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Research Methods in Entomology Basic Terms

1. What is research?
 Refers to a search for knowledge. Scientific and systematic search for pertinent information on a
specific topic.
 A careful investigation or inquiry especially through search for new facts in any branch of
 An objective, systematic and conscious attempt enunciating a selected problem, formulating
relevant hypothesis and on this basis, collecting suitable data, analyzing them and reaching
certain conclusion.
 Commissioned research:
 A research, survey or other work, the costs of which are wholly paid for by the customer.
 Commissioned research may mean a wider research project, a smaller service research or a
2. Basic types of research:
exploratory or formulative research:
 To gain familiarity with a phenomenon or to achieve new insights into it termed as exploratory or
formulative research.
Descriptive research:
 To portray accurately the characteristics of a particular individual, situation or a group (known as
descriptive research, includes (survey and fact finding) of different kinds of groups.
Diagnostic research:
 To determine the frequency with which something occurs or with which it is associated with
something else (known as diagnostic research).
Hypothesis-testing research:
 To test a hypothesis of a causal relationship between variables such studies are known as
hypothesis-testing research.
Ex post facto research:( for descriptive studies )
 The projects that are used for descriptive studies in which the researcher seeks to measure such as
host preferences or similar data.
 Ex post facto studies also include attempts by researchers to discover causes even when they cannot
control the variables.
Applied research:
 finding a solution for an immediate problem facing a society or an industrial/business organization
fundamental research:
 Concerned with generalizations and with the formulation of a theory.
Analytical research:
 To use facts or information already available, and analyze these to make a critical evaluation of the
Quantitative research:
 Based on the measurement of quantity or amount. It is applicable to phenomena that can be
expressed in terms of quantity.
Qualitative research:
 Concerned with qualitative phenomenon, we quite often talk of ‘Motivation Research’, an
important type of qualitative research.
Conceptual research:
 Elated to some abstract idea(s) or theory. philosophers and thinkers to develop new concepts or to
reinterpret existing ones
Empirical research:
 Experience or observation alone, often without due regard for system and theory.
Formalized research:
 Those with substantial structure and with specific hypotheses to be tested.
conclusion-oriented research:
 when a researcher is free to pick up a problem
Decision-oriented research:
 When a researcher is not free to pick up a problem, need a decision maker.
Operations research:
 is an example of decision oriented research since it is a scientific method of providing executive
departments with a quantitative basis for decisions regarding.
Original Research:
 A research that is not based on summary, reviews, of earlier publications,
Research Method:
Refer to the ways in selecting and constructing research techniques.
Research methods used by scholars
1Theoretical problem
2. Factual problem
3. Application problem –
Action Research.
Research methodology:
A way to systematically solve the research problem. Various steps that are generally adopted by a
researcher in studying his research problem along with the logic behind them
Research techniques:
Refer to instruments or tools we use in performing research operations such as making
observations, recording data, techniques of processing data and the like.
6. Scientific method:
 one and same in the branches (of science) and that method is the method of all logically trained
 the man who classifies facts of any kind whatever, who sees their mutual relation and describes their
sequences, is applying the Scientific Method
7. Experimentation:
 To test hypotheses and to discover new relationships. If any, among variables.
A hypothesis is a prediction about the outcome of an experiment.
Proposition or a set of Proposition which explains the occurrence of a specified group of phenomena
Hypothesis is basically used to check the estimate whether it’s purely by chance or its real and not
due to chance (Null & Alternative Hypothesis).
 Significance levels and P values are important tools that help you quantify and control this type of
error in a hypothesis test.
Null Hypothesis: (, H0 )
The null hypothesis typically corresponds to a general or default position.
If all treatments combinations show ( No significance effect )
There is no difference - by chance.
When a P value is less than or equal to the significance level, you reject the null hypothesis
Alternate Hypothesis (HA )
 An alternate hypothesis asserts a rival relationship between the phenomena measured by the null
 If all treatments combinations show ( significance effect )
 It means there is a difference, accepted when (P value is lower than 0.05)
 The type of test (left- or right- tailed or two tailed) is determined by the alternative hypothesis.
9. The level of significance:
 Maximum probability of committing a type I error. This probability is symbolized by α. That is, P
(type I error) = α.
10. Critical value:
 Separates the critical region from the noncritical region.
11. The critical or rejection region:
 the range of values of the test value that indicates that there is a significant difference and that the (
null hypothesis should be rejected )
12. The noncritical or non-rejection region:
 The range of values of the test value that indicates that the difference was probably due to chance
and that the (null hypothesis should not be rejected. )
13. The P-value (or probability value) :
 The probability of getting a sample statistic (such as the mean) or a more extreme sample statistic in
the direction of the alternative hypothesis when the null hypothesis is true.
 If P-value < α, reject the null hypothesis.
 If P-value > α, do not reject the null hypothesis.
 When a P value is less than or equal to the significance level, you reject the null hypothesis
 If the P value is less than your significance (alpha) level, the hypothesis test is statistically significant.
If the confidence interval does not contain the null hypothesis value, the results are statistically
If the P value is less than alpha, the confidence interval will not contain the null hypothesis value.
14. Innovation:
 Innovation involves acting on the creative ideas to make some specific and tangible difference in the
domain in which the innovation occurs.
 successful implementation of creative ideas within an organization
15. Invention:
 Creation of a product or introduction of a process for the first time.
16. Qualitative Data or Attributes:
 The characteristics or traits for which numerical value cannot be assigned, are called attributes, e.g.
level of infestation etc.
17. Quantitative Data or Variables:
 The characteristics or traits for which numerical value can be assigned, are called variables, e.g.
absolute relative etc.
Those which vary from person to person and can be quantified by employing measuring instrument.
The sample or group variation can be ascertained in terms of numerical values.
The characteristic or the trait in the behavioral science which can be quantified is termed as
19. Types of variables:
1: Continuous variables. : (Quantitative variables)
 Those for which fractional value exists and have meaning e.g. age, weight, achievement.
2: Discrete variables: : (Quantitative variables)
 Those on the other hand, which exist only in units not the fractional value (usually units of one) e.g.
30 boys, 25 girls, 40 Indians and 24 Americans.
 having only integer values
3: Binary variable: (dichotomous)
 Observations (i.e., dependent variables) that occur in one of two possible states, often labelled zero
and one. E.g., “improved/not improved” and “completed task/failed to complete task
4: Categorical Variable:
 Usually an independent or predictor variable that contains values indicating membership in one
of several possible categories.
 E.g., gender (male or female), marital status (married, single, divorced, widowed).
5: Confounding variable:
 A variable that obscures the effects of another variable
6: Continuous variable: (Quantitative variables)
 A variable that is not restricted to particular values (other than limited by the accuracy of the
measuring instrument). E.g., reaction time, neuroticism, IQ.
 Interval variable Synonym for continuous variable
7: Control variable:
 Variable that an investigator does not wish to examine in a study. Thus the investigator controls this
variable. Also called a covariate.
8: Criterion variable:
 - The presumed effect in a no experimental study.
9: Dependent variable:
 The presumed effect in an experimental study. The values of the dependent variable depend upon
another variable
10: Independent variable:
 . Strictly speaking, “dependent variable” should not be used when writing about
 Non experimental designs.
 . The values of the independent variable are under experimenter control.
11: Dichotomous variable:
 Synonym for binary variable.
12: Dummy Variables:
 Created by recoding categorical variables that have more than two categories into a series of binary
 Example: var_1: 1=single, 0=otherwise. Var_2: 1=divorced, widowed, or separated, 0=otherwise.
 Dummy variables are used in regression analysis to avoid the unreasonable assumption.
13: Endogenous variable:
 A variable that is an inherent part of the system being studied and that is determined from within
the system.
 A variable that is caused by other variables in a causal system
14: Exogenous variable:
 A variable entering from and determined from outside of the system being studied. A causal system
says nothing about its exogenous variables.
15: Intervening variable:
 A variable that explains a relation or provides a causal link between other variables
 Also called by some authors “mediating variable” or “intermediary variable
16: Latent variable:
 An underlying variable that cannot be observed. It is hypothesized to exist in order to explain other
variables, such as specific behaviors, that can be observed.
17: Nominal variables: (: (Qualitative)
 classification or categorization based on some distinctively different characteristic, but we
cannot rank order those categories.
 Typical examples of nominal variables are sex, religion, blood group, symptoms of disease,
cause of death etc.
18: Ordinal variables: (Qualitative)
 A variable used to rank a sample of individuals with respect to some characteristics, but differences
(i.e., intervals) and different points of the scale are not necessarily equivalent
 Ordinal variables allow us to rank order the categories in terms of which category has less
and which category has more of the quality represented by the variable
19: Qualitative variables:
 Qualitative (categorical) variables are those characteristics which are not numerically
 These variables are either nominal (no natural ordering) or ordinal (ordered categories).
Usually, for the purpose of data entry and analysis using software, categories are coded
assigning numerical values.
20: Quantitative variables
 Quantitative variables are those characteristics which can be a count or measured numerically. They
can be continuous or discrete.
 Quantification is the process of assigning numeral value to the extend or amount of a variable of an
 The quantification is done by employing the process of measurement. This process yields data and
 There are four basic ways of quantifying the variables. They are also called levels of measurement
or scales of measurement ( nominal, ordinal , equal interval , ratio scale )
 The nominal scale is the least precise or crude of the four basic scales of measurement.
 It simply implies the classification of an item into two or more categories without any extent or
 The frequency or numbers are used to give a name to something that may be used for determining
per cent, mode. For example boys and girls; pass and fail; rural and urban.
b. Ordinal Scale:
 The ordinal scale is more precise scale than the nominal scale. It allows the teacher to assign values
by placing of arranging the observations in relative rank order
 This scale is used frequently in the schools for prize distribution.
Equal Interval Scale:
 The equal interval scale is more precise and refined scale than nominal and ordinal scales.
 This scale has all the characteristics and relationship of the ordinal scale, besides which distances
between any two numbers on the scale are known
 has the greater use in teaching-learning situation, educational administration, educational
guidance and counselling and educational research.
d. Ratio Scale:
 Ratio scale has the properties of equal-interval scale plus two additional characteristics:
 This scale has a true, rather than arbitrary ‘zero, numerals have the qualities of real numbers
The variable is quantified by using an instrument. The quantified variable is termed as variate
. When sample subjects I.Q.s or scores of achievement are collected, it is known as variate.
The statistical analysis involves variant analysis: uni-variate, bi-variant, multi-variant analysis.
 On the basis of measurement
 ( nominal, ordinal data )
 ( Qualitative , quantitative data )
 Presentation of data:
 Qualitative data: (nominal or ordinal variable) may be presented in the form of frequency
 We count the number of subjects/units in each category of the variable along with
 Nominal data and ordinal data : with limited number of categories can also be
presented in a diagrammatic form, such as a bar chart and pie chart.
 Quantitative data: can be represented graphically by means of a histogram.
 Analysis of data:
 Type of the variables decides the type of statistical analyses to be performed, parametric or
 Parametric methods,: such as t-tests, ANOVA, Pearson’s correlation, and regression,
 require the assumption that the data follow a normal distribution and that variances of the
distributions are equal
 Nonparametric methods: are Mann-Whitney or Wilcoxon rank sum test, Wilcoxon signed
rank test and rank correlation.
 Non-parametric methods, make no assumptions about the distribution of the data;
they use the rank order of observations rather than actual measurements.
 define a population of interest, including its spatial and temporal boundaries, and choose an
appropriate type and size of sampling unit.
24. Types of sampling:
Simple random sampling:
 Where all the possible sampling units in a population have an equal chance of being selected in a
 These designs are used to estimate variation at a series of hierarchical (or nested) levels
 e.g. quadrats or plots
Haphazard sampling:
 Where sampling units are chosen in a less formal manner.
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Stratified sampling:
 where the population is divided into levels or strata that represent clearly defined groups of
units within the population and sampling is done independently
 Estimating population means and variances from stratified sampling requires modification of the
formulae provided for simple random sampling.
 Formula : (h1 to l strata, sh2 is the sample variance for stratum h )
Cluster sampling:
 uses heterogeneity in the population to modify the basic random sampling design
 we can identify primary sampling units (clusters) in a population, e.g. individual trees. For each
primary unit (tree)
Simple cluster sampling.
 Where we record all secondary units within each primary unit.
 Record all secondary units, e.g. branches on each tree.
Systematic sampling:
 where we choose sampling units that are equally spaced, either spatially or temporally.
 For example, we might choose plots along a transect at 5 m intervals or we might choose weekly
sampling dates.
 Systematic sampling is sometimes used when we wish to describe an environmental gradient
Adaptive sampling:
 When a sampling program has a temporal component, which is often the case in biology, especially
when sampling ecological phenomena or environmental impacts
1: Chance Error:
 This error is due to the individual differences, e.g.,
 if we are studying the superiority of programmed method over traditional method, then the
differences in intelligence, learning ability, socio-economic status.
(a) Sampling Error:
 The error is due to the differences within the sample chosen for study is known as sampling
error, e.g., in the above study habit of study, intelligence etc.
B: Measurement Error: It is due to inability of measuring instruments to produce accurate
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2: Systematic Error:
 Systematic error causes bias in study
 Famous source of systematic error in research studies is the “Hawthorne effect.
26. Experimental design:
 A framework for adequate tests of the relations among variables.
 Design tells us in a sense “what observations to make”, “how to make them”, and how to
analyze the quantitative
 Design does not tell us precisely what to do.
 A design suggests which variables are active and which are assigned
 A design also suggests, what type of statistical analysis to use.
1. Randomization
 The principle of randomization, as advocated by Fisher, is essential for a valid estimate of the
experimental error and also to minimize bias in results
 Randomization is a device to achieve this independence of variances, independence of errors.
 Randomization controls the sampling error or “S” error
2. Replication:
 A treatment is repeated a number of times in order to obtain a more reliable estimate than is
possible from a single observation
 along with randomization, it provides an estimate of the error
 along with local control it reduces the experimental error
 to increase the precision of an experiment is to repeat the experiment
 Measurement error is controlled by replication
 Replication with randomization would be able to control all the three errors and with local control,
the “G” error and
 Measurement error.
 So replication is a very important principle.
3. Local Control:
 Also called local or error control.
 Local control helps in controlling the systematic error or general factors or say “G” erroR