Quantitative methods represent the steps of using the Scientific Method of research. There are following types of quantitative method…

1. Experimental Research is the most well-established quantitative methodology in both the physical and social sciences. This approach uses the principles of research in the physical sciences to conduct experiments that explore human behavior.

2. Survey Research is used to ask people a number of questions about particular topics. Surveys can be online, mailed, handed out, or conducted in interview format. After researchers have collected survey data, they represent participants’ responses in numerical form using tables, graphs, charts, and/or percentages.

3. Content Analysis. Researchers use content analysis to count the number of occurrences of their particular focus of inquiry. Communication researchers often conduct content analyses of movies, commercials, television shows, magazines, etc., to count the number of occurrences of particular phenomena in these contexts to explore potential effects.

4. Meta-Analysis. Do you ever get frustrated when you hear about one research project that says a particular food is good for your health, and then some time later, you hear about another research project that says the opposite? Meta-analysis analyzes existing statistics found in a collection of quantitative research to demonstrate patterns in a particular line of research over time.

Questionnaire survey for quantitative research

Introduction to Statistical Data Analysis

Experimental Research: Types, Examples & Methods

Meta-analysis in management research

Content Analysis Method and Examples

The post Types of quantitative methods appeared first on HKT Consultant.

Think of a survey as a major project, aimed at using data to reach informed conclusions. Yes, that’s right – a survey could be regarded as a questionnaire on steroids.

With a survey, you can dig deeper and find out peoples’ opinions and ideas. You can ask demographic survey questions, you can find out how engaged your employees are, or you can do market research. And much more.

Main contentsSee more from basic to advanced

The objectives of the study relating to the questionnaire

1. Relating research objectives to business objectives The brief that the researcher receives may sometimes include the business objectives for the study and the research

Stakeholders in the questionnaire

Clearly there are a number of different stakeholders in the questionnaire, on each of whom the way in which it is written and laid out

The questionnaire in the survey process

The questionnaire represents one part of the survey process. It is, however, a very vital part of the process. A poorly written questionnaire will not

Interviewer-administered interviews

The key benefits of having an interviewer administer the questionnaire are: Queries about the meaning of a question can be dealt with. A misunderstood question

Collecting unbiased and accurate data by questionnaire

Clearly, the data collected should be as accurate as possible. However, complete accuracy is almost impossible to obtain in surveys where respondents are asked to

Recruitment questionnaires

Recruitment questionnaires are used in qualitative research and for recruit­ment of respondents for some types of quantitative research (eg clinics held in central locations). The

Screening questions in the questionnaire

Following the exclusion question, the next part of the questionnaire will be to screen the respondents for eligibility for the survey, depending on whether or

Exclusion question in the questionnaire

A common, although not universal, practice is to exclude respondents from research surveys who work in market research, marketing or the client’s industry. This will

Sequencing the sections of the questionnaire

The questionnaire can be properly planned once the principal and analy­sis information requirements have been decided. It is most commonly divided into three sections: exclusion

Defining the information required in the questionnaire

It should be clear from the research objectives and the business objectives what information areas the questionnaire needs to cover. This is the prin­cipal information

Introduction

A questionnaire that is going to provide accurate, good-quality informa­tion needs to be thought about and planned, before a single question is written. The sequence

Self-completion surveys

Self-completion methods, whether paper based or electronic, can benefit from the complete absence of an interviewer from the process. This removes a major source of

Open-ended questions in the questionnaire

An open-ended question is an open question where the response is recorded verbatim. An open-ended question is nearly always also an open question. (It would

Prompted questions in the questionnaire

Spontaneous responses rarely tell the researcher the complete picture regarding what the respondent knows or feels, but only what is front-of- mind. However, most people

Spontaneous questions in the questionnaire

A spontaneous question is any question for which the respondent is not given a repertoire of possible answers from which to choose. All open- ended

Open and closed questions in the questionnaire

An open question is one where the range of possible answers is not suggested in the question and which respondents are expected to answer in

Question types in the questionnaire

Any question in an interview can be classified in a number of different ways: open or closed, depending whether or not the answer can come

Main questionnaire

The main questionnaire can now be planned. Once into the main questionnaire, the writer must consider the order in which the various topics are presented

Comparative scaling techniques of questions in the questionnaire

1. Paired comparisons With paired comparisons, respondents are asked to choose between two objects based on the appropriate criterion, eg that one is more important

Attitudinal rating scales of questions in the questionnaire

A number of forms of rating scale have been developed specifically to address responses to a series of attitudinal dimensions. The three most commonly used

Itemized rating scales of questions in the questionnaire

Itemized rating scales are used to help the researcher obtain a measure of attitudes. The researcher first develops a number of dimensions – attitude statements,

Attitude measurement in the questionnaire

The measurement of attitude poses more problems than does the measurement of behaviour. Respondents are able to respond relatively easily to behavioural questions, limited only

Data types in the questionnaire

Responses are measured using four types of data: nominal; ordinal; interval; ratio. These are frequently described as ‘measurement scales’, though most researchers would not necessarily

Pre-coded questions in the questionnaire

1. Pre-coded open questions Frequently with interviewer-administered surveys, a list of pre-codes is provided with open questions for the interviewer’s use. This may simply be

Determining the pre-codes of questions in the questionnaire

The pre-codes that are used on the questionnaire determine what data are collected. If the pre-codes have insufficient accuracy or are incomplete, then data will

Avoiding ambiguity in the question

Ambiguity is to be avoided at all costs. If a question is ambiguous, then the respondent may be presented with the dilemma of hearing or

Use of language in the questionnaire

When writing the questionnaire it is the questionnaire writer’s job to ensure that the respondents will understand the questions and that the respondents will not

The measure dimensions of questions in the questionnaire

1. Determining the attributes to measure No matter which scale is used the crucial factor to get right is the wording of the items against

Measuring brand image by the questionnaire survey

A frequent objective with brand and communication studies is to measure brand image: that is, the perceptions that people hold of the main brands, how

Rating scales of questions in customer satisfaction research

Deciding which scale to use Using rating scales in customer satisfaction research presents the ques­tionnaire writer with a number of choices for the most appropriate

Questionnaire in Omnibus studies

An omnibus survey is a particular type of study on which clients buy space for their own questions. The questionnaire can therefore cover a number

Questionnaire in Tracking studies

Consistency of question wording is important in ongoing or tracking studies, in order to ensure that changes in data over time are not due to

Standardizing questions in the questionnaire

Where a question has been asked in a previous study it is usually to the advantage of the researcher to ensure that, unless there is

Question order in the questionnaire

There are certain rules regarding the ordering of questions that must always be borne in mind. These have been covered in Chapter 3 and include:

Order bias and prompts in the questionnaire

The order in which prompts are presented to respondents, whether on the questionnaire or screen, shown on a card or read out, can have a

Using prompts in the questionnaire

Show cards are frequently used to provide the respondents with prompted answers in face-to-face interviews. In self-completion inter-views the prompts are provided with the question,

Why pilot questionnaires?

There are two key tests for a questionnaire: reliability and validity. A questionnaire is reliable if it provides a consistent distribution of responses from the

Enhancing the experience on the Online questionnaires

Some of the advances offered by web-based questionnaires are the enhance­ments that can be made to the ways in which the questions are asked, ma­terial

Replicating existing approaches to the Online questionnaires

As with CAPI and CATI, electronic formatting of the questionnaire brings opportunities to replicate and improve on pen and paper approaches. Thus it is possible

CAPI and CATI as forms of electronic questionnaires

CAPI, CATI and all forms of electronic questionnaires have a number of advantages over paper questionnaires which have already been touched on in Chapter 2.

Self-completion paper questionnaire

Much of the success of a paper-based self-completion survey depends on the appearance of the questionnaire and the ease with which respondents can use it.

Interviewer-administered paper questionnaires

If a paper questionnaire is being used, the primary concern with regard to layout is that the interviewer can follow the questionnaire sequence easily, asking

Home

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Why do people mix up the two?

Computer questionnaire. Respondents are asked to answer the questionnaire which is sent by mail. The advantages of the computer questionnaires include their inexpensive price, time-efficiency, and respondents do not feel pressured, therefore can answer when they have time, giving more accurate answers. However, the main shortcoming of the mail questionnaires is that sometimes respondents do not bother answering them and they can just ignore the questionnaire.

Telephone questionnaire. Researcher may choose to call potential respondents with the aim of getting them to answer the questionnaire. The advantage of the telephone questionnaire is that, it can be completed during the short amount of time. The main disadvantage of the phone questionnaire is that it is expensive most of the time. Moreover, most people do not feel comfortable to answer many questions asked through the phone and it is difficult to get sample group to answer questionnaire over the phone.

In-house survey. This type of questionnaire involves the researcher visiting respondents in their houses or workplaces. The advantage of in-house survey is that more focus towards the questions can be gained from respondents. However, in-house surveys also have a range of disadvantages which include being time consuming, more expensive and respondents may not wish to have the researcher in their houses or workplaces for various reasons.

Mail Questionnaire. This sort of questionnaires involve the researcher to send the questionnaire list to respondents through post, often attaching pre-paid envelope. Mail questionnaires have an advantage of providing more accurate answer, because respondents can answer the questionnaire in their spare time. The disadvantages associated with mail questionnaires include them being expensive, time consuming and sometimes they end up in the bin put by respondents.

Questionnaires can include the following types of questions:

Open question questionnaires. Open questions differ from other types of questions used in questionnaires in a way that open questions may produce unexpected results, which can make the research more original and valuable. However, it is difficult to analyze the results of the findings when the data is obtained through the questionnaire with open questions.

Multiple choice questions. Respondents are offered a set of answers they have to choose from. The downsize of questionnaire with multiple choice questions is that, if there are too many answers to choose from, it makes the questionnaire, confusing and boring, and discourages the respondent to answer the questionnaire.

Dichotomous Questions. This type of questions gives two options to respondents – yes or no, to choose from. It is the easiest form of questionnaire for the respondent in terms of responding it.

Scaling Questions. Also referred to as ranking questions, they present an option for respondents to rank the available answers to the questions on the scale of given range of values (for example from 1 to 10).

Survey Monkey represents one of the most popular online platforms for facilitating data collection through questionnaires. Substantial benefits offered by Survey Monkey include its ease to use, presentation of questions in many different formats and advanced data analysis capabilities.

The post Questionnaire survey for quantitative research appeared first on HKT Consultant.

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Computerized Content Analysis

The increasing availability of written information on computer files, and the increasing number of computer programs to analyze text files, makes content analysis easier to

The Uses of Content Analysis

Here are several ways in which GAO evaluators have successfully used content analysis techniques. In Stars and Stripes: Inherent Conflicts Lead to Allegations of Military

A Definition of Content Analysis

In content analysis, evaluators classify the key ideas in a written communication, such as a report, article, or film. Evaluators can do content analysis of

How to Apply Content Analysis

GAO evaluators can use content analysis to articulate a program’s objectives, describe its activities, and determine its results. 1. Program Objectives Many evaluations characterize a

Some Disadvantages of Content Analysis

Because content analysis is systematic, sufficient human resources must be committed to it and rigorously applied to it. This may mean, for some evaluation applications,

Some Advantages of Content Analysis

1. It Can Be Unobtrusive One problem with surveys and some experimental methods is that evaluators and their informants can interact during data collection in

Defining the Variables in Content Analysis

The assignment’s evaluation questions lead directly to the relevant variables. In the Stars and Stripes example, we asked “To what extent does the content of

Resources Needed in Content Analysis

In content analysis, evaluators must consider three principal types of resources: an analyst with the technical knowledge and experience to plan and direct the content

Kinds of Analysis Required in Content Analysis

Considering data requirements goes hand in hand with analysis requirements. In many evaluations, the most important, or only, form of analysis may be a simple

Kinds of Data Required for Content Analysis

In the early stages of an assignment, evaluators choose variables of interest. For the descriptive Stars and Stripes assignment, for example, important variables included the

Data Available or to Be Collected for Content Analysis

Whether or not content analysis is appropriate depends on the nature of the information to be evaluated. The information can be anything written: an original

Assignment Objectives of Content Analysis

GAO often expresses an assignment’s objectives in the form of three broad categories of evaluation question: descriptive, normative, or impact questions. (GAO, 1991c) In theory,

Coder Selection and Training in Content Analysis

Coding is generally quicker and more accurate and credible the more expertise coders have in the subject of the material being analyzed. For example, in

Coding Options in Content Analysis

Textual material can be coded directly on the computer or it can be coded manually and transferred clerically to electronic media. With the latter option,

Creating Codes in Content Analysis

Codes are simply abbreviations, or tags, for segments of text. Before evaluators can code a document, they must first create a code for each variable’s

Developing an Analysis Plan in Content Analysis

Developing a plan for an analysis is the final planning step. It finks the data back to the evaluation question. Traditionally, most content analyses have

Defining the Recording Unit for Content Analysis

Once evaluators have defined the variables and selected the textual material, their next major task is to define the recording units. A recording unit is

Selecting Material for Analysis in Content Analysis

To select textual material to include in the content analysis, evaluators may find it easiest to think first about a population of documents. For some

Estimating Reliability of Content Analysis

When several coders code the documents, then- consistency is important. If the coders differ substantially, then the results of the content analysis become questionable. Chapter

Preparing for Data Analysis in Content Analysis

The basic analytic task in content analysis is to count the occurrence of codes, whether all occurrences of a given category (for example, all occurrences

Using a Computer to Code in Content Analysis

This section assumes that the documents to be coded are available in a word processing format such as WordPerfect and that coding proceeds with the

Applying Codes in Content Analysis

In manual coding on hard-copy documents, the coder simply marks the boundaries of the recording unit and writes the code in the margin of the

Selecting and Managing Documents in Content Analysis

1. Using All the Documents Even though the population of documents may seem conceptually clear, assembling them for coding generally has three problems: missing documents,

The Potential for Coding Error in Content Analysis

The four interrelated potential sources of coding inaccuracy in most applications of content analysis are (1) deficiencies in the documents, (2) ambiguity in the judgment

Analyzing and Reporting the Data in Content Analysis

1. Cross-Check Preliminary Results Things are not always what they seem. Try to verify findings by using related variables or slightly different analysis methods. This

Coding in Content Analysis

1. Produce a Coding Manual A good coding manual is indispensable. AvoSrf the temptation to save time by not producing one or by producing only

Planning the Content Analysis

1. Be Clear About the Questions The evaluation questions drive the study. If they are Questions ambiguous or not suited to the users’ needs, even a

Reporting the Methodology and Results of Content Analysis

The methodology and results of a content analysis should be reported the way they are for other evaluations. The methodology should be described in sufficient

Finding Associations in Content Analysis

Beyond simply counting, evaluators might look for an association between two or more variables. In the Stars and Stripes assignment, the frequency of news articles

Counting a Code’s Frequency in Content Analysis

Drawing inferences from the frequency of codes is the simplest and often the most useful form of data analysis. Drawing conclusions in the Stars and

Intercoder Reliability in Content Analysis

An important measure forjudging the quality of a content analysis is the extent to which the results can be reproduced. Known as intercoder reliability, this

Software for Content Analysis

This appendix describes computer software that may be useful to content analysis. The list of programs here is by no means complete, and it is

Analysis of Qualitative Data in Content Analysis

Content analysis applies to textual information in the form of words. An analyst can classify text into categories as described in chapter 1. The categories

Quantitative content analysis highlights frequency counts and objective analysis of these coded frequencies. Additionally, quantitative content analysis begins with a framed hypothesis with coding decided on before the analysis begins. These coding categories are strictly relevant to the researcher’s hypothesis. Quantitative analysis also takes a deductive approach.

Siegfried Kracauer provides a critique of quantitative analysis, asserting that it oversimplifies complex communications in order to be more reliable. On the other hand, qualitative analysis deals with the intricacies of latent interpretations, whereas quantitative has a focus on manifest meanings. He also acknowledges an “overlap” of qualitative and quantitative content analysis. Patterns are looked at more closely in qualitative analysis, and based on the latent meanings that the researcher may find, the course of the research could be changed. It is inductive and begins with open research questions, as opposed to a hypothesis.

Three different definition of content analysis are provided below.

• Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)
• Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).
• Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

• Identify the intentions, focus or communication trends of an individual, group or institution
• Describe attitudinal and behavioral responses to communications
• Determine psychological or emotional state of persons or groups
• Reveal international differences in communication content
• Reveal patterns in communication content
• Pre-test and improve an intervention or survey prior to launch
• Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

• Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.
• Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

• When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.
• When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

• Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.
• What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize error far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

1. Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.
2. Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.
3. Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes.
2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words.
3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

• Strength of relationship: degree to which two or more concepts are related.
• Sign of relationship: are concepts positively or negatively related to each other?
• Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded.
5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding.
6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability: Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

1. Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.
2. Reproducibility: tendency for a group of coders to classify categories membership in the same way.
3. Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity: Three criteria comprise the validity of a content analysis:

1. Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.
2. Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.
3. Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

• Directly examines communication using text
• Allows for both qualitative and quantitative analysis
• Provides valuable historical and cultural insights over time
• Allows a closeness to data
• Coded form of the text can be statistically analyzed
• Unobtrusive means of analyzing interactions
• Provides insight into complex models of human thought and language use
• When done well, is considered a relatively “exact” research method
• Content analysis is a readily-understood and an inexpensive research method
• A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

• Can be extremely time consuming
• Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation
• Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study
• Is inherently reductive, particularly when dealing with complex texts
• Tends too often to simply consist of word counts
• Often disregards the context that produced the text, as well as the state of things after the text is produced

• Can be difficult to automate or computerize

The post Content Analysis Method and Examples appeared first on HKT Consultant.

Statistics is basically a science that involves data collection, data interpretation and finally, data validation. Statistical data analysis is a procedure of performing various statistical operations. It is a kind of quantitative research, which seeks to quantify the data, and typically, applies some form of statistical analysis. Quantitative data basically involves descriptive data, such as survey data and observational data.

Statistical data analysis generally involves some form of statistical tools, which a layman cannot perform without having any statistical knowledge. There are various software packages to perform statistical data analysis. This software includes Statistical Package for the Social Sciences (SPSS), Stata soft, etc.

Data Analysis Using STATA: Use and Interpretation

Data Analysis Using IBM SPSS: Use and Interpretation

Introduction to IBM AMOS SPSS

Introduction to SmartPLS software

Introduction to Eviews

Data in statistical data analysis consists of variable(s). Sometimes the data is univariate or multivariate. Depending upon the number of variables, the researcher performs different statistical techniques.

Main contentsSee more from basic to advanced

Statistical Inference

Many situations require information about a large group of elements (individuals, compa­nies, voters, households, products, customers, and so on). But, because of time, cost, and

Descriptive Statistics

Most of the statistical information in the media, company reports, and other publications consists of data that are summarized and presented in a form that

Data Sources

Data can be obtained from existing sources, by conducting an observational study, or by conducting an experiment. 1. Existing Sources In some cases, data needed

Summarizing Data for a Quantitative Variable

As defined in Section 2.1, a frequency distribution is a tabular summary of data showing the number (frequency) of observations in each of several nonoverlapping

Summarizing Data for a Categorical Variable

1. Frequency Distribution We begin the discussion of how tabular and graphical displays can be used to summarize categorical data with the definition of a

Ethical Guidelines for Statistical Practice

Ethical behavior is something we should strive for in all that we do. Ethical issues arise in statistics because of the important role statistics plays

Computers and Statistical Analysis

Statisticians use computer software to perform statistical computations and analyses. For example, computing the average time until recharge for the 200 batteries in the Rogers

Big Data and Data Mining

With the aid of magnetic card readers, bar code scanners, and point-of-sale terminals, most organizations obtain large amounts of data on a daily basis. And,

Data Analytics

Because of the dramatic increase in available data, more cost-effective data storage, faster computer processing, and recognition by managers that data can be extremely valuable

Measures of Distribution Shape, Relative Location, and Detecting Outliers

We have described several measures of location and variability for data. In addition, it is often important to have a measure of the shape of

Measures of Variability

In addition to measures of location, it is often desirable to consider measures of variability, or dispersion. For example, suppose that you are a purchasing

Measures of Location

1. Mean Perhaps the most important measure of location is the mean, or average value, for a variable. The mean provides a measure of central

Data Visualization: Best Practices in Creating Effective Graphical Displays

Data visualization is a term used to describe the use of graphical displays to summarize and present information about a data set. The goal of

Summarizing Data for Two Variables Using Graphical Displays

In the previous section we showed how a crosstabulation can be used to summarize the data for two variables and help reveal the relationship between

Summarizing Data for Two Variables Using Tables

Thus far in this chapter, we have focused on using tabular and graphical displays to summarize the data for a single categorical or quantitative variable.

Some Basic Relationships of Probability

Given an event A, the complement of A is defined to be the event consisting of all sample points that are not in A. The

Events and Their Probabilities

In the introduction to this chapter we used the term event much as it would be used in everyday language. Then, in Section 4.1 we

Random Experiments, Counting Rules, and Assigning Probabilities

In discussing probability, we deal with experiments that have the following characteristics: The experimental outcomes are well defined, and in many cases can even be

Data Dashboards: Adding Numerical Measures to Improve Effectiveness

In Section 2.5 we provided an introduction to data visualization, a term used to describe the use of graphical displays to summarize and present information

Measures of Association Between Two Variables

Thus far we have examined numerical methods used to summarize the data for one vari­able at a time. Often a manager or decision maker is

Five-Number Summaries and Boxplots

Summary statistics and easy-to-draw graphs based on summary statistics can be used to quickly summarize large quantities of data. In this section we show how

Hypergeometric Probability Distribution

The hypergeometric probability distribution is closely related to the binomial distribution. The two probability distributions differ in two key ways. With the hypergeometric distribu­tion, the

Poisson Probability Distribution

In this section we consider a discrete random variable that is often useful in estimating the number of occurrences over a specified interval of time

Binomial Probability Distribution

The binomial probability distribution is a discrete probability distribution that has many applications. It is associated with a multiple-step experiment that we call the binomial

Bivariate Distributions, Covariance, and Financial Portfolios

A probability distribution involving two random variables is called a bivariate probability distribution. In discussing bivariate probability distributions, it is useful to think of a

Expected Value and Variance

The expected value, or mean, of a random variable is a measure of the central location for the random variable. The formula for the expected

Developing Discrete Probability Distributions

The probability distribution for a random variable describes how probabilities are distributed over the values of the random variable. For a discrete random variable x,

Random Variables

A random variable provides a means for describing experimental outcomes using numer- and its associated experimen- ical values. Random variables must assume numerical values. In

Bayes’ Theorem

In the discussion of conditional probability, we indicated that revising probabilities when new information is obtained is an important phase of probability analysis. Often, we

Conditional Probability

Often, the probability of an event is influenced by whether a related event already occurred. Suppose we have an event A with probability P(A). If

Sampling Distribution of x

In the previous section we said that the sample mean X is a random variable and its probability distribution is called the sampling distribution of

Introduction to Sampling Distributions

In the preceding section we said that the sample mean X is the point estimator of the population mean m, and the sample proportion p

Point Estimation

Now that we have described how to select a simple random sample, let us return to the EAI problem. A simple random sample of 30

Selecting a Sample

In this section we describe how to select a sample. We first describe how to sample from a finite population and then describe how to

The Electronics Associates Sampling Problem

The director of personnel for Electronics Associates, Inc. (EAI), has been assigned the task of developing a profile of the company’s 2500 managers. The characteristics

Exponential Probability Distribution

The exponential probability distribution may be used for random variables such as the time between arrivals at a hospital emergency room, the time required to

Normal Approximation of Binomial Probabilities

In Section 5.5 we presented the discrete binomial distribution. Recall that a binomial experi­ment consists of a sequence of n identical independent trials with each

Normal Probability Distribution

The most commonly used probability distribution for describing a continuous random variable is the normal probability distribution. The normal distribution has been used in a

Uniform Probability Distribution

Consider the random variable x representing the flight time of an airplane traveling from Chicago to New York. Suppose the flight time can be any

Big Data and Confidence Intervals

We have seen that confidence intervals are powerful tools for making inferences about pop­ulation parameters. We now consider the ramifications of big data on confidences

Population Proportion

In the introduction to this chapter we said that the general form of an interval estimate of a population proportion p is The sampling distribution

Determining the Sample Size

In providing practical advice in the two preceding sections, we commented on the role of the sample size in providing good approximate confidence intervals when

Population Mean: s Unknown

When developing an interval estimate of a population mean we usually do not have a good estimate of the population standard deviation either. In these

Population Mean: σ Known

In order to develop an interval estimate of a population mean, either the population stand­ard deviation s or the sample standard deviation 5 must be

Big Data and Standard Errors of Sampling Distributions

The purpose of statistical inference is to use sample data to quickly and inexpensively gain insight into some characteristic of a population. Therefore, it is

Other Sampling Methods

We described simple random sampling as a procedure for sampling from a finite population and discussed the properties of the sampling distributions of X and

Properties of Point Estimators

In this chapter we showed how sample statistics such as a sample mean X, a sample standard deviation 5, and a sample proportion p can

Sampling Distribution of p

The sample proportion p is the point estimator of the population proportion p. The formula for computing the sample proportion is where x = the

Big Data and Hypothesis Testing

We have seen that interval estimates of the population mean m and the population propor­tion p narrow as the sample size increases. This occurs because

Determining the Sample Size for a Hypothesis Test About a Population Mean

Assume that a hypothesis test is to be conducted about the value of a population mean. The level of significance specified by the user determines

Calculating the Probability of Type II Errors

In this section we show how to calculate the probability of making a Type II error for a hypothesis test about a population mean. We

Hypothesis Testing and Decision Making

In the previous sections of this chapter we have illustrated hypothesis testing applications that are considered significance tests. After formulating the null and alternative hypotheses,

Population Proportion

In this section we show how to conduct a hypothesis test about a population proportion p. Using p0 to denote the hypothesized value for the

Population Mean: s Unknown

In this section we describe how to conduct hypothesis tests about a population mean for the σ unknown case. Because the σ unknown case corresponds

Population Mean: σ Known

In Chapter 8 we said that the σ known case corresponds to applications in which historical data and/or other information are available that enable us

Type I and Type II Errors

The null and alternative hypotheses are competing statements about the population. Either the null hypothesis H0 is true or the alternative hypothesis Ha is true,

Developing Null and Alternative Hypotheses

It is not always obvious how the null and alternative hypotheses should be formulated. Care must be taken to structure the hypotheses appropriately so that

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If the data in statistical data analysis is multiple in numbers, then several multivariates can be performed. These are factor statistical data analysis, discriminant statistical data analysis, etc. Similarly, if the data is singular in number, then the univariate statistical data analysis is performed. This includes t test for significance, z test, f test, ANOVA one way, etc.

The data in statistical data analysis is basically of 2 types, namely, continuous data and discreet data. The continuous data is the one that cannot be counted. For example, intensity of a light can be measured but cannot be counted. The discreet data is the one that can be counted. For example, the number of bulbs can be counted.

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“Meta-analysis is the statistical combination of results from two or more separate studies” (Deeks et al, 2019, chapter 10). When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect. When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation.

Decisions about the utility of an intervention or the validity of a hypothesis cannot be based on the results of a single study, because results typically vary from one study to the next. Rather, a mechanism is needed to synthesize data across studies. Narrative reviews had been used for this purpose, but the narrative review is largely subjective (different experts can come to different conclusions) and becomes impossibly difficult when there are more than a few studies involved. Meta-analysis, by contrast, applies objective formulas (much as one would apply statistics to data within a single study), and can be used with any number of studies.

Pharmaceutical companies use meta-analysis to gain approval for new drugs, with regulatory agencies sometimes requiring a meta-analysis as part of the approval process. Clinicians and applied researchers in medicine, education, psychology, criminal justice, and a host of other fields use meta-analysis to determine which interventions work, and which ones work best. Meta analysis is also widely used in basic research to evaluate the evidence in areas as diverse as sociology, social psychology, sex differences, finance and economics, political science, marketing, ecology and genetics, among others.

A Brief History of Meta-Analysis

In this section, I briefly outline the history of meta-analysis. My goal is not to be exhaustive in detailing this history (for more extensive treatments,

Basic Terminology in Meta-Analysis

Before further discussing meta-analysis, it is useful to clarify some relevant terminology. One clarification involves the distinction of meta-analysis from primary or secondary analysis. The

The Need for Research Synthesis in the Social Sciences

Isaac Newton is known to have humbly explained his success: “If I have seen further it is by standing upon the shoulders of giants” (1675;

The Limits of Primary Research and the Limits of Meta-Analytic Synthesis

Perhaps no statement is more true, and humbling, than this offered as the opening of Harris Cooper’s editorial in Psychological Bulletin (and likely stated in

Identifying Goals and Research Questions for Meta-Analysis

In providing a taxonomy of literature reviews (see Chapter 1), Cooper (1988, 2009a) identified the goals of a review to be one of the dimensions

The Scientific Process of Research Synthesis

Given the importance of research syntheses, including meta-analyses, to the progression of science, it is critical to follow scientific standards in their preparation. Most scientists

Reality Checking: Is My Meta-Analysis Search Adequate?

Regardless of what methods of searching the literature you rely upon, the most important question is whether your search is adequate. You can think of

Finding Relevant Literature for Meta-Analysis

After specifying inclusion/exclusion criteria, the next step is to begin search­ing for empirical studies that fit within this sampling frame. In searching for this relevant

Inclusion and Exclusion Criteria for Meta-Analysis

The inclusion criteria, and conversely the exclusion criteria, are a set of explicit statements about the features of studies that will or will not (respectively)

Developing and Articulating a Sampling Frame for Meta-Analysis

Given that meta-analysis uses the individual study as its unit of analysis, it is useful to think of your meta-analysis as consisting of a sample

2.4 Practical Matters: The Reciprocal Relation between Planning and Conducting a Meta-Analysis

My placement of this chapter on identifying research questions for meta­analysis before chapters on actually performing a meta-analysis is meant to correspond to the order

Critiques of Meta-Analysis: When Are They Valid and When Are They Not?

As I outlined in Chapter 1, attention to meta-analysis emerged in large part with the attention received by Smith and Glass’s (1977) meta-analysis of psy­chotherapy

The Common Metrics in Meta-Analysis: Correlation, Standardized Mean Difference, and Odds Ratio

1. Significance Tests Are Not Effect Sizes Before describing what effect sizes are, I describe what they are not. Effect sizes are not significance tests,

Practical Matters: Creating an Organized Protocol for Coding in Meta-Analysis

Once you have decided what study characteristics to code, the next step is to plan to code them (likely coding effect sizes at the same

Evaluating Coding Decisions in Meta-Analysis

Once you have decided what study characteristics to code, the next step, of course, is to do it—to carefully read obtained reports and to record

Coding Study “Quality” in Meta-Analysis

Some have recommended that meta-analysts code for study quality and then either (1) include only studies meeting a certain level of quality or (2) evalu­ate

Identifying Interesting Moderators in Meta-Analysis

Decisions about which study characteristics to code need to be heavily informed by your knowledge of the content area in which you are performing a

Practical Matters: Beginning a Meta-Analytic Database

Aside from perhaps persistence and patience, the most import virtue you can have for searching the literature for a meta-analysis is organization. As you have

The Controversy of Correction to effect Sizes in Meta-Analysis

There is some controversy about correcting effect sizes used in meta-analyses for methodological artifacts. In this section I describe arguments for and against correction, and

Practical Matters: Using Effect Size Calculators and Meta-Analysis Programs

As I described in Chapter 1, several computer programs are designed to aid in meta-analysis, some of which are available for free and others for

Comparisons among r, g, and o

I have emphasized the importance of basing the decision to rely on r, g, or o on conceptualizations of the association involving two continuous vari­ables,

Computing o from Commonly Reported Results

When you are interested in computing the odds ratio (o, sometimes denoted by OR), or the association between two dichotomous variables, the range of typically

Computing g from Commonly Reported Results

As when computing r, you can compute standardized mean differences from a wide range of commonly reported information. Although I have presented three different types

Computing r from Commonly Reported Results

You can compute r from a wide range of results reported in primary studies. In this section, I describe how you can compute this effect

Miscellaneous Effect Sizes in Meta-Analysis

As I hope is becoming increasingly clear, you can include a wide range of options for effect sizes in your meta-analyses. Although this section on

Regression Coefficients and Similar Multivariate Effect Sizes in Meta-Analysis

1. Regression Coefficients In many areas of study, researchers are interested in associations of one variable (X), with another variable (Y) controlling for other variables

When the Metric Is Meaningful: Raw Difference Scores

Paralleling the situation when you might want to meta-analyze means and standard deviations—that is, when included studies share a common (or comparable) scale for variable

Describing Single Variables in Meta-Analysis

There are relatively few instances of meta-analyzing single variables, yet this information could be potentially valuable. At least three types of information regarding single variables

Practical Matters: When (and How) to Correct: Conceptual, Methodological, and Disciplinary Considerations in Meta-Analysis

1. General considerations As I described earlier, one consideration in deciding whether to correct for artifacts is the expected magnitude of effects these artifacts have

Artifact Corrections to Consider in Meta-Analysis

Hunter and Schmidt (2004; see also Schmidt, Le, & Oh, 2009) suggest several corrections to methodological artifacts of primary studies. These corrections involve unreliability of

Practical Matters: Nonindependence among Effect Sizes

An important qualifier to the analyses I have described in this chapter (and those I will describe in subsequent chapters) is that they should be

Evaluating Heterogeneity among Effect Sizes

In Figure 8.1, all of the studies had confidence intervals that contained the vertical line representing the overall population effect size. This situation is called

Inferential Testing and Confidence Intervals of Average Effect Sizes

The key to making inferences regarding statistical significance about, or computing confidence intervals around, this (weighted) mean effect size is to compute a standard error

Measures of Central Tendency in Effect Sizes

1. Choices of Indices of Central Tendency Turning momentarily away from the topic of weighting, I now consider the ways in which you can represent

The Logic of Weighting

Although the democratic process of giving equal weight to each study has some appeal, the reality is that some studies provide better effect size esti­mates

Practical Matters: The Opportunities and Challenges of Meta-Analyzing Unique Effect Sizes

1. The Challenges of Meta-Analyzing unique Effect Sizes Meta-analyzing unique effect sizes carries a number of challenges. In this section, I describe some challenges to

Differences among fixed-, random-, and Mixed-Effects Models

It is easiest to begin with the simple case in which you are interested only in the mean effect size among a set of studies,

Practical Matters: The Limits of Interpreting Moderators in Meta-Analysis

Notwithstanding the considerable flexibility of a regression framework and the SEM approach for moderator analysis in meta-analysis, you should con­sider three potential limits when drawing

An Alternative SEM Approach for Moderation in meta-analysis

Cheung (2008) described an approach to meta-analysis within an SEM frame­work that can be used for moderator analyses as described in this chapter, as well

A General Multiple Regression Framework for Moderation in meta-analysis

After considering the regression approach to analyzing continuous modera­tors (previous section), you are probably wondering whether this approach allows for evaluation of multiple moderators—it does.

Continuous Moderators in meta-analysis

Continuous moderators in meta-analysis are coded study variables that can be considered to vary along a continuum of possible values. For example, mean characteristics of

Categorical Moderators in meta-analysis

1. Evaluating the Significance of a categorical Moderator The logic of evaluating categorical moderators in meta-analysis parallels the use of ANOVA in primary data analysis.

Where does meta-analysis fit in the research process?

Publications

Many journals encourage researchers to submit systematic reviews and meta-analyses that summarize the body of evidence on a specific question, and this approach is replacing the traditional narrative review. Meta-analyses also play supporting roles in other papers.  For example, a paper that reports results for a new primary study might include a meta-analysis in the introduction to synthesize prior data and help to place the new study in context.

Planning new studies

Meta-analyses can play a key role in planning new studies. The meta-analysis can help identify which questions have already been answered and which remain to be answered, which outcome measures or populations are most likely to yield significant results, and which variants of the planned intervention are likely to be most powerful.

Grant applications

Meta-analyses are used in grant applications to justify the need for a new study.  The meta-analysis serves to put the available data in context and to show the potential utility of the planned study. The graphical elements of the meta-analysis, such as the forest plot, provide a mechanism for presenting the data clearly, and for capturing the attention of the reviewers. Some funding agencies now require a meta-analysis of existing research as part of the grant application to fund new research.

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The classification process, called “coding,” consists of marking text passages with short alphanumeric codes. This creates “categorical variables” that represent the original, verbal information and that can then be analyzed by standard statistical methods. The text passages can come from structured interviews, focus group discussions, case studies, open-ended questions on survey instruments, workpapers, agency documents, and previous evaluations. 1 Content analysis is particularly useful in GAO work because of the large quantity of written material that evaluators typically collect during a project, especially when it
comes from diverse and unstructured sources.

To classify a document’s key ideas, the evaluator identifies its themes, issues, topics, and so on. Theresult  might be a simple list of the topics in a series of meeting notes. Content analysis can go further if theevaluator counts  the frequency of statements, detects subtle differences in their intensity, or examines issues over time, in different situations, or from different groups.

Thus, content analysis can not only help summarize the formal content of written material; it can also describe the attitudes or perceptions of the author of that material. For example, if an evaluator wanted to assess the effects of a program on the lives of older people from their perspective, he or she could analyze open-ended interview responses to determine their outlook on life, loneliness, or security. Similarly, an evaluator could assess the effect of Voice of America broadcasts by analyzing the content of Soviet newspaper articles and radio broadcasts. (Inkeles, 1952)

Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.

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1. In Stars and Stripes: Inherent Conflicts Lead to Allegations of Military Censorship (GAO, 1988), GAO evaluators used content analysis to help assess issues of censorship, news management, and other influences on various editions of the military newspaper. Details of technique and substance from this report are used as examples throughout this transfer paper.
2. In Student Loans: Direct Loans Could Save Billions in First 5 Years With Proper Implementation (GAO, 1992c), GAO evaluators examined transcripts of focus groups discussing the difficulty of implementing a student loan program. The participants’ views on whether the Department of Education could administer a direct loan program were mixed, but the evaluators were able, through content analysis, to highlight the dominant views and the reasons for them.
3. Federal Employment: How Federal Employees View the Government As a Place to Work (GAO, 1992a) reported that while the majority of the survey respondents looked favorably on working for the government, many did not. The evaluators used content analysis to assess the respondent’s insightful, written comments to open-ended questions. An appendix in the report is devoted to the analysis of these comments.
4. Another excellent example of the use of content analysis appears in Women in the Military: Deployment in the Persian Gulf War (GAO, 1993c). For this study, the evaluators gave a primarily positive assessment of women’s performance, using content analysis to determine that while men and women endured similarly harsh encampment facilities and conditions, both men and women considered health and hygiene problems inconsequential and their cohesion in mixed-gender units effective.
5. Among other fine examples of the use of content analyis is Veterans’ Health Care: Veterans’. Perceptions of VA Services and VA’s Role in Health Care Reform (GAO, 1994a). The report’s scope and methodology section details the analysis and summary of veterans’ views that changing the VA system could, among other things, diminish or eliminate their benefits as well as harm them both emotionally and in terms of their specialized health care needs.

Other uses of content analysis in GAO reports include an analysis of transcripts of focus groups on people’s ability to participate in food assistance programs (GAO, 1990), an analysis of descriptive text on the maintenance of aging aircraft (GAO, 1993a), and an analysis of open-ended discussions with Amerasian immigrants on their experiences in Vietnam, the Philippines, and the United States (GAO, 1994b).

Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.

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Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.

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One problem with surveys and some experimental methods is that evaluators and their informants can interact during data collection in ways other than how they would “naturally” react. For example, a content analysis of the hearing transcripts might be more useful than interviews with federal officials about what took place during public hearings on proposed environmental regulations. The officials might leave out important points, unconsciously or purposely, in order to protect themselves, but the transcripts provide the complete record. Thus, bias can be reduced dining data collection. Similarly, the evaluator can eliminate from analysis survey questions that might be inappropriate because they invaded a respondent’s privacy.

2. It Can Deal With Large Volumes of Material

Content analysis has explicit procedures and quality control checks that make it possible for only a few or a great number of evaluators to analyze large volumes of textual data. Furthermore, the explicit procedures and quality control checks allow two or more groups of analysts to work on the same kind of data in different geographic locations, and computer software may be used to perform many of the required steps. (See appendix II.)

3. It Is Systematic

Content analysis can help evaluators learn more about the issues and programs they examine because it is systematic. It has structured forms that allow evaluators to extract relevant information more consistently than if they were reading the same documents only casually.

4. It Can Corroborate Other Evaluation Methods

When the findings from content analysis are not the main evidence in an evaluation, they can still be used to help corroborate other findings, such as responses from closed-ended surveys or from economic measures. For example, Webb and colleagues have described how investigators can use “multiple operations” to increase confidence in their findings, although we do not discuss them in this paper. (Webb et al., 1981)

Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.

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Source: GAO (2013), Content Analysis: A Methodology for Structuring and Analyzing Written Material: PEMD-10.3.1, BiblioGov.

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