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Introduction

Last week we talked about qualitative analysis, which is the non-numerical examination and interpretation of observations. This week we're moving into quantitative analysis which involves the techniques by which researchers convert data into a numerical form and subject it to statistical analyses. Quantitative researchers are primarily concerned with measurement, causality, generalization, and replication. Measurement is an easy one to understand as researchers using quantitative methods are typically taking a numerical approach to address research problems. When it comes to causality, the researcher isn't just trying to show what is occurring, but they are also interested in explaining why it's occurring.

Also in quantitative research, the researcher wants to show that their work can be generalized beyond the boundaries of their study, for example a questionnaire on racism is something that the researcher hopes can be generalized beyond the people that answered the survey. Finally, quantitative work should be replicable, as a way to demonstrate the accuracy of the claims being made. The APUS library offers a good description of quantitative methods at  http://apus.libguides.com/research_methods_guide/research_methods_quantitative . Don’t forget the Sage Research Methods link you learned about last week as well. You will find an amazing amount of resources at your fingertips!

Research Topic

Frequently, a topic is not restricted to being explored by either a qualitative or quantitative approach.  In many cases we can examine a topic using a qualitative design, a quantitative design, or a design that incorporates both qualitative and quantitative designs (also called  mixed methods research  because you are blending the two approaches).    Pretty much any topic can be structured in a qualitative or a quantitative design. Both designs are very useful in research and sometimes what dictates what we will do depends on the amount of funding or who is funding the research. For example, you may want to dive into all aspects of the life of your experimental and control group members. However, with 50 persons in each group and only $10,000 to conduct your research, all of the time and energy required to conduct interviews for such a project is not feasible. Because of this you may be limited as to how deep you can go, how much information can be collected, and from how many subjects. As such, a quantitative approach is often the norm in our research as we are able to collect large amounts of data from large groups with controlled and minimized costs. Also, it's important to note that your funding source may lean more towards one design or the other and as they control the purse strings, this will then cause them to have some influence on the path you pursue.

Quantitative and Qualitative Data

What's nice about the research that we design is that we are not necessarily restricted to just a quantitative or just a qualitative design to an issue.  A combination of qualitative and quantitative data does seem ideal in many instances.  No matter how much you know about a certain topic to be researched, you still will not be able to come up with every possible response to or aspect of a topic.  Also even if you could, you may have over 50 fixed choice quantitative responses – not ideal under most situations.  By combining qualitative and quantitative data, you can gather the quantitative that will probably account for over half of the information you seek.  By gathering qualitative data, you capture what was missed in the quantitative data, and also paint a more vivid picture of all the findings for the consumer of the research. 

In general, you'll find that many times a multi-pronged approach, gathering both quantitative and qualitative data, is often most desirable.  At this point in the course, you should see that there are many different ways to look at and to frame a project.  No one approach is necessarily better than another and to some degree to get a proper picture of the issue, one will need to approach it from a variety of angles.

Qualitative

It is important to note that if you start gathering qualitative data, it can be converted to quantitative data.  By establishing coding scheme(s) and applying this scheme(s) to the qualitative data, quantitative data can be produced.  The researcher can now analyze this quantitative data utilizing a variety of tools and techniques to aid in the understanding and interpretation of the initial qualitative findings.  One example of this comes to us from leadership profiling.  When we attempt to profile leaders from a distance we can use a method called Operational Code (OpCode) which focuses on assessing a leader's instrumental or philosophical beliefs.  OpCode can be assessed both qualitatively by conducting a content analysis of documents, but it can also be conducted quantitatively using the Verbs in Context System (VICS) which is a coding scheme that looks for certain words in speech.

Quantitative

When carrying out quantitative research there are a number of steps involved.  Like with qualitative research we first start off with theory.  Next we work to deduce a hypothesis based on the theory.  It's important to note that some quantitative research does not require the specification of a hypothesis.  Instead "the theory acts loosely as a set of concerns in relation to which the social researcher collects data" typically hypotheses are specified in experimental research as this involves hypothesis testing (Bryman, 2012, p. 161).  From here we then move into the research design phase in the research process.  This is where we work to operationalize (or measure) our concepts.  Once this has been determined we then work to determine what research sites we plan to use and also determine our research subjects/respondents.  After the subjects have been determined the investigator can then administer the research instruments and collect their data.  After the data has been collected the investigator then moves into the data processing and analysis phases.  Finally, they are then able to write up their findings and conclusions.

Survey Research

One of the most popular ways to collect data is through a survey. Think about how often you are asked to take a survey...when you leave a store, when you purchase something online, after talking to your cell phone carrier on the phone they ask if you are willing to take a survey, etc. They are everywhere. Why? The general features are easy to work with.

· They are versatile - Surveys can be designed to study almost any social issue and we can use them to learn about people, organizations, criminal behavior, etc.

· They are efficient. Data can easily be collected from a large number of people relatively inexpensively and quickly.

· Often times they can be generalizable as we are usually able to get information from a representative sample of a large population (Bachman and Schutt, 2012).

Students should understand the basic terminology involved in survey research including the following: Survey Research - Collection of information from a sample of individuals through their responses to questions.

QUESTIONNAIRE

Survey instrument containing the questions in a self-administered survey.

RESPONDENT

A person who answers questions on a survey.

RESPONSE RATE

Percentage of persons surveyed who actually complete a survey.

(Bachman and Schutt, 2012)

Please note that probably the most important thing to know about a survey is the development of the survey questions is key. This will affect the reliability and validity of any data collected. Second to this is how the interview or survey is administered; this too is important and relates directly to the reliability and validity of the survey results. Keep in mind that survey questions are answered as part of a questionnaire, not in isolation from other questions. The context created by the questionnaire and how the questionnaire is administered impact how individual questions are interpreted; and whether they are even answered. Finally, we must give very careful attention to design of the questionnaire as a whole, as well as to each individual question that it includes. Remember, to do this type of work at APUS you will need IRB approval!

Another method that we can pull from to carry out quantitative research is through secondary data analysis.  There is a tremendous amount of research done each year that makes use of this method. This type of research uses pre-existing data to perform the study in question rather than going out and collecting new data for the inquiry. In effect, the data have already been collected but they will be analyzed in a different way or can be used to answer a different research question than was originally intended by those who collected the data in the first place. This happens all of the time and it's a great way to do research.

The major types of secondary data analysis used in social science research include:

· Surveys

· official statistics

· official records

· historical documents

The most common sources of secondary data are social science survey data collected in studies funded by federal and state government. Other common sources include official records maintained by government agencies for administrative, rather than research, purposes - for example, police department arrest data. (Data such as this may also still need IRB approval!) Often what happens is a government agency - like the National Institute of Justice - will fund data collection and research. After the data is collected and several reports are written, the government agency then asks for a clean set of the data collected; they then hold this for other researchers that may want to analyze the data. Other researchers can request a portion of the data to answer certain research questions. In fact, the government agency encourages use of the data to help with our understanding of various social science and social policy questions. They will even sometimes fund researchers' projects that analyze secondary data.

Validity and Reliability

You should know by now that validity and reliability are two very important research terms. To assess validity in a study we are essentially asking if we are studying what we think we are studying? Are we measuring what we think we are and do our measures really represent the concepts that we think they do?

Reliability is achieved when a measure or study yields consistent scores or observations on different occasions and/or different locations (city to city for example).  Basically we're concerned with the consistency of the measures that are in use (Bryman, 2012).  A study and or measure must be reliable in order to be valid.

Some options for testing reliability include:

TEST-RETEST METHOD (STABILITY)

Where a study or measure obtains the same results at two different times.

INTER-ITEM (INTERNAL RELIABILITY)

When we use multiple items to measure a single concept.

ALTERNATE FORMS

Compare slightly different versions of measures.

INTER-OBSERVER CONSISTENCY

Use more than one observer to measure the same thing.

Validity refers to whether or not the test measures what we think it measures.  There are a number of ways to determine measurement validity including face validity, concurrent validity, predictive validity, construct validity and convergent validity (Bryman, 2012).  Face validity is what researchers need to establish within their work as this basically shows that you are "getting at the concept that is the focus of attention." (Bryman, 2012, p. 171). 

Conclusion

In this lesson, we moved into quantitative analysis which involves the techniques by which researchers convert data into a numerical form and subject it to statistical analyses. In the next lesson, we will start thinking about how we'll be working with our data.

References

Bachman, Robert and Russell K. Schutt. (2012). Fundamentals of Research in Criminology and Criminal Justice.  Thousand Oaks, California: Sage.

Bryman, Alan. (2012). Social Research Methods 4th ed. New York: Oxford University Press.