SCIENTIFIC METHOD AND SCIENTIFIC INQUIRY
SCIENTIFIC METHOD
Scientific method refers to a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry must be based on gathering observable, empirical and measurable evidence subject to specific principles of reasoning. A scientific method consists of the collection of data through observation and experimentation, and the formulation and testing of hypotheses
THE PROCESS OF SCIENTIFIC INQUIRY
Scientific inquiry employs both induction and deduction. Induction uses particular or specific instances as observed by the scientist to arrive at general conclusions or axioms. This is the use of data or evidence to arrive at generalities, often called empiricism. The mathematical expression of induction is found in statistical inference: the scientist examines many cases and arrives at a conclusion. Deduction in contrast, begins with what is general and applies it to particular cases, this is often called logic or rationalism. Deductions is employed by the scientist in making the leap from a hypothesis (a generalization) to an operational definition so that the hypothesis can be tested with specific real-world phenomena or cases.
ACQUIRING EMPIRICAL DATA
In mass communication research several methods are frequently employed to acquire empirical data in a systematic fashion. These are:-
ü Survey Research
ü Content Analysis
ü Experimental Design
ü Case Studies
SURVEY RESEARCH: The sample survey is used to answer question about how a large number of subjects feel, behave, or are especially with regard to variables that change over time. It is the study of a portion or sample of a specific “population” (magazine subscribers, newspaper readers, television viewers etc). If done according to statistical principles, generalizations can then be made from the sample to the population with a certain degree of assurance or confidence. A sample is less costly than a census, which is an enumeration of all the members of population.
Sample survey can also compare relationship between variables by correlation. Often variables of interest to the researcher cannot be manipulated in an experiment. The survey allows for comparisons between people who differ on a given characteristics and also for differences in their behaviors.
CONTENT ANALYSIS: Content analysis is a systematic method of analyzing message content. It is a tool for analyzing the messages of certain communicators. Instead of interviewing people or asking them to respond to questionnaires, as in survey or observing behavior, as in the human experiment the investigator using content analysis examines the communications that have been produced at times and places of his or her own choosing. It has been described as the “objective, systematic, and quantitative description” of communication content. Six main stages of content analysis are:-
v Selecting content for analysis
v Units of content
v Preparing content for coding
v Coding the content
v Counting and weighting
v Drawing conclusions
Selecting Content for Analysis: If content is huge the world contains a near-infinite amount of content. It’s rare that an area of interest has so little content that you can analyse it all. Even when you do analyse the whole of something (e.g. all the pictures in one issue of a magazine) you will usually want to generalize those findings to a broader context (such as all the issues of that magazine).
Deciding sample size unless you want to look at very fine distinctions, you don’t need a huge sample. The same principles apply for content analysis as for surveys: most of the time, a sample between 100 and 2000 items is enough - as long as it is fully representative. For radio and TV, the easiest way to sample is by time.
The need for a focus, when you set out to do content analysis, the first thing to acknowledge is that it’s impossible to be comprehensive. No matter how hard you try, you can’t analyse content in all possible ways. I’ll demonstrate, with an example. Let’s say that you manage a radio station. It’s on air for 18 hours a day, and no one person seems to know exactly what is broadcast on each program. So you decide that during April all programs will be taped. Then you will listen to the tapes and do a content analysis.
Units of content: To be able to count content, your corpus needs to be divided into a number of units, roughly similar in size. There’s no limit to the number of units in a corpus, but in general the larger the unit, the fewer units you need. If the units you are counting vary greatly in length, and if you are looking for the presence of some theme, a long unit will have a greater chance of including that theme than will a short unit. If the longest units are many times the size of the shortest, you may need to change the unit - perhaps "per thousand words" instead of "per web page." If the interviews vary greatly in length, a time-based unit may be more appropriate than "per interview."
Preparing content for coding: Before content analysis can begin, it needs to be preserved in a form that can be analysed. For print media, the internet, and mail surveys (which are already in written form) no transcription is needed. However, radio and TV programs, as well as recorded interviews and group discussions, are often transcribed before the content analysis can begin. Full transcription – that is, conversion into written words, normally into a computer file – is slow and expensive. Though it’s sometimes necessary, full transcription is often avoidable, without affecting the quality of the analysis. A substitute for transcription is what I call content interviewing
Coding the content: Coding in content analysis is summarizing responses into groups, reducing the number of different responses to make comparisons easier. Thus you need to be able to sort concepts into groups, so that in each group the concepts are both
- as similar as possible to each other, and
- as different as possible from concepts in every other group.
Does that seem puzzling? Read on: the examples below will make it clearer.
Counting and weighting: When all the preparation for content analysis has been done, the counting is usually the quickest part - specially if all the data is on a computer file, and software is used for the counting. Software is an important tool for content analysis, but this page has mentioned it only briefly. Because software and links to it are constantly changing, we have a separate page on content analysis software.
Drawing conclusions: An important part of any content analysis is to study the content that is not there: what was not said. This sounds impossible, doesn’t it? How can you study content that’s not there? Actually, it’s not hard, because there’s always an implicit comparison. The content you found in the analysis can be compared with the content that you (or the audience) expected - or it can be compared with another set of content. It’s when you compare two corpora (plural of corpus) that content analysis becomes most useful. This can be done either by doing two content analyses at once (using different corpora but the same principles) or comparing your own content analysis with one that somebody else has done. If the same coding frame is used for both, it makes the comparison much simpler.
EXPERIMENTAL DESIGN: Experimental design are the classic method of dealing with questions of causality. An experiment involves the control or manipulation of a variable by the experimenter and an observation or measurement of the result in an objective and systematic way. When it is possible to use the experimental method, it is the research method most apt to provide answers of cause and effect.
In designing a controlled experiment the investigator must have two setups: first, an experimental setup that receives the test treatment (known as the independent variable); and second, a control setup that does not receive the test treatment (the independent variable is absent or set at a standard value). The two setups must be identical except for the independent variable so that the investigator is able to attribute changes between the two groups, the dependent variable, to the test treatment. All of the factors that are kept equal in the experimental and control setups are called standardized variables.
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