEV Standard deviation
SUM Totals
‘Data’ > ‘Filter’ Identify particular values
‘Data’ > ‘Sort’ Order data alphabetically or numerically

Source: Author.

Next, use appropriate summary statistics to set up tables in the word processor
and then use the table data to create graphs. Tables and charts are equivalent to
paragraphs. Just as a paragraph deals with one main idea, so does a table or a chart
by presenting data about one particular aspect of the research. Usually, a table will
present numbers set out in rows and columns. A chart or figure will present ideas in
a systematic form such as a diagram. Normally, each of these will require one or two

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172 Basic ReseaRch Methods

written paragraphs of explanation. The following list will help improve the quality of
tables and charts:

1. Avoid too much information in each one; if necessary, split data or ideas into
smaller units and present them in two or three tables.

2. Identify each table, chart or figure.

(a) Number each one.
(b) Use a clear and accurate title.

3. Label each row and column accurately, and show the units used in each row
and column in the table (for example, per cent or No.).

4. Space out tables and figures—avoid clutter.
5. Keep tables on one page.

(a) Do not split tables, figures or charts over more than one page. If they are
longer than a full page, divide the data into two tables.

(b) Start tables straight after the end of the paragraph that first refers to them
if they fit on that page. If not, place them after the paragraph that carries
onto the next page.

6. Cite sources: cite the sources for your information in a table note, including
your own research.

Table 16.3 demonstrates these points.

Table 16.3 Reporting of most troublesome incident to the police

Reported
Arawa

2004 (%)
Arawa

2005 (%)
Arawa

2006 (%)

Buka

2004 (%)
Buka

2005 (%)
Buka

2006 (%)

Yes 16 15 35 23 18 19
No 84 85 65 77 82 81
Total 100 100 100 100 100 100

Source: Guthrie et al. (2007: 55).
Note: Q.4.13. Arawa 2004 N = 106, Non-response = 66%; 2005 N = 106, Non-response = 65%;

2006 N = 75, Non-response = 75%. Buka 2004 N = 125, Non-response = 57%;
2005 N = 94, Non-response = 68%; 2006 N = 74, Non-response = 75%. The high non-
response rates derive mainly from respondents who gave nil responses to S.3.

The safest Word graph types are:

1. Clustered column (to compare scores).
2. Line (for trends).
3. Pie charts (for proportions).

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quantitative data 173

For graphs, take time and explore fully the options for each type, setting up the
first graph very carefully. Thereafter, you will have the style you want, into which you
can cut, paste and input new data. This way other such graphs will be much easier
to prepare. You can use Excel to set up the graphs and insert them from Word using
‘Edit’ > ‘Paste Special’. Or, you can use the Word graph functions, which are quite
advanced. With Word, copy the table data into the datasheet, accept the graph provided,
then right click on it to change and/or edit it with ‘Chart Object’ > ‘Chart Type’ > ‘Edit’.
Then, right click the graph again and use ‘Chart Options’.

Use distinct colours if your printer supports them, but it is easy to be carried away
with other options. Some graph types are better for public relations than for research.
In particular:

1. Do not use three-dimensional graphs. They use depth, which implies volume
and distorts the visual perception compared to, say, a bar graph.

2. Set up all vertical axes so that they start at zero and do not distort rates of
change.

3. Use logarithmic line graphs to show rates of change.

16.3

Inferential statistics

Inferential statistics allow inferences to be drawn about the similarities or differences
between the sample and the population, or between samples or between subsets of a
sample. Testing can use:

1. Means (for example, t and z tests).
2. Variance (for example, analysis of variance or ANOVA).
3. Distribution (for example, chi square).
4. Correlations (for example, Spearman rank correlation coefficient).

An important point to note is that a correlation measures a relationship between two
variables, usually on a scale that ranges from +1.00 to –1.00. A high positive correlation
means the variables change in the same direction; a negative correlation means that
they change in opposite directions. Some correlation coefficients can be tested for
significance and correlations can be used for prediction (if Variable A changes, so too
does Variable B). However, this describes an association between the variables and does
not establish cause-and-effect unless measured as part of an experimental design. The
changes could be coincidental. For example, a significant positive correlation might
be found between smoking and alcoholism, but smoking does not cause alcoholism.
Generally, negative correlations occur between health and education, on the one hand,

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174 Basic ReseaRch Methods

and financial poverty on the other. But lack of health and lack of education do not
cause financial poverty; lack of money does.

Key issues to be considered while choosing a significance test or a particular version
of a test are:

1. The measurement level (binary, nominal, ordinal, interval, ratio).
2. Number of sample cases (one sample, two sample, or k (three-plus) samples).
3. Sample type (related or independent).
4. Sample size.
5. Direction of hypothesised difference (one-tail or two-tail).

Statistical tests can be extremely complicated and require algebra to be understood.
However, many of the basic statistical tests are quite straightforward and can be
calculated with Excel (Table 16.4). Study carefully the ‘Help’ material on each function
because it is written in mathematic language. You will also have to calculate and enter
degrees of freedom to get test results (I would like to be able to explain what they are,
but like many, I have failed to understand the numerous definitions. Have faith and
just do what the textbooks say!).

Table 16.4 Guidance on inferential statistics

Function Purpose

CHIDIST Chi square statistic
CHITEST Chi square significance
CORREL Correlation coefficient
CRITBINOMIAL Binomial test
FDIST F test statistic
FTEST F test significance
NORMSDIST z score
TDIST Student’s t statistic
TTEST Student’s t test significance
ZTEST z test significance

Source: Author.

16.4

Presenting data

The steps in quantitative data presentation are similar to those used for qualitative data:

1. Describe the numerical results (with descriptive statistics in the data chapters).
2. Analyse (with inferential statistics in the data chapters).
3. Interpret (with written interpretations later in the conclusions chapter).

Clarity and brevity are important, as is neutrally toned language. C
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quantitative data 175

Box 16.1 is an example combining many of the features discussed in the previous
section. This is a synthesis of results from 16 crime surveys on one particular indicator
for levels of reported property crime.

1. The first sentence in the box describes the indicator by defining the terms.
2. The second sentence states the importance of the indicator.
3. The first dot point is a brief analysis using statistical significance. Chi square

was used to test whether or not there was a common national level of the crime,
which the result showed was not the case; the point being that normalcy was
tested, not assumed.

4. The second paragraph briefly summarises the data shown in the graph, identifying
the top, middlemost and lowest ranking towns to express both the average and
extreme cases, and then states the national mean. The third sentence comments
on exceptions and patterns.

5. The graph presents the data. The heavy line shows the national mean, with the
columns showing the towns relative to the mean and to each other. The graph
was edited to insert the raw percentages at the top of each column so that a
separate table was unnecessary.

6. The paragraph following the graph discusses an issue that arose, and then briefly
expresses what it meant to the respondents by quoting some of their comments,
thus adding qualitative data to the quantitative. With other indicators, tables
were used to present the data for the top, middlemost and lowest ranking towns
to demonstrate the amount of variation.

16.5 Summary

The most basic social science data can be expressed numerically and tested statistically.
A strength of quantitative research is that its detailed rules encourage intellectual
discipline.

Quantitative data principles

1. Numerical data expresses quantities and variables.
2. Numbers particularly come from some types of available data, structured

observation, questionnaires and tests.
3. In social science research, quantities are usually classified on the nominal and

ordinal measurement scales.
4. Inferential statistics are used to test null hypotheses. Parametric tests are based

on an assumption of a normal distribution in the data. Non-parametric tests do
not make an assumption of normalcy.C

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176 Basic ReseaRch Methods

Box 16.1 ComBining types of analysis

Levels of Property Crime

‘The…indicator for particular types of crime victimisation was the mean percentage of households
that had a member who was a victim of stealing property (Graph). In all the surveys, theft was the
most common type of crime victimisation reported.

• The differences from the national mean were highly significant (X2 = 111.5, df = 15,
p > .001), ie. the towns had very different levels of victimisation involving theft.

‘The highest rate was in Kainantu, with two-thirds (67%) of households in 2008 being victims. Port
Moresby in 2004 was the middlemost town, with 38%. Arawa in 2006 had 8%. The national mean
was 38.3% of households being victims in the previous 12 months. Notable was the relatively high
rate for Kokopo, and the declines in Arawa and Buka from 2004 to 2006, which were statistically
significant (Arawa X2 = 17.8, df = 1, p = .001; Buka X2 = 12.5, df = 1, p > .001).

Graph: Household the victim of stealing

‘During the surveys, informal comments were made occasionally to the researchers that petty theft
was not a “real” crime: either it was a traditionally derived behaviour because private ownership
was not a feature of tribal life; or it was so common as to be part of daily life and not really a
crime. However in all the surveys, comments in the interviews about the most troubling crimes that
had occurred to respondents in the previous year showed that stealing was a constant irritation…
For example:

• I paid a lot for the bicycle.
• We paid a lot of money for that generator.
• Those bags of dry beans were worth K1000…
• When I recovered the property it was damaged.
• They stole our clothes on the line.
• Truck parts are really expensive today. People lack respect for others’ belongings.’

Source: Adapted from Guthrie (2008: 30–31).

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quantitative data 177

5. The mathematical assumptions of many common parametric statistics are
robust but the results should still be interpreted according to the underlying
measurement scale.

Descriptive statistics, tables and charts

1. Summarise data using central tendency, distribution, outliers, range and non-
conforming cases, as appropriate.

2. Tables and charts present data about one particular aspect of the research.
Simple column, line and pie charts are the most useful graphs.

3. You should: (a) not put too much information into each table, chart or figure;
(b) identify each one; (c) accurately label each row and column; (d) space out
tables and figures; (e) keep tables on one page; and (f) cite sources.

Inferential statistics

Key issues in choosing a test or a particular version of a test are the measurement
level, number of sample cases, sample type, sample size and direction of hypothesised
difference.

Presenting data

Describe the numerical results with descriptive statistics, then analyse with inferential
statistics, then make written interpretation. Clarity and brevity are important, as is a
neutrally toned presentation.

To repeat a key point, just because we are careful with our math does not necessarily
mean that our research is strong. If we are intellectually sloppy and overlook valid
alternative variables, the research is of little value despite the statistics. In other words,
statistical analysis can add to the precision and reliability of our research, but we need
validity too. Both analytical rigour as well as technical strength are essential.

16.6 Annotated references

Cozby, P. (2009). Methods in Behavioral Research, 10th edition. Boston: McGraw Hill.
A good clear psychology text with chapters on statistics.

Gaur, A. and S. Gaur. (2009). Statistical Methods for Practice and Research: A Guide to
Data Analysis Using SPSS, 2nd edition. New Delhi: Response.Co

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178 Basic ReseaRch Methods

This book takes you through using the SPSS package for the types of test outlined
in this chapter and much more.

Israel, D. (2008). Data Analysis in Business Research: A Step-by-Step Nonparametric
Approach. New Delhi: Response.
Contains the major non-parametric statistical tests that can be used with small

samples in all social science subjects, assuming little knowledge of statistics.

Kanji, K. (2006). 100 Statistical Tests, 3rd edition. New Delhi: Vistaar.
This book covers the most commonly used statistical tests, both parametric and

non-parametric. For each test, it describes the purpose, limitations and assumptions,
a worked example and the calculation.

An internet search will help you obtain much guidance on using Excel for data
analysis. There are also several books in the market showing how to use it.

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CJ598

Week 3 Assignment

Evaluating Research Methods: Data Collection and Analysis

For this assignment, you will evaluate both qualitative and quantitative approaches to your applied research topic and determine the most appropriate method of data collection and analysis for your proposal. Select three qualitative studies and three quantitative studies from your work in developing the literature review portion of your proposal. Provide an overview of the data collection and analysis procedures for each of the studies. Compare and contrast the findings from each and discuss the application of these findings to developing trends related to your research proposal topic. Finally, discuss the most appropriate method and design for data collection and analysis for your own research proposal, including justification and support from scholarly sources. Your paper must be 4-6 pages in length and include each of the following components:

· An overview of the data collection and analysis procedures for each of three qualitative and three quantitative studies related to your research proposal topic

· An evaluation and comparison of the findings from each of the qualitative studies

· An evaluation and comparison of the findings from each of the quantitative studies

· An application of the findings from all six studies to the current and developing trends related to your proposal topic

· Based on your analysis and evaluation, identify the most appropriate method and design for data collection and analysis for your research proposal, and provide support and justification from both peer-reviewed and methodological sources. Keep in mind that your decision should be based on (a) how best to address the research problem/question, and (b) how best to contribute to current knowledge and trends related to your topic.

Be sure to write in a scholarly and objective tone, avoiding the use of first person, personal pronouns, contractions, and colloquial or conversational language. Use citations from scholarly, peer-reviewed sources throughout to support your content and credit sources of information and ideas.

Note: This assignment requires outside research. Use at least four scholarly, peer-reviewed sources in addition to the Reading material throughout your assignment to support your content and credit sources of information and ideas.

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