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ҫlwV'GtFc/\֩,gՂL#a~d0& -& & Inductive reasoning:2&0$Repeating the experiments essentially under the same conditions and
keenly observing the outcome each time and
relating them to derive a fact is the system followed in inductive reasoning in scienceZ&$Deductive Reasoning:*&0$f Pure Mathematics is an example of formal science , or deductive reasoning
where the conclusions are derived on the basis of existing facts, definitions, theorems, and axioms. 8Z&(&$The Principles and decision-making *%#&(&If inductive reasoning helps us in developing the principles that can be generalized,
the deductive reasoning guides us in generalized decision-making.
8Z&(&Measurement Scales"$8Nominal scale
Ordinal scale
Interval scale
Ratio scale99&$ Error and Biash&&(&&g0No experimentation or observation can be totally free from errors and escape from bias.
But we must identify and recognize them for their elimination as for as possible or to control and minimize the effectZ&$&$&$&$&$&$&$Measurements even being valid, if lack in precision and accuracy, irrespective of the magnitude or quantity of deviation from the intended measurement, are called errors. - One sided repeated errors or systematic errors are called bias. n-d&&0A&&0&
Selection or allocation biases, - Measurement bias,- Instrument bias, - Inter & intra investigator or - Observer s bias, - Misclassification bias etc. are some of the frequently encountered bias P-n&.&&,We know that the techniques of blinding, randomization, replication, standardization, selection of controls and to a great extent the experimental designs do help us to overcome some of them.i&& (& & '&& && & & #&& & *#variable g6A variable takes on or can assume various values
But the same quantity may be a constant in some situation and a variable in another @$$|$+$Classification6The variables may broadly be classified in a number of ways such as,
continuous & discrete,
qualitative & quantitative,
random & non-random etc. NEOF$H&$$,%%terminologies and role of variables D&&0&&0WVarious models use different terminologies to explain the role and status of variables &XV&,,-&$terminologies and role of variables:%&0&&0For example in epidemiology we use the terms independent, dependent and intervening variables ; or
parallel to that cause, effect and confounding / interacting variables ;
in certain situations the same are called input, process and output variables ; J-& & &.'$terminologies and role of variables:%&0&&0In forecasting the nomenclature preferred is predicting, predicted and disturbing variables ;
in laboratory situations we pronounce them as experimental, outcome and chance / random variables and so on. $& $/& $&$$4&
$$/(Changing role of Variablesb A dependent or outcome variable can serve as an independent or input variable in another process 4c$`&$$0)Changing role of Variables$Researchers do experience hundreds of other terms used invariably to explain very specific role assigned to a variable in a particular situation, such as,
pseudo variable, or dummy, proxy, nuisance, substitute, culprit, treatment, response, extraneous, manipulated and complex variables etc @%R$$$1* Clarity in knowing the variables!!&,The clarity in knowing the variables of interest to be considered in a particular study helps a lot in
recruitment of research tools, techniques and methods to be used during experimentation and
use of statistical tests at the end of the study. T$$$$ Experimental Designs The purpose of an experimental design is to enhance the power of inference making by either; - eliminating undesired independent variables from the site of experiment or minimizing their effect during the experimentation, and - also to allow the desired independent (or experimental) variables to their full exploitation for manipulations by the research investigator &g(&&p&g2+Experimental Designs g(SExperimental designs also help in sequencing the deployment of experimental tools, techniques and methods.
completely randomized and randomized block designs are a few examples.
Clinical trials with or without randomization and blinding, self-controlled and without control or crossover designs are frequently used in clinical settings. `TZl$$$$$!The Sample and Sampling:^$&6$&6$A study of entire population is impossible in most of the situations.
Sometimes, the study process destroys (animal sacrifice) or depletes the item being studied.
In such situations the only alternative is sample study. Z&$
Advantages,Vsample results are often more accurate, apart from being
quick and
less expensive zW&,&,&,&,&,&," If samples are properly selected, probability methods can be used to estimate the error in the resulting statistics.
It is this aspect of sampling that permits investigators to make probability statements about the observations in a study 8Z&&$' Sample size and sampling error d &,&&,&,&,The sample size has to be directly proportional to the heterogeneity in the population,
whereas, the sampling error is always inversely proportional to it. &$&$(&$
&$"&$&$)&$$(!Probability sampling&0The techniques of sampling may be classified as
Probability sampling such as;
- Simple random sampling,
- Stratified, cluster, systematic,
- Multi-stage and multi-phase sampling; and
fQZsZZ&$&$&$)"Non-Probability sampling&0such as;
Convenience sampling,
Inverse or quota sampling,
Judgment and purposive sampling etc.
But non-probability sampling findings are usually not qualified for any generalizations as they lack to be representative of the entire population.
R
ZZZ$$%Power of a study&6kIt is not only the sample-size
but also the sampling method equally responsible for
the power of a study.
l&(&(
&(&(&(&(&(&(#To summarize
g0Kbigger does not always mean better or
more powerful in making inferences. :L"&,'&,$$qFor this reason, investigators must plan the sample size appropriate for their study prior to beginning research &rp&,,x{l{wv<fm֬mĘ5/Cv06l PSv!Ҕ(RR5ȣU ʫR_AP5LDj:(胈(eJJ{ov^w3c{8xmV*jD6ER&mphh4kh\S,kVõ_E|Sҧq3)^qZ.]#7֫-HEr`2]9zD-cv%_:L&sr~&+
*X,V)w:sx6/49,X/lZu{Q,{Kı1nJ?^JΪr~uG%EXgq͖5'XzGf'jTEh"^Xj\oH!ϊ3
Dx
hhivֵ*mqZY> Jq^y[QЇ-~N1
ҫlwV'GtFc/\֩,gՂL#a~d0& -& & Inductive reasoning:2&0$Repeating the experiments essentially under the same conditions and
keenly observing the outcome each time and
relating them to derive a fact is the system followed in inductive reasoning in scienceZ&$Deductive Reasoning:*&0$f Pure Mathematics is an example of formal science , or deductive reasoning
where the conclusions are derived on the basis of existing facts, definitions, theorems, and axioms. 8Z&(&$The Principles and decision-making *%#&(&If inductive reasoning helps us in developing the principles that can be generalized,
the deductive reasoning guides us in generalized decision-making.
8Z&(&Measurement Scales"$8Nominal scale
Ordinal scale
Interval scale
Ratio scale99&$ Error and Biash&&(&&g0No experimentation or observation can be totally free from errors and escape from bias.
But we must identify and recognize them for their elimination as for as possible or to control and minimize the effectZ&$&$&$&$&$&$&$Measurements even being valid, if lack in precision and accuracy, irrespective of the magnitude or quantity of deviation from the intended measurement, are called errors. - One sided repeated errors or systematic errors are called bias. n-d&&0A&&0&
Selection or allocation biases, - Measurement bias,- Instrument bias, - Inter & intra investigator or - Observer s bias, - Misclassification bias etc. are some of the frequently encountered bias P-n&.&&,We know that the techniques of blinding, randomization, replication, standardization, selection of controls and to a great extent the experimental designs do help us to overcome some of them.i&& (& & '&& && & & #&& & *#variable g6A variable takes on or can assume various values
But the same quantity may be a constant in some situation and a variable in another @$$|$+$Classification6The variables may broadly be classified in a number of ways such as,
continuous & discrete,
qualitative & quantitative,
random & non-random etc. NEOF$H&$$,%%terminologies and role of variables D&&0&&0WVarious models use different terminologies to explain the role and status of variables &XV&,,-&$terminologies and role of variables:%&0&&0For example in epidemiology we use the terms independent, dependent and intervening variables ; or
parallel to that cause, effect and confounding / interacting variables ;
in certain situations the same are called input, process and output variables ; J-& & &.'$terminologies and role of variables:%&0&&0In forecasting the nomenclature preferred is predicting, predicted and disturbing variables ;
in laboratory situations we pronounce them as experimental, outcome and chance / random variables and so on. $& $/& $&$$4&
$$/(Changing role of Variablesb A dependent or outcome variable can serve as an independent or input variable in another process 4c$`&$$0)Changing role of Variables$Researchers do experience hundreds of other terms used invariably to explain very specific role assigned to a variable in a particular situation, such as,
pseudo variable, or dummy, proxy, nuisance, substitute, culprit, treatment, response, extraneous, manipulated and complex variables etc @%R$$$1* Clarity in knowing the variables!!&,The clarity in knowing the variables of interest to be considered in a particular study helps a lot in
recruitment of research tools, techniques and methods to be used during experimentation and
use of statistical tests at the end of the study. T$$$$ Experimental Designs The purpose of an experimental design is to enhance the power of inference making by either; - eliminating undesired independent variables from the site of experiment or minimizing their effect during the experimentation, and - also to allow the desired independent (or experimental) variables to their full exploitation for manipulations by the research investigator &g(&&p&g2+Experimental Designs g(SExperimental designs also help in sequencing the deployment of experimental tools, techniques and methods.
completely randomized and randomized block designs are a few examples.
Clinical trials with or without randomization and blinding, self-controlled and without control or crossover designs are frequently used in clinical settings. `TZl$$$$$!The Sample and Sampling:^$&6$&6$A study of entire population is impossible in most of the situations.
Sometimes, the study process destroys (animal sacrifice) or depletes the item being studied.
In such situations the only alternative is sample study. Z&$
Advantages,Vsample results are often more accurate, apart from being
quick and
less expensive zW&,&,&,&,&,&," If samples are properly selected, probability methods can be used to estimate the error in the resulting statistics.
It is this aspect of sampling that permits investigators to make probability statements about the observations in a study 8Z&&$' Sample size and sampling error d &,&&,&,&,The sample size has to be directly proportional to the heterogeneity in the population,
whereas, the sampling error is always inversely proportional to it. &$&$(&$
&$"&$&$)&$$(!Probability sampling&0The techniques of sampling may be classified as
Probability sampling such as;
- Simple random sampling,
- Stratified, cluster, systematic,
- Multi-stage and multi-phase sampling; and
fQZsZZ&$&$&$)"Non-Probability sampling&0such as;
Convenience sampling,
Inverse or quota sampling,
Judgment and purposive sampling etc.
But non-probability sampling findings are usually not qualified for any generalizations as they lack to be representative of the entire population.
R
ZZZ$$%Power of a study&6kIt is not only the sample-size
but also the sampling method equally responsible for
the power of a study.
l&(&(
&(&(&(&(&(&(#To summarize
g0Kbigger does not always mean better or
more powerful in making inferences. :L"&,'&,$$qFor this reason, investigators must plan the sample size appropriate for their study prior to beginning research &rp&,,x{l{wv<fm֬mĘ5/Cv06l PSv!Ҕ(RR5ȣU ʫR_AP5LDj:(胈(eJJ{ov^w3c{8xmV*jD6ER&mphh4kh\S,kVõ_E|Sҧq3)^qZ.]#7֫-HEr`2]9zD-cv%_:L&sr~&+
*X,V)w:sx6/49,X/lZu{Q,{Kı1nJ?^JΪr~uG%EXgq͖5'XzGf'jTEh"^Xj\oH!ϊ3
Dx
hhivֵ*mqZY> Jq^y[QЇ-~N1
ҫlwV'GtFc/\֩,gՂL#a~d0& -& & Inductive reasoning:2&0$Repeating the experiments essentially under the same conditions and
keenly observing the outcome each time and
relating them to derive a fact is the system followed in inductive reasoning in scienceZ&$Deductive Reasoning:*&0$f Pure Mathematics is an example of formal science , or deductive reasoning
where the conclusions are derived on the basis of existing facts, definitions, theorems, and axioms. 8Z&(&$The Principles and decision-making *%#&(&If inductive reasoning helps us in developing the principles that can be generalized,
the deductive reasoning guides us in generalized decision-making.
8Z&(&Measurement Scales"$8Nominal scale
Ordinal scale
Interval scale
Ratio scale99&$ Error and Biash&&(&&g0No experimentation or observation can be totally free from errors and escape from bias.
But we must identify and recognize them for their elimination as for as possible or to control and minimize the effectZ&$&$&$&$&$&$&$Measurements even being valid, if lack in precision and accuracy, irrespective of the magnitude or quantity of deviation from the intended measurement, are called errors. - One sided repeated errors or systematic errors are called bias. n-d&&0A&&0&
Selection or allocation biases, - Measurement bias,- Instrument bias, - Inter & intra investigator or - Observer s bias, - Misclassification bias etc. are some of the frequently encountered bias P-n&.&&,We know that the techniques of blinding, randomization, replication, standardization, selection of controls and to a great extent the experimental designs do help us to overcome some of them.i&& (& & '&& && & & #&& & *#variable g6A variable takes on or can assume various values
But the same quantity may be a constant in some situation and a variable in another @$$|$+$Classification6The variables may broadly be classified in a number of ways such as,
continuous & discrete,
qualitative & quantitative,
random & non-random etc. NEOF$H&$$,%%terminologies and role of variables D&&0&&0WVarious models use different terminologies to explain the role and status of variables &XV&,,-&$terminologies and role of variables:%&0&&0For example in epidemiology we use the terms independent, dependent and intervening variables ; or
parallel to that cause, effect and confounding / interacting variables ;
in certain situations the same are called input, process and output variables ; J-& & &.'$terminologies and role of variables:%&0&&0In forecasting the nomenclature preferred is predicting, predicted and disturbing variables ;
in laboratory situations we pronounce them as experimental, outcome and chance / random variables and so on. $& $/& $&$$4&
$$/(Changing role of Variablesb A dependent or outcome variable can serve as an independent or input variable in another process 4c$`&$$0)Changing role of Variables$Researchers do experience hundreds of other terms used invariably to explain very specific role assigned to a variable in a particular situation, such as,
pseudo variable, or dummy, proxy, nuisance, substitute, culprit, treatment, response, extraneous, manipulated and complex variables etc @%R$$$1* Clarity in knowing the variables!!&,The clarity in knowing the variables of interest to be considered in a particular study helps a lot in
recruitment of research tools, techniques and methods to be used during experimentation and
use of statistical tests at the end of the study. T$$$$ Experimental Designs The purpose of an experimental design is to enhance the power of inference making by either; - eliminating undesired independent variables from the site of experiment or minimizing their effect during the experimentation, and - also to allow the desired independent (or experimental) variables to their full exploitation for manipulations by the research investigator &g(&&p&g2+Experimental Designs g(SExperimental designs also help in sequencing the deployment of experimental tools, techniques and methods.
completely randomized and randomized block designs are a few examples.
Clinical trials with or without randomization and blinding, self-controlled and without control or crossover designs are frequently used in clinical settings. `TZl$$$$$!The Sample and Sampling:^$&6$&6$A study of entire population is impossible in most of the situations.
Sometimes, the study process destroys (animal sacrifice) or depletes the item being studied.
In such situations the only alternative is sample study. Z&$
Advantages,Vsample results are often more accurate, apart from being
quick and
less expensive zW&,&,&,&,&,&," If samples are properly selected, probability methods can be used to estimate the error in the resulting statistics.
It is this aspect of sampling that permits investigators to make probability statements about the observations in a study 8Z&&$' Sample size and sampling error d &,&&,&,&,The sample size has to be directly proportional to the heterogeneity in the population,
whereas, the sampling error is always inversely proportional to it. &$&$(&$
&$"&$&$)&$$(!Probability sampling&0The techniques of sampling may be classified as
Probability sampling such as;
- Simple random sampling,
- Stratified, cluster, systematic,
- Multi-stage and multi-phase sampling; and
fQZsZZ&$&$&$)"Non-Probability sampling&0such as;
Convenience sampling,
Inverse or quota sampling,
Judgment and purposive sampling etc.
But non-probability sampling findings are usually not qualified for any generalizations as they lack to be representative of the entire population.
R
ZZZ$$%Power of a study&6kIt is not only the sample-size
but also the sampling method equally responsible for
the power of a study.
l&(&(
&(&(&(&(&(&(#To summarize
g0Kbigger does not always mean better or
more powerful in making inferences. :L"&,'&,$$qFor this reason, investigators must plan the sample size appropriate for their study prior to beginning research &rp&,,x{l{wv<fm֬mĘ5/Cv06l PSv!Ҕ(RR5ȣU ʫR_AP5LDj:(胈(eJJ{ov^w3c{8xmV*jD6ER&mphh4kh\S,kVõ_E|Sҧq3)^qZ.]#7֫-HEr`2]9zD-cv%_:L&sr~&+
*X,V)w:sx6/49,X/lZu{Q,{Kı1nJ?^JΪr~uG%EXgq͖5'XzGf'jTEh"^Xj\oH!ϊ3
Dx
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