An Introduction to Scientific work and its Methodology Seminars Slovak University of Technology Faculty of Material Science and Technology in Trnava.

An Introduction to Scientific work and its Methodology

Seminars

Slovak University of TechnologyFaculty of Material Science and Technology in Trnava

1 Introduction

Philosophy of Research Ethics in Research Evaluation Research Conceptualizing Language of Research

Philosophy of Research

Structure of Research Deduction & Induction Introduction to Validity

Structure of Research

Deduction & Induction

Deductive and Inductive Thinking In logic, we often refer to the two broad methods of

reasoning as the deductive and inductive approaches.

Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top-down" approach. We might begin with thinking up a theory about our topic of interest. We then narrow that down into more specific hypotheses that we can test. We narrow down even further when we collect observations to address the hypotheses. This ultimately leads us to be able to test the hypotheses with specific data -- a confirmation (or not) of our original theories.

Deductive reasoning

Inductive reasoning works the other way, moving from specific observations to broader generalizations and theories. Informally, we sometimes call this a "bottom up" approach (please note that it's "bottom up" and not "bottoms up" which is the kind of thing the bartender says to customers when he's trying to close for the night!). In inductive reasoning, we begin with specific observations and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up developing some general conclusions or theories.

Inductive reasoning

Introduction to Validity

Validity:the best available approximation to the

truth of a given proposition, inference, or conclusion

4 validity types

Conclusion Validity: In this study, is there a relationship between the two variables?

Internal Validity: Assuming that there is a relationship in this study, is the relationship a causal one?

Construct Validity: Assuming that there is a causal relationship in this study, can we claim that the program reflected well our construct of the program and that our measure reflected well our idea of the construct of the measure?

External Validity: Assuming that there is a causal relationship in this study between the constructs of the cause and the effect, can we generalize this effect to other persons, places or times?

Ethics in Research

Ethical Issues : voluntary participation informed consent not put participants in a situation where they

might be at risk of harm confidentiality anonymity the ethical issue of a person's right to service.

Evaluation Research

Evaluation is the systematic acquisition and assessment of information to provide useful feedback about some object

Types of Evaluation

There are many different types of evaluations depending on the object being evaluated and the purpose of the evaluation. Perhaps the most important basic distinction in evaluation types is that between formative and summative evaluation.

Formative evaluations strengthen or improve the object being evaluated -- they help form it by examining the delivery of the program or technology, the quality of its implementation, and the assessment of the organizational context, personnel, procedures, inputs, and so on.

Summative evaluations, in contrast, examine the effects or outcomes of some object -- they summarize it by describing what happens subsequent to delivery of the program or technology; assessing whether the object can be said to have caused the outcome; determining the overall impact of the causal factor beyond only the immediate target outcomes; and, estimating the relative costs associated with the object.

The Planning-Evaluation Cycle

An Evaluation Cultureshould be: action-oriented accessible, teaching-oriented diverse, inclusive, participatory, responsive and

fundamentally non-hierarchical humble, self-critical interdisciplinary honest, truth-seeking prospective and forward-looking The evaluation culture is one that will emphasize fair,

open, ethical and democratic processes.

Conceptualizing

Problem Formulation Where do research topics come from?1. The most common sources of research ideas is the experience

of practical problems in the field .

2. The literature in the specific field

Is the study feasible? There are several practical considerations that almost always

need to be considered when deciding on the feasibility of a research project :

1. - how long the research will take to accomplish.

2. - ethical constraints

3. - the needed cooperation

4. - the costs of conducting the research

The Literature Review

Concentrate your efforts on the scientific literature

Do the review early Be careful of Citation and References

Concept Mapping

Concept mapping is a general method that can be used to help any individual or group to describe their ideas about some topic in a pictorial form.

Language of Research

Variables A variable is any entity that can take on

different values. For instance, age can be considered a variable because age can

take different values for different people or for the same person at different times. Similarly, country can be considered a variable because a person's country can be assigned a value.

Variables aren't always 'quantitative' or numerical. The variable 'gender' consists of two text values: 'male' and 'female'.

An attribute is a specific value on a variable. For instance, the variable sex or gender has two

attributes: male and female. Or, the variable agreement might be defined as having five attributes:

1 = strongly disagree 2 = disagree 3 = neutral 4 = agree 5 = strongly agree

Another important distinction having to do with the term 'variable' is the distinction between an independent and dependent variable.

The independent variable is what you (or nature) manipulates

The dependent variable is what is affected by the independent variable -- your effects or outcomes.

For example, if you are studying the effects of a new educational program on student achievement, the program is the independent variable and your measures of achievement are the dependent ones.

Each variable should be exhaustive, it should include all possible answerable responses.

For instance, if the variable is "religion" and the only options are "Protestant", "Jewish", and "Muslim", there are quite a few religions.

The list does not exhaust all possibilities. On the other hand, if you exhaust all the possibilities with some variables you would simply have too many responses. The way to deal with this is to explicitly list the most common attributes and then use a general category like "Other" to account for all remaining ones.

Hypotheses

An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory (inductive research).

Usually, we call the hypothesis that you support (your prediction) the alternative hypothesis, and we call the hypothesis that describes the remaining possible outcomes the null hypothesis.

Sometimes we use a notation like HA or H1 to represent the alternative hypothesis or your prediction, and HO or H0 to represent the null case

The important thing to remember about stating hypotheses is that you formulate your prediction (directional or not), and then you formulate a second hypothesis that is mutually exclusive of the first and incorporates all possible alternative outcomes for that case.

When your study analysis is completed, the idea is that you will have to choose between the two hypotheses.

If your prediction was correct, then you would (usually) reject the null hypothesis and accept the alternative.

If your original prediction was not supported in the data, then you will accept the null hypothesis and reject the alternative.

The logic of hypothesis testing is based on these two basic principles:

the formulation of two mutually exclusive hypothesis statements that, together, exhaust all possible outcomes

the testing of these so that one is necessarily accepted and the other rejected

Types of Data :

qualitative quantitative The way we typically define them, we call data

'quantitative' if it is in numerical form and 'qualitative' if it is not. The qualitative data could be much more than just words or text. Photographs, videos, sound recordings and so on, can be considered qualitative data.

All quantitative data is based upon qualitative judgments; and all qualitative data can be described and manipulated numerically.

Unit of Analysis

The unit of analysis is the major entity that you are analyzing in your study. For instance, any of the following could be a unit of analysis in a study:

individuals groups artifacts (books, photos, newspapers) geographical units (town, census tract, state) social interactions (dyadic relations, divorces,

arrests)

Research Fallacies

A fallacy is an error in reasoning, usually based on mistaken assumptions. Researchers are very familiar with all the ways they could go wrong, with the fallacies they are susceptible to.

Assignment # 1

1. identify meaningful question or problem 2. prepare literature review, internet

survey for the topic mention above (give special attention on citation and references)

3. create concept map of chosen topic 4. suggest scientific hypotheses

II Sampling

External validity Sampling Terminology Statistical terms Probability, nonprobability

External validity

external validity is the degree to which the conclusions in your study would hold for other persons in other places and at other times

external validity refers to the approximate truth of conclusions the involve generalizations

In science there are two major approaches to how we provide evidence for a generalization :

the Sampling Model In the sampling model, you start by identifying the

population you would like to generalize to. Then, you draw a fair sample from that population and conduct your research with the sample. Finally, because the sample is representative of the population, you can automatically generalize your results back to the population.

the Sampling Model

the Proximal Similarity Model Under this model, we begin by thinking about different

generalizability contexts and developing a theory about which contexts are more like our study and which are less so. For instance, we might imagine several settings that have people who are more similar to the people in our study or people who are less similar. This also holds for times and places. When we place different contexts in terms of their relative similarities, we can call this implicit theoretical a gradient of similarity. Once we have developed this proximal similarity framework, we are able to generalize.

We conclude that we can generalize the results of our study to other persons, places or times that are more like (that is, more proximally similar) to our study. Notice that here, we can never generalize with certainty -- it is always a question of more or less similar.

the Proximal Similarity Model

Sampling Terminology

Random Selection & Assignment

Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study.

It is possible to have both random selection and assignment in a study. Let's say you drew a random sample of 100 clients from a population list of 1000 current clients of your organization. That is random sampling. Now, let's say you randomly assign 50 of these clients to get some new additional treatment and the other 50 to be controls. That's random assignment.

Statistical Terms in Sampling

A response is a specific measurement value that a sampling unit supplies.

When we look across the responses that we get for our entire sample, we use a statistic (mean, median, mode).

If you measure the entire population and calculate a value like a mean or average, we don't refer to this as a statistic, we call it a parameter of the population.

The Sampling Distribution

Sampling Error

Sampling error gives us some idea of the precision of our statistical estimate. A low sampling error means that we had relatively less variability or range in the sampling distribution.

So how do we calculate sampling error? We base our calculation on the standard deviation of

our sample. The greater your sample size, the smaller the standard

error. If you take a sample that consists of the entire population you actually have

no sampling error because you don't have a sample, you have the entire population. In that case, the mean you estimate is the parameter.

The 68, 95, 99 Percent Rule

There is a general rule that applies whenever we have a normal or bell-shaped distribution.

Start with the average -- the center of the distribution. If you go up and down (i.e., left and right) one standard unit, you will include approximately 68% of the cases in the distribution (i.e., 68% of the area under the curve).

If you go up and down two standard units, you will include approximately 95% of the cases.

If you go plus-and-minus three standard units, you will include about 99% of the cases.

Example

Probability Sampling

Simple Random Sampling

Stratified Random Sampling, also sometimes called proportional or quota random sampling, involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup.

Systematic Random Sampling

Cluster (Area) Random SamplingIn cluster sampling, we follow these steps: divide population into clusters (usually along geographic boundaries) randomly sample clusters measure all units within sampled clusters

Nonprobability Sampling

We can divide nonprobability sampling methods into two broad types: accidental or purposive.

Most sampling methods are purposive in nature because we usually approach the sampling problem with a specific plan in mind.

3 Measurement

Construct Validity Reliability Survey Research(design and implementation of

interviews and questionnaires )

Scaling Qualitative Measures

Construct validity

Construct validity is the approximate truth of the conclusion that your operationalization accurately reflects its construct.

Idea of Construct Validity

Reliability

Reliability has to do with the quality of measurement. In its everyday sense, reliability is the "consistency" or "repeatability" of your measures.

4 general classes of reliability:

Inter-Rater or Inter-Observer Reliability Test-Retest Reliability Parallel-Forms Reliability Internal Consistency Reliability

1.Inter-Rater or Inter-Observer ReliabilityUsed to assess the degree to which different raters/observers give consistent estimates of the same phenomenon.

2.Test-Retest Reliability

Used to assess the consistency of a measure from one time to another.

3.Parallel-Forms ReliabilityUsed to assess the consistency of the results of two tests constructed in the same way from the same content domain.

4.Internal Consistency ReliabilityUsed to assess the consistency of results across items within a test.

Survey Research

Types of Surveys: Questionnaires (mail survey, group administered questionnaire,

household drop-off survey)

Interviews (personal interview, telephone interview)

Types Of Questions:

1. Dichotomous Questions

2. Questions Based on Level Of Measurement

a nominal question

ordinal question:

survey questions that attempt to measure on an interval level- Likert response scale

Filter or Contingency Questions

Scaling

Scaling is the branch of measurement that involves the construction of an instrument that associates qualitative constructs with quantitative metric units.

Scaling is the assignment of objects to numbers according to a rule.

Scaling

Dimensionality

Dimensionality

The semantic differential. Their theory essentially states that you can rate any object along those three dimensions.

Qualitative Measures

Here are a number of important questions you should consider before undertaking qualitative research:

Do you want to generate new theories or hypotheses?

Do you need to achieve a deep understanding of the issues?

Are you willing to trade detail for generalizability?

Is funding available for this research?

4. Design

Internal Validity Research Design Types of Designs

Internal Validity

Internal Validity is the approximate truth about inferences regarding cause-effect or causal relationships.

The key question in internal validity is whether observed changes can be attributed to your program or intervention (i.e., the cause) and not to other possible causes (sometimes described as "alternative explanations" for the outcome).

Internal validity

Research Design

Research design can be thought of as the structure of research -- it is the "glue" that holds all of the elements in a research project together.

Types of Designs

a randomized experiment quasi-experimental design non-experimental design

Assignment # 2

Prepare survey sample for your research Identify variables Prepare questionnaire Collect data

5 Analysis

The data analysis involves three major steps:

Cleaning and organizing the data for analysis (Data Preparation)

Describing the data (Descriptive Statistics) Testing Hypotheses and Models (

Inferential Statistics)

Conclusion Validity

Conclusion validity is the degree to which conclusions we reach about relationships in our data are reasonable.

Data Preparation

Logging the Data Checking the Data For Accuracy Developing a Database Structure Entering the Data into the Computer Data Transformations (missing values,

item reversals ,scale totals, categories)

Descriptive Statistics

Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.

Descriptive Statistics are used to present quantitative descriptions in a manageable form.

Descriptive statistics help us to simply large amounts of data in a sensible way.

Descriptive statistics provide a powerful summary that may enable comparisons across people or other units.

Univariate Analysis

Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at:

the distribution the central tendency the dispersion

The Distribution. The distribution is a summary of the frequency of

individual values or ranges of values for a variable. One of the most common ways to describe a single

variable is with a frequency distribution. Depending on the particular variable, all of the data

values may be represented, or you may group the values into categories first (e.g., with age, price, or temperature variables, it would usually not be sensible to determine the frequencies for each value. Rather, the value are grouped into ranges and the frequencies determined.). Frequency distributions can be depicted in two ways, as a table or as a graph.

Frequency distribution bar chart.

Central Tendency

The central tendency of a distribution is an estimate of the "center" of a distribution of values. There are three major types of estimates of central tendency:

Mean Median Mode

The Dispersion Dispersion refers to the spread of the values around the central

tendency. There are two common measures of dispersion, the range and the standard deviation.

The range is simply the highest value minus the lowest value. In our example distribution, the high value is 36 and the low is 15, so the range is 36 - 15 = 21.

The Standard Deviation is a more accurate and detailed estimate of dispersion because an outlier can greatly exaggerate the range (as was true in this example where the single outlier value of 36 stands apart from the rest of the values. The Standard Deviation shows the relation that set of scores has to the mean of the sample.

The formula for the standard deviation:

We can describe the standard deviation as:the square root of the sum of the squared deviations from the mean divided by the number of scores minus one

We can calculate these univariate statistics by hand, it gets quite tedious when you have more than a few values and variables. Every statistics program is capable of calculating them easily for you. For instance, SPSS produce the following table as a result:

N 8 Mean 20.8750 Median 20.0000 Mode 15.00 Std. Deviation 7.0799 Variance 50.1250 Range 21.00

Correlation

The correlation is one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables.

Calculating the Correlation Testing the Significance of a Correlation The Correlation Matrix

Inferential Statistics

With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.

We use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

The T-Test

Whenever you wish to compare the average performance between two groups you should consider the t-test for differences between groups.

The t-test assesses whether the means of two groups are statistically different from each other.

Figure shows the formula for the t-test and how the numerator and denominator are related to the distributions.

Assignment # 3

Prepare data for analysis Make Univariate Analysis (calculate

Mean,Median, mode, Std. Deviation, Variance, Range, frequency distribution)

Prepare graphic presentation of your data

6 Scientific Paper

How to write Scientific Paper

Write accurately: Scientific writing must be accurate. Although writing instructors may tell you not to

use the same word twice in a sentence, it's okay for scientific writing, which must be accurate.

(A student who tried not to repeat the word "hamster" produced this confusing sentence: "When I put the hamster in a cage with the other animals, the little mammals began to play.")

Make sure you say what you mean.

Instead of: The rats were injected with the drug. (sounds like a syringe was filled with drug and ground-up rats and both were injected together)Write: I injected the drug into the rat.

Be careful with commonly confused words: Temperature has an effect on the reaction.Temperature affects the reaction.

I used solutions in various concentrations. (The solutions were 5 mg/ml, 10 mg/ml, and 15 mg/ml)I used solutions in varying concentrations. (The concentrations I used changed; sometimes they were 5 mg/ml, other times they were 15 mg/ml.)

Less food (can't count numbers of food)Fewer animals (can count numbers of animals)

A large amount of food (can't count them)A large number of animals (can count them)

Write clearly

1. Write at a level that's appropriate for your audience.

"Like a pigeon, something to admire as long as it isn't over your head." Anonymous

 2. Use the active voice. It's clearer and more concise than the passive voice.

  Instead of: An increased appetite was manifested by the rats and an increase in body weight was measured.Write: The rats ate more and gained weight.

3. Use the first person.  Instead of: It is thought

Write: I think

  Instead of: The samples were analyzed Write: I analyzed the samples

 4. Avoid dangling participles.  "After incubating at 30 degrees C, we

examined the petri plates." (You must've been pretty warm in there.)

Write succinctly

1. Use verbs instead of abstract nouns

  Instead of: take into consideration Write: consider

 2. Use strong verbs instead of "to be"  Instead of: The enzyme was found to be the active

agent in catalyzing... Write: The enzyme catalyzed...

3. Use short words.

Instead of: possess Write: have

demonstrate - show

terminate - end

4. Use concise terms.

Instead of: prior to due to the fact that in a considerable number

of cases the vast majority of during the time that in close proximity to it has long been known

that

Write: Before Because Often

Most When Near I'm too lazy to look up the

reference

5. Use short sentences.

A sentence made of more than 40 words should probably be rewritten as two sentences.

 "The conjunction 'and' commonly serves to indicate that the writer's mind still functions even when no signs of the phenomenon are noticeable." Rudolf Virchow, 1928

Check your grammar, spelling and punctuation

1. Use a spellchecker, but be aware that they don't catch all mistakes.

  "When we consider the animal as a hole,..." Student's paper

2. Your spellchecker may not recognize scientific terms. For the correct spelling, try Biotech's Life Science Dictionary or one of the technical dictionaries on the reference shelf in the Biology or Health Sciences libraries.

 3. Don't, use, unnecessary, commas.  4. Proofread carefully to see if you

any words out.

Formatting

The paper must have all the sections in the order given below, following the specifications outlined for each section (all pages numbers are approximate):

Title Page Abstract (on a separate single page) The Body (no page breaks between sections in the body)

Introduction (2-3 pages) Methods (7-10 pages)

Sample (1 page) Measures (2-3 pages) Design (2-3 pages) Procedures (2-3 pages)

Results (2-3 pages) Conclusions (1-2 pages)

References Tables (one to a page) Figures (one to a page) Appendices

How to read Scientific paper

1. Skimming. Skim the paper quickly, noting basics like headings, figures and the like. This takes just a few minutes. You're not trying to understand it yet, but just to get an overview.

2. Vocabulary. Go through the paper word by word and line by

line, underlining or highlighting every word and phrase you don't understand.

Look up simple words and phrases.

Get an understanding from the context in which it is used.

3. Comprehension,

section by section. Try to deal with all the words and phrases, although a few technical terms. Now go back and read the whole paper, section by section, for comprehension.

4. Reflection and criticism.

After you understand the article and can summarize it, then you can return to broader questions and draw your own conclusions

Here are some questions that may be useful in analyzing various kinds of research papers:

What is the overall purpose of the research? Do you agree with the author's rationale for studying the question in

this way? Were the measurements appropriate for the questions the

researcher was approaching? If human subjects were studied, do they fairly represent the

populations under study? What is the one major finding? Were enough of the data presented so that you feel you can judge

for yourself how the experiment turned out?

Assignment # 4

Read sample of Scientific paper, identify key words, prepare critical annotation

Write paper-report of your research Prepare PowerPoint presentation of your

research

PowerPoint presentation was created from the following sources:

http://www.socialresearchmethods.net/kb/index.php (Research Methods Knowledge Base)

http://www2.lv.psu.edu/jxm57/irp/scipaper.html WRITING A SCIENTIFIC RESEARCH ARTICLE (FORMAT FOR THE PAPER) How to Read a Scientific Research Paper- four-step guide for students by Ann McNeal, School of Natural Science, Hampshire

College, Amherst MA 01002 file://localhost/H:/Research/HOW_READ.html