An Introduction to Scientific work and its Methodology

1
An Introduction to Scientific work and its
Methodology
Slovak University of Technology Faculty of
Material Science and Technology in Trnava

• Seminars

2
1 Introduction

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

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Philosophy of Research

• Structure of Research
• Deduction Induction
• Introduction to Validity

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Structure of Research
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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.

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Deductive reasoning
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• 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.

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Inductive reasoning
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Introduction to Validity

• Validitythe best available approximation to the
  truth of a given proposition, inference, or
  conclusion

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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?

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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.

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Evaluation Research

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

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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.

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The Planning-Evaluation Cycle
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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.

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Conceptualizing

• Problem Formulation
• Where do research topics come from?
• The most common sources of research ideas is the
  experience of practical problems in the field .
• The literature in the specific field

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• 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
• - how long the research will take to accomplish.
• - ethical constraints
• - the needed cooperation
• - the costs of conducting the research

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The Literature Review

• Concentrate your efforts on the scientific
  literature
• Do the review early
• Be careful of Citation and References

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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.

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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'.

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• 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

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• 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.

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• 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.

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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).

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• 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

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• 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.

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• 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.

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• 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

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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.

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

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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)

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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.

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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

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II Sampling

• External validity
• Sampling Terminology
• Statistical terms
• Probability, nonprobability

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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

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• 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.

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the Sampling Model
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• 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.

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the Proximal Similarity Model
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Sampling Terminology
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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.

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Statistical Terms in Sampling
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• 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.

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The Sampling Distribution
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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.

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The 68, 95, 99 Percent Rule
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• 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.

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Example
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Probability Sampling

• Simple Random Sampling

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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.
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Systematic Random Sampling
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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
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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.

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3 Measurement

• Construct Validity
• Reliability
• Survey Research(design and implementation of
  interviews and questionnaires )
• Scaling
• Qualitative Measures

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Construct validity

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

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Idea of Construct Validity
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Reliability

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

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4 general classes of reliability

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

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1.Inter-Rater or Inter-Observer ReliabilityUsed
to assess the degree to which different
raters/observers give consistent estimates of the
same phenomenon.
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2.Test-Retest Reliability Used to assess the
consistency of a measure from one time to
another.
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3.Parallel-Forms ReliabilityUsed to assess the
consistency of the results of two tests
constructed in the same way from the same content
domain.
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4.Internal Consistency ReliabilityUsed to assess
the consistency of results across items within a
test.
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Survey Research

• Types of Surveys
• Questionnaires (mail survey, group administered
  questionnaire, household drop-off survey)
• Interviews (personal interview, telephone
  interview)

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Types Of Questions

• 1. Dichotomous Questions

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• 2. Questions Based on Level Of Measurement
• a nominal question

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• ordinal question

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

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Filter or Contingency Questions
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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.

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

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4. Design

• Internal Validity
• Research Design
• Types of Designs

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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).

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Internal validity
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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.

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Types of Designs

• a randomized experiment
• quasi-experimental design
• non-experimental design

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Assignment 2

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

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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)

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Conclusion Validity

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

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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)

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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.

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• 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

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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.

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Frequency distribution bar chart.
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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

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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.

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• The formula for the standard deviation

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

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• 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

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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

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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.

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• 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.

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

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6 Scientific Paper

• How to write Scientific Paper

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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.")

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• 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.

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• 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)

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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.

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• 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.)

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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...

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• 3. Use short words.
• Instead of possess Write
  have
• demonstrate -
  show
• terminate -
  end

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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

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• 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
•   

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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.

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

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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

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• How to read Scientific paper

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• 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.

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• 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.

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• 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.

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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?

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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

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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