Quantitative and Qualitative Data Analysis MID Semester Test

Quantitative and Qualitative Data Analysis

MID Semester Test

 

Submitted as the Requirement to Fulfill an Assignment of Qualitative Data Analysis Course

Under the Direction of Bachrudin Musthafa, M.A., Ph.D.

 

Submitted by:

Rezki Firdaus

1407335

 

ENGLISH EDUCATION DEPARTMENT

SCHOOL OF POSTGRADUATE STUDIES

INDONESIA UNIVERSITY OF EDUCATION

2015

Research is “a process of steps used to collect and analyze information to increase our understanding of a topic or issue” (Creswell, 2012:3). So, what is the difference between quantitative and qualitative research? Quantitative research generates numerical data or information that can be converted into numbers. Qualitative Research, on the other hand generates non-numerical data.

A.           The purpose in quantitative and qualitative research

Quantitative research is the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. The objective of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining to phenomena. The process of measurement is central to quantitative research because it provides the fundamental connection between empirical observation and mathematical expression of quantitative relationships. Quantitative data is any data that is in numerical form such as statistics, percentages, etc. Quantitative research is generally made using scientific methods, which can include:

·       The generation of models, theories and hypotheses

·       The development of instruments and methods for measurement

·       Experimental control and manipulation of variables

·       Collection of empirical data

·       Modeling and analysis of data (Available at https://en.wikipedia.org/w/index.php?title=Quantitative_research&oldid=689548910, retrieved 2015-11-10 22:26:09)

Qualitative research is interested in understanding the meaning people have constructed, that is, how people make sense of their world and the experiences they have in the world. Further, qualitative research is research which is the collection, analysis, and interpretation of comprehensive narrative and visual data to gain insights into a particular phenomenal interest (Auerbach & Silverstein, 2013). In other words, this is an in-depth study that collecting data from observation or analysis people in their natural setting. Further, qualitative research is to cover new ideas and insight. So that is why the researcher must search and explore with a variety of methods until a deep understanding is achieved. To achieve that result, data collection is largely determined by the nature of the problem by using primary textual data, such as observation, interview, and document analysis. Then, the data that are collected should contribute to understanding the phenomenon. Interpretive analysis is used to analyze the data. It is contrasted with quantitative research which is conducted to confirm theories by using data.

The purpose in quantitative and qualitative research;

Quantitative Research usually used to test a certain hypothesis through statistical procedure or quantify the data to create generalization based on the findings from a sample to the population interest. Quantitative methods are used to gather data for the purpose of analyzing quantity and numbers, and deriving meaning and understanding of these. Quantitative methods are useful in providing an understanding of what a phenomenon is occurring. They also lend themselves to a deductive research approach which means that they begin by taking existing theories and testing them in a top-down approach to research. (Available at http://atlasti.com/quantitative-vs-qualitative-research/, retrieved 2015-11-10 22:22:51)

Qualitative Research purpose to provide new insight into the setting of a problem, generating ideas and/or hypothesis for later qualitative research. In other words, it aims at creating a hypothesis of what the data show to the researcher without any intention to generalize the findings. Moreover, it conducted to create or revise the current theory based on the collected data. So, qualitative research is to delineate some of the essential qualities of complex social phenomena. Many concepts in organizational theory, such as learning, replicating routines, power, authority, dynamic capabilities, or chaos, involve intricate webs of causes, effects, processes, and dynamics: they are about the qualities. Qualitative analysis characterizes these webs so we can appreciate what the phenomenon is really like in practice, how it works, and how it is affected by other patterns in the organization. (Available at http://www.rangahau.co.nz/method/74/, retrieved 2015-11-10 22:29:54)

B.            Roles of theory in quantitative and qualitative research

All research contains theory in some form, and social work research is no exception. Much research is clear about the theories being used, and the ways they are applied within the formation of the project. Theory will manifest itself to some degree:

ü The theoretical approach itself (the methodology)

ü The arguments about what might happen

ü The approach to the fieldwork or data-gathering

ü The analysis and synthesis of the findings.

The role of theory in quantitative research becomes apparent on examination of a body of work, the choice between comparison groups or highlighting the variability in a single group, choosing approaches for generalizability or context specificity, and remaining “distant” or participating in action research (Carter & Hurtado, 2007). Quantitative research is not wholly objective and that there are ways in which autobiography can intersect with research; critical quantitative approaches identify discrepancies between theory and fact; and there are positives and negatives for comparative group versus context-specific approaches to understanding group differences.

The role of measurement in quantitative research is somewhat divergent. Measurement is often regarded as being only a means by which observations are expressed numerically in order to investigate causal relations or associations. However, it has been argued that measurement often plays a more important role in quantitative research. This is because accepting a theory based on the results of quantitative data could prove to be a natural phenomenon. The theory and definitions which underpin measurement is generally deterministic in nature. (Available at https://en.wikipedia.org/w/index.php?title=Quantitative_research&oldid=689548910 accessed 2015-11-10 23:02:58)

The choice of research design is based on the goals of the study and a solid review of the literature. Quantitative research design utilizes deductive reasoning, which begins with identifying the theoretical framework that will provide structure and guide the research project. The theoretical framework is presented in earlier sections of a quantitative research proposal to establish the grounds for the study. The theoretical framework will direct the research methods we choose to employ. The chosen methodology should provide conclusions that are compatible with the theory.

The role of theory were examines the underlying philosophical assumptions of qualitative research methods, and the implications that these assumptions have for framing a research problem, data collection, analysis, writing, and other dissemination strategies. It also provides some basic opportunities to attain practical, hands-on experience with developing research questions, techniques for data collection, and data analysis.

The role of theory in qualitative research is often underplayed but it is relevant to the quality of such research in three main ways. Theory influences research design, including decisions about what to research and the development of research questions. Theory underpins methodology and has implications for how data are analyzed and interpreted. Finally, a theory about a particular issue may be developed, contributing to what is already known about the topic that is the focus of the study (Kelly, 2010).

Roles of theory in quantitative and qualitative research;

Quantitative Research theory is very fundamental. It is because the research questions and the hypothesis in this research are commonly formulated based on the theory. Theory in quantitative research is usually used to confirm the data or finding.

Qualitative Research theory plays a minor role in this kind of research. This is only used as the researcher’s prior knowledge in order to create a better picture when the researcher investigates the phenomenon and to justify that the phenomenon under the investigation is worth an exploration. Commonly, data or findings in qualitative research are results new insight or theory.

C.           Research questions in quantitative and qualitative research

Choosing a research question is the central element of both quantitative and qualitative research and in some cases it may precede the construction of the conceptual framework of the study. In all cases, it makes the theoretical assumptions in the framework more explicit, most of all it indicates what the researcher wants to know most and first. The student or researcher then carries out the research necessary to answer the research question, whether this involves reading secondary sources over a few days for an undergraduate term paper or carrying out primary research over years for a major project. When the research is complete and the researcher knows the (probable) answer to the research question, writing up can begin (as distinct from writing notes, which is a process that goes on through a research project). In term papers, the answer to the question is normally given in summary in the introduction in the form of a thesis statement (Available at https://en.wikipedia.org/w/index.php?title=Research_question&oldid=689974439 retrieved 2015-11-10 23:12:04).

Types and purpose. The research question serves two purposes: It determines where and what kind of research the writer will be looking for and It identifies the specific objectives the study or paper will address. Therefore, the writer must first identify the type of study (qualitative, quantitative, or mixed) before the research question is developed. (Available at https://en.wikipedia.org/w/index.php?title=Research_question&oldid=689974439 retrieved 2015-11-10 23:12:04)

There are three main types of questions that a researcher can ask when writing a quantitative study. They are: Causal, Descriptive, Predictive (Davie, n.d.-b).

Causal Questions. Causal questions are exactly what they sound like – a question that tries to compare two or more phenomena and determine (or at least suggest) a relationship between the two (or more). Quantitative questions rely on an independent variable or one that remains the same. (Davie, n.d.-b)

Descriptive Questions. Once again, these are pretty much what we would expect them to be: descriptive research questions ask “how often?”, “how much?”, or “what is the change over time or in a different situation?” questions. Many times descriptive questions involve the degree or the existence of the relationship that exists between two or more variables. Descriptive questions usually lead to further questions that our study was never meant to answer and it is a BIG MISTAKE to suggest so. The answer “why” is an entirely different study and almost always a qualitative one. (Davie, n.d.-b)

Predictive Questions. Predictive questions are questions that try to predict (no way!) whether one or more variables can be used to predict an outcome. Predictive questions and studies are always highly controversial, be sure to cover all our bases when trying to predict something, more often than not there are about variables that come together to create an outcome and trying to link only a few of those to always get the same outcome can be a huge mistake (especially in social science). (Davie, n.d.-b)

As a general suggestion, especially early on, stay away from predictive studies. They can be some of the most fun, but more often than not people get far too excited and overstep the bounds of their study. (Davie, n.d.-b)

A quantitative study seeks to learn where, or when, so the writer’s research must be directed at determining the where, or when of the research topic. Therefore, when crafting a research question for a quantitative study, the writer will need to ask a where, or when questions about the topic. For example: Where should the company market its new product? Unlike a qualitative study, a quantitative study is mathematical analysis of the research topic, so the writer’s research will consist of numbers and statistics (Cresswell, 2009).

In qualitative research, research questions serve to narrow the purpose. There are two types: Central; the most general questions we could ask. Sub-questions; Subdivides central question into more specific topical questions and Limited number.

Use good qualitative wording for these questions; Begin with words such as “how” or “what”, tell the reader what we are attempting to “discover,” “generate,” “explore,” “identify,” or “describe”, ask “what happened?” to help craft our description, ask “what was the meaning to people of what happened?” to understand our results, and ask “what happened over time?” to explore the process. Avoid words such as: relate, influence, impact, effect, cause.

Scripts to help design qualitative central and sub-questions: Central question script (usually use only one): “What does it mean to _________________ (central phenomenon)?” or “How would ______________ (participants) describe (central phenomenon)?”. Sub-question script: “What _________ (aspect) does __________ (participant) engage in as a _____________ (central phenomenon)?” (Davie, n.d.-a; Creswell & Clark, 2004)

In qualitative research questions things that we can do is use open-ended questions, avoiding leading questions, probe issues in depth and let the information lead. Use open-ended questions that allow the respondent to answer presented or applied choices. Avoiding leading questions allow people to answer their own terms voicing their own views, values and experiences. Letting the informant lead me and that we keep the conversation focused on a topic, while giving the informant room to define the content of the discussion. The rule is: get informant to a topic of interest and get out of the way. Let the informant provide information that he or she thinks is important (RAP, 2000).

A qualitative study seeks to learn why or how, so the writer’s research must be directed at determining the what, why and how of the research topic. Therefore, when crafting a research question for a qualitative study, the writer will need to ask a why or how question about the topic. The sources needed for qualitative research typically include print and internet texts (written words), audio and visual media (Cresswell, 2009).

Research questions in quantitative and qualitative research;

In Quantitative Research, the research questions are commonly about the description of the relationship or an explanation among variables, more specific and narrow study, deductive, static, outcome-oriented, confirming theories. In other words, the research questions will be answered through the explanation of how the variables each other’s.

In Qualitative Research, the research questions which are released in this paradigm commonly an exploration about a phenomenon. It means that the research questions required an answer that shows the detailed information about the problem. Usually, qualitative research represents much new information.

D.           Kinds of data to be collected in quantitative and qualitative research

Data collection approaches to qualitative research, usually involves: direct interaction with individuals on a one to one basis or direct interaction with individuals in a group setting

Qualitative research data collection methods are time consuming, therefore data are usually collected from a smaller sample than would be the case for quantitative approaches - therefore this makes qualitative research more expensive. The benefits of the qualitative approach are that the information is richer and has a deeper insight into the phenomenon under study. The main methods for collecting qualitative data are: Individual interviews, Focus groups, Observations, Triangulation, Documentary research, Case study and Action Research.

Interviews. In contrast to the survey questionnaires, qualitative interviewing aims to delve deep beneath the surface of superficial responses to obtain true meanings that individuals assign to events, and the complexity of their attitudes, behaviors and experiences. There are two types of interviews, unstructured and semi-structured, their usage depending on the aim of the study.

Unstructured interviewing allows the respondent to tell their own stories in their own words, with prompting by the interviewer. Lofland summarized the objective of the unstructured interview as being, 'to elicit rich, detailed materials that can be used in qualitative analysis. Its objective is to find out what kind of things are happening rather than to determine the frequency of predetermined kinds of things that the researcher already believes can happen' (1971:76) cited in (admin, 2010). In an unstructured interview, the researcher simply has a list of topics that they want the respondent to talk about. But the way the questions are phrased and which order they come will vary from one interview to the next as the interview process is determined by the responses (stories) of the interviewees.

Semi-structured interviews are characterized by topic guides containing major questions that are used in the same way in every interview, although the sequence of the questions might vary as well as the level of probing for information by the interviewer. Semi-structured interviewing is suitable when the researcher already has some grasp of what is happening within the sample in relation to the research topic. However, the researcher should ensure there is no danger of loss of meaning as a consequence of imposing a standard way of asking questions (Fielding & Thomas: 2001 cited in admin, 2010). This could be achieved by conducting pilot interviews (these use broad topic guides with few direct questions) prior to data collection.

Regardless of whether unstructured or semi-structured, the questions posed during the interview should be as open-ended as possible, in order to avoid yes/no or rehearsed answers. Further, the questioning techniques should encourage respondents to communicate their underlying attitudes, beliefs and values that are so central to this method. This can be limited where the interviewee has a lack of awareness/information or is not used to putting feelings into words. The interviewee might feel exposed by questions (in particular where attitudes are probed in sensitive topics such as political attitudes, sexual orientation, borderline or illegal behavior).

On the other hand, interviewees might feel that they need to present themselves in a specific way in order to fit in with their perception of the researcher's requirements, or wish to bring in their own agenda of life-topics that do not fit easily with the aim of the interview. For these reasons, it is important to build a rapport with the interviewee before starting the interview so that both sides can feel more at ease. Different ways of posing questions and using probing and prompting help to elicit more information or steer the interview. More information about how to use these strategies, to develop a topic guide, interviewer effects and recording methods are given in the following references - see Fielding & Thomas (2001) cited in (admin, 2010)for a good overview of issues in qualitative interviewing; see Wengraf (2001) cited in (admin, 2010) for a very detailed discussion of qualitative interviewing.

Focus groups. Focus groups are a form of a group interview with the aim of capturing the interaction between the participants based on topics that are supplied by the researcher (Morgan, 1998) cited in (admin, 2010). The main purpose of focus group research is to evoke a level of respondents' attitudes, feelings, beliefs, experiences and reactions otherwise not available when using methods, such as observation or interviewing. These attitudes, feelings and beliefs may be partially independent of a group or its social setting, but are more likely to be revealed via the social gathering and the interaction created in a focus group. Focus groups are particularly useful when there are power differences between the participants and decision-makers or professionals, when the everyday use of language and culture of particular groups is of interest, and when one wants to explore the degree of consensus on a given topic (Gibbs, 1997) cited in (admin, 2010). For these reasons it is important to make sure that the participants have a specific experience/opinion about the topic to be discussed, and that a specific interview guide is used.

Despite all the potential of focus groups, this method has its limitations. However, these limitations are dependent on the study design and can be reduced by diligent planning. Four of the main limitations are:

(a)     The researcher has less control over the data produced

(b)     The researcher has little control over the interaction other than generally keeping participants focused on the topic

(c)     The researcher can have difficulties in recruiting and assembling the focus group (e.g. finding a date and time for seven busy health care professionals, or resistance from people who are less articulate or confident)

(d)     The researcher cannot assure full confidentiality and anonymity as information is shared in the group.

The practical organization of focus groups requires the following:

·           Planning the recruitment process

·           Negotiating the date and time of the focus group

·           Choosing a venue (a neutral place is usually of advantage; where participants live/work too far apart the focus group can also be conducted via a telephone conference line)

·           Insuring adequate recording facilities

·           Organizing a co-moderator (e.g. to take notes and monitor recording equipment), deciding how many people should be in the focus group (usually six to ten)

·           Informing participants about the potential length of the focus group (usually one to two hours).

·           Being clear about the role of the moderator. This will require the researcher to provide clear explanations of the purpose to the group, ask questions and facilitate interaction between group members (e.g. allowing quieter participants to speak).

Observations. When undertaking observational fieldwork, the researcher is also known as the 'ethnographer' as he/she attempt to discover the practices and meanings that the members of the group under study take for granted (Denzin:1989) cited in (admin, 2010). By observing a group of people, the researcher sets out to identify the meanings people develop about their existence (Bowling:1997) cited in (admin, 2010). In participant observation, the researcher adopts the perspective of those studied. For example, a study might be interested in the rules of the waiting room in a GP practice. The researcher in his/her observing role would adopt the perspective of a patient waiting to be called in to see the doctor. He/she would observe the interaction of the people present. However, this does not mean simply adopting a passive watching role; the researcher might also interact with those that he/she is observing.

Observation can involve a combination of methods, including e.g. unstructured conversations/interviews, notes on observations, recordings (audio and video) and illustrative material (floor maps, information material). Nevertheless, like all data collection methods, observation does have its limitations. These include observer bias (the influence the observer's presence might have on the situation he/she is watching), and the difficulty of replicating the data.

There are a number of points that a researcher needs to be cognizant of before embarking on observational fieldwork, a selection is listed here with references for further reading:

·       Selecting the field setting (Denzin, 1989) cited in (admin, 2010)

·       Gaining access (Hornsby-Smith, 1993) cited in (admin, 2010)

·       Deciding whether participant observation will be concealed (e.g. gaining employment to field setting without informing anyone there about the observation) or open (i.e. being open about the observing role). A famous study of concealed participation was conducted by Rosenhan (1973) cited in (admin, 2010), for a discussion of the methods see Lofland & Lofland (1995) cited in (admin, 2010)

·       Recording the action - field notes (Denzin & Lincoln, 2003) cited in (admin, 2010)

·       Validation of the observations (Fielding, 2001) cited in (admin, 2010)

Triangulation. 'Methodological triangulation', a term coined by Denzin (1989) cited in (admin, 2010), implies that the researcher may use several methods in different combinations in order to gain the most detailed picture of participants' experiences. Triangulation is a characteristic of qualitative design as it allows for multiple views of framing the problem, selecting research strategies and extending discourse across several fields of study. Triangulation requires comparison across data sets and adds validity to the findings.

Document research. Also known as 'documentary analysis', this research method involves the study of existing documents, either to understand their substantive content or to illuminate deeper meanings which may be revealed by their style and coverage. These may be public documents like media reports, government papers or publicity materials, procedural documents (e.g. minutes from meeting, formal letters or financial accounts), personal documents (e.g. diaries, letters, photographs). Researching documents are particularly useful where the history of events or experiences has relevance and where private as well as public accounts are needed. A further reason for drawing on documentary sources is that it is not always possible to engage in direct observation or questioning.

It might be helpful to set up a questionnaire that can be filled in for each document read and analyzed. That way the researcher builds a system of information that is easily accessible and helpful in comparing documents and cross-referencing. This type of questionnaire should be designed to capture the following information:

·       Title of document

·       Date of publication

·       What kind of document is it?

·       First reading: relation to research question (e.g. relevance, importance, type)

·       Context: why was the document written

·       Method: how was the document developed (e.g. purpose and scope, objectivity, entitlement)

·       Content (e.g. accessibility and readability, ownership, argument)

·       Implementation (What was the distribution plan? Timetable for implementation? Evaluation and review?

·       Notes and thoughts

Case Studies. As a research method deployed within social and behavioral science research, the case study is utilized in order to gain an in-depth contextualized examination of social interaction within a single social setting; this may be within an organization or focused on the playing-out of a specific social process (Yin:1994) cited in (admin, 2010). These studies generally utilize several data collection methods, for example, observation, interview and documentary analysis. Case study research is exploratory in nature, and is typically used to generate models and hypothesis of the process under investigation in a specific context, which can then be tested through larger scale quantitative surveys. It is not possible to generalize about the wider social situation directly from the findings of a single case study.

Case studies as utilized within the policy analysis would typically study the micro-workings of large scale organizations, for example an in-depth observation of staff in a single health care unit in order to understand how public health policies are implemented at ground level.

Action Research. This is a collaborative and cyclical (between practical action and research) approach to research, in which both practitioners and researchers (although they can potentially be one and the same) look for a solution to a practice-related problem or to bring about change in a particular setting. Action research methodologies aim to integrate action and reflection, so that the knowledge developed in the research process is directly relevant to the issues being studied. Action research has a long history, going back to social scientists' attempts to help solve practical problems in wartime situations in both Europe and America. Over the past ten years there has been a resurgence of interest, and many developments in both theory and practice. The newer approaches to action research place emphasis on a full integration of action and reflection and on increased collaboration between all those involved in the inquiry project. They include, among other approaches, "co-operative inquiry", "participatory action research", and "action science" or "action inquiry".

The work of Pawson and Tilley (1997) cited in (admin, 2010) and they’re advocating of a 'realist' approach to the evaluation of policy interventions has been of particular influence in the fields of social policy and public health in recent years. Although methodologically pluralist rather than uniquely qualitative in approach, they contribution to evaluative (or action) research is that it places the emphasis on social and cultural conditions necessary for policy changes and interventions to be successfully implemented. An understanding of a specific organizational culture ('context') and the change 'mechanisms' that operate therein, in order to evaluate the 'outcomes' of programs or policy interventions requires that the researcher listen to, and utilizes key stakeholder’s knowledge of these processes. (Admin, 2010)

Quantitative data are typically collected directly as numbers. Some examples include:

·       The frequency (rate, duration) of specific behaviors or conditions

·       Test scores (e.g., scores/levels of knowledge, skill, etc.)

·       Survey results (e.g., reported behavior, or outcomes to environmental conditions; ratings of satisfaction, stress, etc.)

·       Numbers or percentages of people with certain characteristics in a population (diagnosed with unemployed, Spanish-speaking, under age 14, grade of school completed, etc.)

(Available at http://ctb.ku.edu/en/table-of-contents/evaluate/evaluate-community-interventions/collect-analyze-data/main retrieved 2015-11-10 23:37:39)

Data can also be collected in forms other than numbers, and turned into quantitative data for analysis. Researchers can count the number of times an event is documented in interviews or records, for instance, or assign numbers to the levels of intensity of an observed event or behavior. For instance, community initiatives often want to document the amount and intensity of environmental changes they bring about – the new programs and policies that result of their efforts. Whether or not this kind of translation is necessary or useful depends on the nature of what you’re observing and on the kinds of questions your evaluation is meant to answer. (Available at http://ctb.ku.edu/en/table-of-contents/evaluate/evaluate-community-interventions/collect-analyze-data/main retrieved 2015-11-10 23:37:39)

Quantitative data are usually subjected to statistical procedures such as calculating the mean or average number of times an event or behavior occurs (per day, month, year). These operations, because numbers are “hard” data and not interpretation, can give definitive, or nearly definitive, answers to different questions. Various kinds of quantitative analysis can indicate changes in a dependent variable related to – frequency, duration, timing (when particular things happen), intensity, level, etc. They can allow you to compare those changes to one another, to changes in another variable, or to changes in another population. They might be able to tell you, at a particular degree of reliability, whether those changes are likely to have been caused by your intervention or program, or by another factor, known or unknown. And they can identify relationships among different variables, which may or may not mean that one causes another. (Available at http://ctb.ku.edu/en/table-of-contents/evaluate/evaluate-community-interventions/collect-analyze-data/main retrieved 2015-11-10 23:37:39)

Kinds of data to be collected in quantitative and qualitative research;

In Quantitative Research the questions and the responses used in the instruments are predetermined. This makes the data are typically restricted. The collected data are typically numeric. The researcher tends to involve a large number of participants.

In Qualitative Research the data collections are typically started using general questions to elicit responses from the participant which will prompt the subsequent questions which are more and more detailed. The data are in the form of text, picture or document. The data are commonly collected from a small range of individuals.

E.            Instruments used to collect data in quantitative and qualitative research

Type of Approach	Defining Features	Data Collection Implications
Phenomenology	·      Focuses on individual experiences, beliefs, and perceptions. ·      Text used as a proxy for human experience.	·      Questions and observations are aimed at drawing out individual experiences and perceptions. ·      In focus groups, group experiences and normative perceptions are typically sought out. ·      In-depth interviews and focus groups are ideal methods for collecting phenomenological data.
Ethnography	·      Oriented toward studying shared meanings and practices (i.e., culture). ·      Emphasizes the emic perspective. ·      Can have a contemporary or historical focus.	·      Questions and observations are generally related to social and cultural processes and shared meanings within a given group of people. ·      Traditionally, it is associated with long-term fieldwork, but some aspects are employed in applied settings. ·      Participant observation is well suited to ethnographic inquiry.
Inductive Thematic Analysis	·      Draws on inductive analytic methods (this would be same for Grounded Theory below as well). ·      Involves identifying and coding emergent themes within data. ·      Most common analytic approach used in qualitative inquiry.	·      ITA requires generation of free-flowing data. ·      In-depth interviews and focus groups are the most common data collection techniques associated with ITA. ·      Notes from participant observation activities can be analyzed using ITA, but interview/focus group data are better.
Grounded Theory	·      Inductive data collection and analytic methods. ·      Uses systematic and exhaustive comparison of text segments to build thematic structure and theory from a body of text. ·      Common analytic approach in qualitative studies.	·      As above, in-depth interviews and focus groups are the most common data collection techniques associated with GT. ·      Sample sizes for grounded theory are more limited than for ITA because the analytic process is more intensive and time consuming. ·      Note: Many researchers incorrectly label all inductive thematic analyses “grounded theory,” as a default. Technically, they are not the same thing.
Case Study	·      Analysis of one to several cases that are unique with respect to the research topic. ·      Analysis primarily focused on exploring the unique quality.	·      Cases are selected based on a unique (often rarely observed) quality. ·      Questions and observations should focus on, and delve deeply into, the unique feature of interest.
Discourse/ Conversation Analysis	·      Study of “naturally occurring” discourse. ·      Can range from conversation to public events to existing documents. ·      Text and structures within discourse used as objects of analysis.	·      These linguistically focused methods often use existing documents as data. ·      Conversations between individuals that spontaneously emerge within group interviews or focus groups may be studied but are not preferred. ·      Participant observation is conducive to discourse analysis if narratives from public events can be recorded.
Narrative Analysis	·      Narratives (storytelling) used as source of data. ·      Narratives from one or more sources (e.g., interviews, literature, letters, diaries).	·      If generating narratives (through in-depth interviews), then questions/ tasks need to be aimed at eliciting stories and the importance those stories, hold for participants, as well as larger cultural meaning.
Mixed Methods	·      Defined as integrating quantitative and qualitative research methods in one study. ·      Two most common designs are sequential and concurrent.	·      Collection of qualitative data in a mixed methods study can be informed from a wide range of theoretical perspectives and analytic approaches. ·      Researchers must specify up front, and in detail, how, when, and why qualitative and quantitative datasets will be integrated.

(Guest, Namey, & Mitchell, 2012, pp. 8–10)

F.            Data analysis in qualitative and quantitative research

Quantitative and qualitative research differ somewhat in their approach to data analysis. In quantitative research, data analysis often only occurs after all or much of data have been collected. However, in qualitative research, data analysis often begins during, or immediately after, the first data are collected, although this process continues and is modified throughout the study. Initial analysis of the data may also further inform subsequent data collection. For example, interview schedules may be slightly modified in light of emerging findings, where additional clarification may be required. (Burnard, Gill, Stewart, Treasure, & Chadwick, 2008)

Quantitative Data Analysis is widely used in many fields, including economics, sociology, psychology, market research, health development, and many different branches of science. Quantitative data are generally more reliable than qualitative data, and good analysis relies on good data. Quantitative data refer to numbers and statistics, and is very useful in finding patterns of behavior or overriding themes. (Joseph, n.d.)

Quantitative data are numerical. This means you can gather percentages and statistics and analyze your results using graphs and charts. Most data discovered by Quantitative methods are less prone to bias and can often be extrapolated to fit a larger sample size than the data was collected from. (Joseph, n.d.)

Quantitative Data Analysis is applied to the raw data so that the research can be displayed in a friendly fashion, especially to those who do not understand the area the statistics refer to. Quantitative Data Analysis usually starts with taking a sample size and minimum / maximum values, in addition to any preliminary tables that have been drawn up. From here, statisticians will often create averages and figure out the deviation from the expected result – and this helps inform whether or not hypotheses are correct. Dispersion is a very important part of data analysis, along with the aforementioned averages. (Joseph, n.d.)

Numerical Data Analysis usually relies on a set of data that has to be a “fair representation” of the areas that it is concerned with. This means that if you are trying to create a survey on any given subject, you must strive to get the most accurate data in the first instance, as any extrapolation from data with errors will lead to larger errors down the road. The larger the sample size, the more accurate your findings will be, and the more effective your data analysis will be.

Numerical Data Analysis allows us to make sense of any data that is currently available to us. Using Quantitative Data Analysis allows us to organize and summarize and prepare the data for dissemination to others. In addition to these common uses of Quantitative Data Analysis, it is also used to find patterns in data.

Quantitative Data Analysis will find differences as well as similarities and most other patterns in any data you analyze. This allows you to extrapolate and ask questions about the data to see new patterns you wouldn’t see without analyzing the data. Quantitative Data Analysis allows you to analyze your data in a highly objective manner, as quantitative research takes into account very few variables and huge amounts of data.

The analysis of research in any project involves summarizing the mass of data that has been collected and the presenting the results in a way that communicates the most important findings or features. The analysis of quantitative research involves the analysis of any of the following: Frequencies of variables, Differences between variables, Statistical tests designed to estimate the significance of the results and the probability that they did not occur by chance, and All of the above is achievable by counting and comparison. (Available at http://libweb.surrey.ac.uk/library/skills/Introduction%20to%20Research%20and%20Managing%20Information%20Leicester/page_76.htm Retrieved 2015-11-10 23:42:38

Quantitative data analysis is helpful in evaluation because it provides quantifiable and easy to understand results. Quantitative data can be analyzed in a variety of different ways. In this section, you will learn about the most common quantitative analysis procedures that are used in small program evaluation. You will also be provided with a list of helpful resources that will assist you in your own evaluation efforts.  

Quantitative Analysis in Evaluation. Before you begin your analysis, you must identify the level of measurement associated with the quantitative data. The level of measurement can influence the type of analysis you can use. There are four levels of measurement:  

·       Nominal

·       Ordinal

·       Interval

·       Ratio (scale)

Nominal data – data has no logical; data are basic classification data. Example: Male or Female. There is no order associated with male nor female. Each category is assigned an arbitrary value (male = 0, female = 1). Ordinal data – data has a logical order, but the differences between values are not constant: Example: T-shirt size (small, medium, large), Example: Military rank (from Private to General).

Interval data – data is continuous and has a logical order, data have standardized differences between values, but no natural zero. Example: Fahrenheit degrees. Remember that ratios are meaningless for interval data. You cannot say, for example, that one day is twice as hot as another day. Example: Items measured on a Likert scale – rank your satisfaction on a scale of 1-5.

1        = Very Dissatisfied

2        = Dissatisfied

3        = Neutral

4        = Satisfied

5        = Very satisfied

Ratio data – data is continuous, ordered, has standardized differences between values, and a natural zero.

·       Example: height, weight, age, length

·       Having an absolute zero enables you to meaningfully say that one measure is twice as long as another.

For example – 10 inches is twice as long as 5 inches. This ratio holds true regardless of which scale the object is being measured in (e.g. meters or yards).

Once you have identified your levels of measurement, you can begin using some of the quantitative data analysis procedures outlined below. Due to sample size restrictions, the types of quantitative methods at your disposal are limited. However, there are several procedures you can use to determine what narrative your data is telling. Below you will learn how about:  

·       Data tabulation (frequency distributions & percent distributions)

·       Descriptive data

·       Data disaggregation

·       Moderate and advanced analytical methods

(Available at http://toolkit.pellinstitute.org/evaluation-guide/analyze/analyze-quantitative-data/ Retrieved 2015-11-10 23:42:11)

Quantitative analysis: Statistically reliable and generalizable results. In quantitative research, we classify features, count them, and even construct more complex statistical models in an attempt to explain what is observed. Findings can be generalized to a larger population, and direct comparisons can be made between two corpora, so long as valid sampling and significance techniques have been used. Thus, quantitative analysis allows us to discover which phenomena are likely to be genuine reflections of the behavior of a language or variety, and which are merely chance occurrences. The most basic task of just looking at a single language variety allows one to get a precise picture of the frequency and rarity of particular phenomena, and thus their relative normality or abnormality.

However, the picture of the data which emerges from quantitative analysis is less rich than that obtained from qualitative analysis. For statistical purposes, classifications have to be of the hard-and-fast (so-called "Aristotelian" type). An item either belongs to class x or it doesn't. So in the above example about the phrase "the red flag" we would have to decide whether to classify "red" as "politics" or "colour". As can be seen, many linguistic terms and phenomena do not therefore belong to simple, single categories: rather they are more consistent with the recent notion of "fuzzy sets" as in the red example. Quantitative analysis is therefore an idealization of the data in some cases. Also, quantitative analysis tends to sideline rare occurrences. To ensure that certain statistical tests (such as chi-squared) provide reliable results, it is essential that minimum frequencies are obtained - meaning that categories may have to be collapsed into one another resulting in a loss of data richness. (Available at http://www.sal.tohoku.ac.jp/ling/corpus3/3qual.htm Retrieved 2015-11-10 23:43:14)

The analysis of qualitative research involves aiming to uncover and / or understand the big picture - by using the data to describe the phenomenon and what this means. Both qualitative and quantitative analysis involves labelling and coding all of the data in order that similarities and differences can be recognized. Responses from even an unstructured qualitative interview can be entered into a computer in order for it to be coded, counted and analyzed. The qualitative researcher, however, has no system for pre-coding, therefore a method of identifying and labelling or coding data needs to be developed that is bespoke for each research which is called content analysis. Content analysis can be used when qualitative data has been collected through: Interviews, Focus groups, Observation, and Documentary analysis.

Content analysis is '...a procedure for the categorization of verbal or behavioral data, for purposes of classification, summarization and tabulation.' The content can be analyzed at two levels: Basic level or the manifest level: a descriptive account of the data i.e. this is what was said, but no comments or theories as to why or how, higher level or latent level of analysis: a more interpretive analysis that is concerned with the response as well as what may have been inferred or implied.

Content analysis involves coding and classifying data, also referred to as categorizing and indexing and the aim of context analysis is to make sense of the data collected and to highlight the important messages, features or findings. (Available at http://libweb.surrey.ac.uk/library/skills/Introduction%20to%20Research%20and%20Managing%20Information%20Leicester/page_75.htm Retrieved 2015-11-10 23:42:42)

Qualitative data analysis involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the research questions at hand.

Qualitative analysis is Not GUIDED by universal rules, is a very fluid process that is highly dependent on the evaluator and the context of the study, likely to change and adapt as the study evolves and the data emerges. It is important to note that qualitative data analysis is an ongoing, fluid, and cyclical process that happens throughout the data collection stage of your evaluation project and carries over to the data entry and analysis stages. Although the steps listed below are somewhat sequential they do not always (and sometimes should not) happen in isolation of each other. (Available at http://toolkit.pellinstitute.org/evaluation-guide/analyze/analyze-qualitative-data/ Retrieved 2015-11-10 23:46:53)

Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected in some form of explanation, understanding or interpretation of the people and situations we are investigating. QDA is usually based on an interpretative philosophy. The idea is to examine the meaningful and symbolic content of qualitative data. For example, by analyzing interview data the researcher may be attempting to identify any or all of:

·       Someone's interpretation of the world,

·       Why they have that point of view,

·       How they came to that view,

·       What they have been doing,

·       How they conveyed their view of their situation,

·       How they identify or classify themselves and others in what they say,

The process of QDA usually involves two things, writing and the identification of themes. Writing of any kind is found in almost all forms of QDA. In contrast, some approaches, such as discourse analysis or conversation analysis may not require the identification of themes (see the discussion later on this page). Nevertheless, finding themes is part of the overwhelming majority of QDA carries out today.

Writing. Writing involves writing about the data and what you find there. In many cases, what you write may be analytic ideas. In other cases, it may be some form of précis or summary of the data, though this usually contains some analytic ideas.

Coding into themes. Looking for themes involves coding. This is the identification of passages of text (or other meaningful phenomena, such as parts of images) and applying labels to them that indicate they are examples of some thematic idea. At its simplest, this labelling or coding process enables researchers quickly to retrieve and collect together all the text and other data that they have associated with some thematic idea so that they can be examined together and different cases can be compared in that respect. Taylor and Gibbs (2010) Available at (http://onlineqda.hud.ac.uk/Intro_QDA/ Retrieved 2015-11-10 23:46:47)

Qualitative analysis: Richness and Precision. The aim of qualitative analysis is a complete, detailed description. No attempt is made to assign frequencies to the linguistic features which are identified in the data, and rare phenomena receives (or should receive) the same amount of attention as most frequent phenomena. Qualitative analysis allows for fine distinctions to be drawn because it is not necessary to shoehorn the data into a finite number of classifications. Ambiguities, which are inherent in human language, can be recognized in the analysis. For example, the word "red" could be used in a corpus to signify the color red, or as a political categorization (e.g. socialism or communism). In a qualitative analysis, both senses of red in the phrase "the red flag" could be recognized.

The main disadvantage of qualitative approaches to corpus analysis is that their findings cannot be extended to wider populations with the same degree of certainty that quantitative analyses can. This is because the findings of the research are not tested to discover whether they are statistically significant or due to chance. (Available at http://www.sal.tohoku.ac.jp/ling/corpus3/3qual.htm Retrieved 2015-11-10 23:43:14)

Data analysis in quantitative and qualitative research;

In Quantitative Research The data tend to be analyzed using statistical analysis. It is because the data in the form of numeric data. The data analysis commonly involves the description of trends, a comparison of group differences or the relationship among variables. The interpretation of the findings is done by comparing with the theoretical prediction or with the previous research.

In Qualitative Research text analysis is employed in analyzing the collected data. The data analysis commonly involves finding out categories and themes refined from the collected data. The interpretation process is commonly conducted through explaining the wider meaning of the findings

G.           Conclusion in quantitative and qualitative research

The most important parts of a research report are the descriptions, analyses, and interpretations of the data. What you do with the findings, i.e. the implications, are just as important.

The research needs to identify for the reader why and how the analyses and interpretations were made and the way key concepts in the analyses evolved. In addition, the researcher needs to inform the reader of any unexpected findings or patterns that emerged from the data and report a range of evidence to support assertions or interpretations presented.

Showing, not telling about your findings, is the best way to let your reader know what you discovered. Quotes, vignettes, field notes, work samples and other data can be used to support interpretations and assertions. The best way to show findings is to look for those critical incidents in your data, the "aha" or "oh no" moments, when you had a breakthrough in answering your research question. If it was a moment of vivid insight for you, it may well be a breakthrough for your audience.

A conclusion section refocuses the purpose of the research, revealing a synopsis of what was found and leads into the implications of the findings. A conclusion may also include limitations of the study and future research needs.

Implications for Practice. The meanings you construct from your data help give your ideas about how to teach in a particular way. The statements you make about how you might teach are the implications for future teaching. Is Teacher Research Valid and Reliable? That is a question that has been asked many times by both traditional educational researchers and teacher-researchers. Validity in research is the degree to which a study is honest and true to its intent, its context, and its reporting. It is the result of your integrity as a teacher and as a researcher.

Each school is different and the conditions are never the same from one class to the next. Teacher research derives its reliability from providing enough information to be able to make reasonable "comparisons" to other situations and contexts. Teacher researchers do not try to recreate the context of a study, but rather consider asking questions such as these:

·       How does the context affect the findings in the study?

·       What different variables are in the context?

·       If the multicultural mix of students was substituted for a more homogeneous one, how would that affect the findings?

Make revisions of your research questions to ensure a focus on your current teaching and what your students are learning. Frequent, consistent writing about our own observations will help you to discover what you think and to record what happens over a period of time. Collect a broad database of information to provide grounds for the interpretations that emerge from the data. Have other teacher-researchers examine and challenge your work. Read literature from theoretical and methodological frameworks to seek different theories and methods that challenge and deepen your own.

Quantitative research and qualitative research are two distinctly different ways to study and analyze research topics (Creswell, 2013). The quantitative is objective in that it is research and analysis that is based on statistical analysis against determined variables (Creswell, 2013). Qualitative is subjective in that it is allowed to be more reflexive and lends this technique to where the author can use his opinions and assumptions (Creswell, 2013).

Quantitative and qualitative research studies have utility based upon the problem and the purpose. Quantitative research is based on hard facts that are arguably objective. The research is proven with graphics, statistics, and charts that are driven by numbers. Many times the numbers are hard to dispute. Qualitative research is by necessity hard to quantify in a statistic. It is based on surveys, testimonials, and case studies that can often be disputed. Additionally, the author can include his bias, experience, and assumptions based upon personal experience. There is definitely a use for an exclusively quantitative research method and a qualitative research method. However, in many cases quantitative metrics and graphs can be reinforced with qualitative testimonials and case studies to effectively prove a recommendation. Quantitative methods would mostly appeal to hard sciences methodology, while qualitative methods would appeal to social science methodologies. The mixture of quantitative and qualitative methodologies would be most effective for managers and leaders. These papers could be improved upon by employing a mixture of quantitative and qualitative research.

Conclusion in quantitative and qualitative research;

In Quantitative Research, the research tends to result in the confirmation or justification, whether or not a certain hypothesis derived from a certain theory were approved. In other words, the results in form of hypothesis testing.

In Qualitative Research, the research results tend to reveal the detailed picture of a certain phenomenon happening in a particular context. From this revelation, there may be one or more hypothetical that will bear a new theory or revised the existing theory. It can be said that the result revealed a new understanding from a new perspective.

References

Admin. (2010, June 25). Section 5: Data Collection Methods [Text]. Retrieved November 10, 2015, from http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1d-qualitative-methods/section5-data-collection-methods

Auerbach, C. F., & Silverstein, L. B. (2013). Qualitative Data: An Introduction to Coding and analysis. New York: New York Unversity Press.

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data. British Dental Journal, 204(8), 429–432. http://doi.org/10.1038/sj.bdj.2008.292

Carter, D. F., & Hurtado, S. (2007). Bridging key research dilemmas: Quantitative research using a critical eye. New Directions for Institutional Research, 2007(133), 25–35. http://doi.org/10.1002/ir.202

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. Los Angeles: Sage.

Creswell, J. W. (2012). Research design. Qualitative, Quantitative, and Mixes Method. Retrieved from http://www.stiba-malang.com/uploadbank/pustaka/RM/RESEARCH%20DESIGN%20QUA%20QUAN.pdf

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. Retrieved from http://books.google.com/books?hl=en&lr=&id=EbogAQAAQBAJ&oi=fnd&pg=PR1&dq=%22may+both+test+theories+and+generate+them.+Moreover,%22+%22moves+to+the+use+of+theory+in+a+qualitative+study.%22+%22and+the+types+that+are+used+in+forming+theories.+A+variable%22+&ots=caeRqRPAz6&sig=7uzg_3D_vgPyhZVG2Sq93VhqvwE

Creswell, J. W., & Clark, V. L. P. (2004). Principles of qualitative research: Designing a qualitative study. Office of Qualitative & Mixed Methods Research, University of Nebraska, Lincoln. Retrieved at http://www. andrews. edu/leaderpart/roundtable/2004/workshops/2b/au-qual-071504-jwc-vpc. pdf. Retrieved from http://www.cc.andrews.edu/leaderpart/RoundTable/2004/workshops/2b/AU-Qual-071504-jwc-vpc.pdf

Davie, G. (n.d.-a). Writing Good Qualitative Research Questions. Retrieved from http://masscommtheory.com/2011/05/05/writing-good-qualitative-research-questions/

Davie, G. (n.d.-b). Writing Good Quantitative Research Questions. Retrieved from http://masscommtheory.com/2012/10/19/writing-good-quantitative-research-questions/

Guest, G., Namey, E. E., & Mitchell, M. L. (2012). Collecting qualitative data: A field manual for applied research. Sage.

Joseph, L. (n.d.). Quantitative Data Analysis: A Versatile and Beneficial Process. Retrieved November 10, 2015, from https://blog.udemy.com/quantitative-data-analysis/

Kelly, M. (2010). The role of theory in qualitative health research. Family Practice, 27(3), 285–290. http://doi.org/10.1093/fampra/cmp077

RAP, R. A. P. (2000). Interviewer’s Guide. Retrieved from http://173.0.142.150/resources/general_fieldtools/toolkit/CDC-CERTI%20RAP/rap_annexes.pdf

Taylor, C and Gibbs, G R. (2010). "What is Qualitative Data Analysis (QDA)?", Online QDA Web Site, [onlineqda.hud.ac.uk/Intro_QDA/what_is_qda.php].