2.RESEARCH DESIGN
#Explanatory Research DESIGN
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
Explanatory research is a systematic approach to understanding events, behaviors, or situations. It involves identifying a problem or issue and then collecting data in an effort to explain why it exists.
#Explanatory research can be used to identify the causes of problems or to understand the factors that influence behavior. In many cases, explanatory research is used to develop hypotheses that can be tested through experimentation.
#Explanatory Research Data Collection Methods
Common data collection methods in Explanatory Research are:
-Literature reviews
-Interviews
-Focus groups
-Pilot studies
-Observations
-Experiments
1.Literature reviews
A literature review is a critical summary of what the scientific literature says about your specific topic or question. It is a written overview of the current state of research on a given topic, and it usually appears as part of a larger research project, such as a dissertation.
A literature review has three main purposes:
1. To survey the current state of knowledge on a topic
2. To identify gaps in the existing research
3. To provide context for a new research project
2.Interviews
In explanatory research, interviews are used to gather information from individuals about their experiences, opinions, and behaviors. This type of research is typically used to understand why people do what they do and how they think about certain issues.
3.Focus groups
Focus groups are an important tool in the explanatory research process. search, Focus groups help researchers understand people’s opinions and attitudes on a particular issue. They also provide insights into how people think and feel about certain topics.
Focus groups are usually small, with 6-10 participants. This allows for a more intimate setting where people can feel comfortable sharing their thoughts and opinions.
3.Pilot studies
Pilot studies are small-scale, preliminary research investigations. They are conducted to explore the feasibility of a larger study and to gather preliminary data. Pilot studies in explanatory research help researchers to refine their hypothesis and research design.
4.Observations
In explanatory research, observations are made in order to get a clear understanding of a phenomenon.
This type of research is often used in the sciences, as it allows for the collection of data that can be used to explain a certain event or natural occurrence.
There are two main types of observations in Explanatory Research:
Qualitative
Quantitative
፨፨፨፨፨፨፨፨፨፨፨፨፨፨
Qualitative observations are those that are made without the use of numbers or measurements. They are often more subjective and can be more difficult to analyze.
Quantitative observations are those that involve some form of measurement. These types of observations are often easier to analyze, but can sometimes be less accurate than qualitative ones.
Experiments(መኩራ)
Experiments in explanatory research are designed to provide information about causal relationships. These studies test hypotheses about how certain independent variables affect dependent variables.
Explanatory Research Data Analysis Methods
There are many methods of data analysis used in explanatory research. Some common methods are:
Regression Analysis
Chi-Square Test
T-Test
ANOVA
Regression Analysis
Regression analysis is a method of data analysis that is used to predict the relationship between two or more variables. This method is used to determine the strength of the relationship between the variables and to identify any trends.
Chi-Square Test
The chi-square test is a statistical test that is used to determine if there is a significant difference between two or more groups. This test is used to compare categorical data.
T-Test
The T-test is a statistical test that is used to compare means between two groups. This test can be used to compare data that are not normally distributed.
#Explanatory Research DESIGN
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
Explanatory research is a systematic approach to understanding events, behaviors, or situations. It involves identifying a problem or issue and then collecting data in an effort to explain why it exists.
#Explanatory research can be used to identify the causes of problems or to understand the factors that influence behavior. In many cases, explanatory research is used to develop hypotheses that can be tested through experimentation.
#Explanatory Research Data Collection Methods
Common data collection methods in Explanatory Research are:
-Literature reviews
-Interviews
-Focus groups
-Pilot studies
-Observations
-Experiments
1.Literature reviews
A literature review is a critical summary of what the scientific literature says about your specific topic or question. It is a written overview of the current state of research on a given topic, and it usually appears as part of a larger research project, such as a dissertation.
A literature review has three main purposes:
1. To survey the current state of knowledge on a topic
2. To identify gaps in the existing research
3. To provide context for a new research project
2.Interviews
In explanatory research, interviews are used to gather information from individuals about their experiences, opinions, and behaviors. This type of research is typically used to understand why people do what they do and how they think about certain issues.
3.Focus groups
Focus groups are an important tool in the explanatory research process. search, Focus groups help researchers understand people’s opinions and attitudes on a particular issue. They also provide insights into how people think and feel about certain topics.
Focus groups are usually small, with 6-10 participants. This allows for a more intimate setting where people can feel comfortable sharing their thoughts and opinions.
3.Pilot studies
Pilot studies are small-scale, preliminary research investigations. They are conducted to explore the feasibility of a larger study and to gather preliminary data. Pilot studies in explanatory research help researchers to refine their hypothesis and research design.
4.Observations
In explanatory research, observations are made in order to get a clear understanding of a phenomenon.
This type of research is often used in the sciences, as it allows for the collection of data that can be used to explain a certain event or natural occurrence.
There are two main types of observations in Explanatory Research:
Qualitative
Quantitative
፨፨፨፨፨፨፨፨፨፨፨፨፨፨
Qualitative observations are those that are made without the use of numbers or measurements. They are often more subjective and can be more difficult to analyze.
Quantitative observations are those that involve some form of measurement. These types of observations are often easier to analyze, but can sometimes be less accurate than qualitative ones.
Experiments(መኩራ)
Experiments in explanatory research are designed to provide information about causal relationships. These studies test hypotheses about how certain independent variables affect dependent variables.
Explanatory Research Data Analysis Methods
There are many methods of data analysis used in explanatory research. Some common methods are:
Regression Analysis
Chi-Square Test
T-Test
ANOVA
Regression Analysis
Regression analysis is a method of data analysis that is used to predict the relationship between two or more variables. This method is used to determine the strength of the relationship between the variables and to identify any trends.
Chi-Square Test
The chi-square test is a statistical test that is used to determine if there is a significant difference between two or more groups. This test is used to compare categorical data.
T-Test
The T-test is a statistical test that is used to compare means between two groups. This test can be used to compare data that are not normally distributed.
Research Method
One-to-One Interview - Methods and Guide - Research Method
One-to-one interviews are an important part of qualitative research. They allow researchers to collect detailed information from participants.
ANOVA
ANOVA is a statistical test that is used to compare means between three or more groups. This test can be used to compare data that are not normally distributed.
Example of Explanatory Research
One example of explanatory research is a study that was conducted to understand the reasons behind the increasing number of car accidents in the United States. The study looked at various factors such as the number of cars on the road, the number of miles driven, speed limits, and weather conditions. The findings showed that the increase in car accidents was due to a combination of factors, including more cars on the road and higher speeds.
Another example of explanatory research is a study that was conducted to understand why people are more likely to get divorced nowadays.
When to use Explanatory Research
Explanatory research is used to answer specific questions about how or why something occurs. This type of research is explanatory in nature because it seeks to provide a clear and concise explanation for a particular event or phenomenon. Explanatory research is often used in the social sciences, where researchers are interested in understanding the causes and effects of social behavior.
There are several factors that can influence when explanatory research is most appropriate.
One key consideration is the availability of data. Explanatory research typically relies on existing data, so if there is limited data available on a particular topic, then other research methods may be more appropriate.
It is often most useful when there is a clearly defined question that can be addressed with empirical evidence.
When investigating complex phenomena or relationships, explanatory research may be less effective than other methods such as observational research or qualitative analysis.
ገላጭ ዳሰሳዊ የጥናት ውጥን ዘዴ
ANOVA is a statistical test that is used to compare means between three or more groups. This test can be used to compare data that are not normally distributed.
Example of Explanatory Research
One example of explanatory research is a study that was conducted to understand the reasons behind the increasing number of car accidents in the United States. The study looked at various factors such as the number of cars on the road, the number of miles driven, speed limits, and weather conditions. The findings showed that the increase in car accidents was due to a combination of factors, including more cars on the road and higher speeds.
Another example of explanatory research is a study that was conducted to understand why people are more likely to get divorced nowadays.
When to use Explanatory Research
Explanatory research is used to answer specific questions about how or why something occurs. This type of research is explanatory in nature because it seeks to provide a clear and concise explanation for a particular event or phenomenon. Explanatory research is often used in the social sciences, where researchers are interested in understanding the causes and effects of social behavior.
There are several factors that can influence when explanatory research is most appropriate.
One key consideration is the availability of data. Explanatory research typically relies on existing data, so if there is limited data available on a particular topic, then other research methods may be more appropriate.
It is often most useful when there is a clearly defined question that can be addressed with empirical evidence.
When investigating complex phenomena or relationships, explanatory research may be less effective than other methods such as observational research or qualitative analysis.
ገላጭ ዳሰሳዊ የጥናት ውጥን ዘዴ
Research Method
Qualitative Research - Methods, Analysis Types and Guide
Qualitative research is a method of inquiry that focuses on understanding the meanings that people attach to their experiences.
Forwarded from SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy)) (abyfamilies123)
☸☸☸☸☸References♓️♓️♓️
Fench , D. (2022). Discovering statistics using IBM SPSS statistics (5th edition). Sage: Thousand Oaks, CA.
Pallant, J. (2010). SPSS survival manual: A step-by-step guide to data analysis using SPSS. Maidenhead: Open University Press/McGraw-Hill.
Pituch, K.A. and Stevens, J.P. (2016) Applied Multivariate Statistics for the Social Sciences.
Call#0970461746
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
https://t.me/mamaker
Fench , D. (2022). Discovering statistics using IBM SPSS statistics (5th edition). Sage: Thousand Oaks, CA.
Pallant, J. (2010). SPSS survival manual: A step-by-step guide to data analysis using SPSS. Maidenhead: Open University Press/McGraw-Hill.
Pituch, K.A. and Stevens, J.P. (2016) Applied Multivariate Statistics for the Social Sciences.
Call#0970461746
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
https://t.me/mamaker
Telegram
SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy))
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
SAMPLING (ናሙና)
#ናሙና በሁለት ዋና ዋና ዘርፎች ሊከፈል ይችላል፡፡ እነርሱም፡-
1. ግምት ሰጭ (Probability sampling)
2. ግምት የማይሰጥ (Non-probability sampling) #100kgchallenge ግምት ሰጭ (Probability samplijng)
ግምት ሰጭ ናሙና ከአጠቃላይ ስብስብ ውስጥ ለናሙናነት ለሚመረጡ ሰዎች ወይም ሁኔታዎች እኩል የመመረጥ እድል የሚሰጥ የናሙና አመራረጥ ዘርፍ ነው፡፡ በዚህ ዘርፍ ሥር አራት የናሙና አይነት ይገኛሉ፡፡ እነርሱም፡-
1. ነሲብ ናሙና (Simple Random Sampling)
2. በሥርዓት የተዘጋጀ ናሙና (Systematic Sampling)
3. የተከፋፈለ ናሙና (Stratified Sampling)
4. ጥምር ናሙና (Cluster Sampling)
1.2. ነሲብ ናሙና (Simple Random) Sampling)
ነሲብ ናሙና ለእያንዳንዱ በጥናት የመታቀፍ ባሕርይ ላለው ሰው ወይም ሁኔታ እኩል የመመረጥ ዕድል የሚሰጥ የናሙና ዓይነት ነው፡፡ ለጥናቱ የሚመረጡ ሰዎች ወይም ሁኔታዎች በአንድ አጠቃላይ ስብስብ ውስጥ እስከሆኑ ድረስ ተመሳሳይ ወይም የማይመሳሰሉ ቢሆኑም የመመረጥ እድላቸው ተመጣጣኝ መሆን ይኖርበታል፡፡
1.3. በሥርዓት የተደራጀ ናሙና (Systematic Sampling)
ይህ ዓይነቱ ናሙና የተወሰነ የአመራረጥ ስልትን ተከትሎ የሚካሄድ ነው፡፡ ለምሳሌ ሊመረጡ የሚችሉ የሚገመቱ 1ዐዐ ተማሪዎች ቢኖሩና ከመካከላቸው 2ዐ በናሙናነት የሚወሰዱ ከሆነ ያሉት ተማሪዎች በሙሉ /1ዐዐ/ ዝርዝራቸው ተጽፎ ከአምስት አንድ ብቻ ተለቅመው ሊወሰዱ ይችላሉ፡፡ በዚህ አሠራር 5ኛ፣ 1ዐኛ፣ 15ኛ፣ ….1ዐዐኛ ድረስ ያሉት 2ዐ ተማሪዎች ይመረጣ ማለት ነው፡፡
#የተከፋፈለ ናሙና (Stratified Sampling)
ይህ አይነቱ ናሙና ከላይ በ3.6.1.2.የተጠቀሰውን የናሙና አይነት በተሻለ መንገድ ለመግለጽ ወይም ለመጠቀም የቀረበ ነው፡፡ አጥኝው ይህንን የናሙና አወሳሰድ ዘዴ ለመጠቀም በሚያሰብብበት ወቅት የተጠኚ ኩረት ናሙናውን ያላቸውን ባሕሪያት በመጠቀም በምድብ በምድብ በመከፋፈል የተወሰነ ወካይ ቁጥር ከየምድቡ የሚወስድበት ዘዴ ነው፡፡
ለምሳሌ #ተማሪዎችን በትምህርት ውጤታቸው ከፍተኛ፣ መካከለኛ እና ዝቅተኛ ውጤት በማለትና በመከፋፈል ከእያንዳንዱ ምድብ ናሙና በመውሰድ መረጃውን የተሟላ ለማድረግ ወይም በነዚህ ባሕሪያት መካከል ያለውን ልዩነት ወይም ግንኙነት ለማጥናት ይጠቀምበታል፡፡
#ይህንን እድል ሰጭ የናሙና አይነት እንደገና በቦስት ከፋፍሎ ማየት ይቻላል፡፡ የመጀመሪያው ተመጣጣኝ ያልሆነ የተከፋፈለ ናሙና (Disproportionate Stratified sampling) በሚል የሚታወቅ ሲሆን ከእያንዳንዱ ምድብ የሚወሰደው ናሙና በየምድቡ ካለው የናሙና ቁጥር (ብዛት) ሳይሆን በአጥኝ የግል ውሳኔ እንደየሁኔታው ናሙና የሚወሰድበትና ብዙ ጊዜ ደካማ የናሙና አወሳሰድ የሚባል ነው፡፡ ምክንያቱም መረጃውን ሊያዛባው ስለሚችል ነው፡፡ ሁለተኛው ደግሞ ተመጣጣኝ የሆነ የተከፋፈለ ናሙና (Proportionate Stratified Sampling) የሚባለው የተከፋፈለ ናሙና አይነት ነው፡፡ ይህ አይነቱ ናሙና አወሳሰድ ለሁሉም ምድቦች እኩል እድል የሚሰጥ ሲሆን ሦስተኛውና የመጨረሻው ሁለንተናዊ ተመጣጣኝ የተከፋፈለ ናሙና (Optimum
Allocation Stratified Sampling) የሚባለው ሲሆን ጎናዊና ወርዳዊ ሁኔታዎችን በመፈተሽ የናሙናውን ውክልና ሁለንተናዊ ምጥጥን እንዲኖረው በማድረግ መረጃው በሚሰበሰብበት ወቅት አስተማማኝነቱን ለማጠንከር የሚያስችልና የተሳለው የናሙና ዓይነት ነው ለማለት
ይቻላል፡፡
🧲 #ጥምር ናሙና (Cluster Sampling)
ይህ ግምት ሰጭ የናሙና አይነት በናሙናዎች ቁጥር ላይ ሳይሆን አጠቃላይ ቡድንን በመምረጥ ላይ ያተኮረ ነው፡፡ ይህ ማለት ከበርካታ የተጠኚ የትኩረት ቡድኖች (ለምሳሌ ት/ቤት፣ ትም/ተቋም ሊሆን ይችላል) (Target Populations) መካከል የተወሰኑትን ትምህርት ቤቶች ወይም የትምህርት ተቋማት በ% በመውሰድ በውስጡ ያሉትን ተማሪዎች ወይም ተጠኝዎች በሙሉ በመውሰድ የጥናቱ አካል ወይም ተሳታፊ የሚያደርግ የናሙና አወሳሰድ ዘዴ ነው፡፡
ዋና ዋናዎቹ ከላይ ከ1 እስከ 4 የተጠቀሱት ግምት ሰጭ የናሙና አወሳሰድ ዘዴዎች ሲሆኑ ከዚህ በተጨማሪ ሌሎች የናሙና አወሳሰድ አይነቶች እንዳሉ ታሳቢ ማድረግ ያስፈልጋል፡፡
2.ግምት የማይሰጥ ናሙና (Non-probability sampling)
ግምት የማይሰጥ ናሙና በጥናቱ ለመታቀፍ እንደሚችሉ የሚገመቱ ሰዎች ወይም ሁኔታዎች ለጥናቱ የመመረጥ እድላቸው ምን ያህል እንደሆነ የማያሳይ የናሙና ዓይነት ነው፡፡ ይህ ዓይነቱ ናሙና በቀላል ወይ ባልተወሰነ ሁኔታ እንዲካሄድ ለሚፈለጉ ትናንሽ የመነሻ ጥናቶች የሚያገለግል ነው፡፡
በዚህ ዓይነት #ናሙና የሚካሄድ ጥናት ውጤት ሊሠራ የሚችለው በናሙናነት ለተመረጡ ሰዎች ወይም ሁኔታዎች ብቻ ነው፡፡ የጥናቱ ውጤት ከናሙናው ውጭ በአጠቃላይ ስብስብ ውስጥ ላሉት ሰዎች ወይም ሁኔታዎች ለማጠቃለል አስቸጋሪ ይሆናል፡፡ ግምትየማይሰጥ ናሙና በሦስት ዋና ዋና የናሙና አመራረጥ ስልቶች ይወሰዳል፡፡ እነርሱም #የሚከተሉት ናቸው፡-
a.የምቾት ናሙና (Convenience sampling)
b. የድርሻ ናሙና (Quota sampling)
c. የታለመ ናሙና (Purposive sampling)
d.የምቾት ናሙና (Convenience sampling)
የምቾት ናሙና በቅርብ በቀላሉ የሚገኙትን የመረጃ ምንጮች የሚጠቁም የናሙና ዓይነትነው፡፡ ለምሳሌ አንድ መምህር በሚኖርበት ወረዳ ባሉት ቀበሌዎች# በHIV ትምህርት ያላቸውን ግንዛቤ ለማጥናት ይፈልጋል፡፡ መምህሩ ለጥናቱ የሚያስፈልገውን መረጃ በቅርቡበመኖሪያው አካባቢ ካሉት አምስት ቀበሌዎች ይሰበስባል፡፡ ይህ አይነቱ አሠራር ከምቾትአንፃር የሚካሄድ ይሆናል፡፡
#ናሙና በሁለት ዋና ዋና ዘርፎች ሊከፈል ይችላል፡፡ እነርሱም፡-
1. ግምት ሰጭ (Probability sampling)
2. ግምት የማይሰጥ (Non-probability sampling) #100kgchallenge ግምት ሰጭ (Probability samplijng)
ግምት ሰጭ ናሙና ከአጠቃላይ ስብስብ ውስጥ ለናሙናነት ለሚመረጡ ሰዎች ወይም ሁኔታዎች እኩል የመመረጥ እድል የሚሰጥ የናሙና አመራረጥ ዘርፍ ነው፡፡ በዚህ ዘርፍ ሥር አራት የናሙና አይነት ይገኛሉ፡፡ እነርሱም፡-
1. ነሲብ ናሙና (Simple Random Sampling)
2. በሥርዓት የተዘጋጀ ናሙና (Systematic Sampling)
3. የተከፋፈለ ናሙና (Stratified Sampling)
4. ጥምር ናሙና (Cluster Sampling)
1.2. ነሲብ ናሙና (Simple Random) Sampling)
ነሲብ ናሙና ለእያንዳንዱ በጥናት የመታቀፍ ባሕርይ ላለው ሰው ወይም ሁኔታ እኩል የመመረጥ ዕድል የሚሰጥ የናሙና ዓይነት ነው፡፡ ለጥናቱ የሚመረጡ ሰዎች ወይም ሁኔታዎች በአንድ አጠቃላይ ስብስብ ውስጥ እስከሆኑ ድረስ ተመሳሳይ ወይም የማይመሳሰሉ ቢሆኑም የመመረጥ እድላቸው ተመጣጣኝ መሆን ይኖርበታል፡፡
1.3. በሥርዓት የተደራጀ ናሙና (Systematic Sampling)
ይህ ዓይነቱ ናሙና የተወሰነ የአመራረጥ ስልትን ተከትሎ የሚካሄድ ነው፡፡ ለምሳሌ ሊመረጡ የሚችሉ የሚገመቱ 1ዐዐ ተማሪዎች ቢኖሩና ከመካከላቸው 2ዐ በናሙናነት የሚወሰዱ ከሆነ ያሉት ተማሪዎች በሙሉ /1ዐዐ/ ዝርዝራቸው ተጽፎ ከአምስት አንድ ብቻ ተለቅመው ሊወሰዱ ይችላሉ፡፡ በዚህ አሠራር 5ኛ፣ 1ዐኛ፣ 15ኛ፣ ….1ዐዐኛ ድረስ ያሉት 2ዐ ተማሪዎች ይመረጣ ማለት ነው፡፡
#የተከፋፈለ ናሙና (Stratified Sampling)
ይህ አይነቱ ናሙና ከላይ በ3.6.1.2.የተጠቀሰውን የናሙና አይነት በተሻለ መንገድ ለመግለጽ ወይም ለመጠቀም የቀረበ ነው፡፡ አጥኝው ይህንን የናሙና አወሳሰድ ዘዴ ለመጠቀም በሚያሰብብበት ወቅት የተጠኚ ኩረት ናሙናውን ያላቸውን ባሕሪያት በመጠቀም በምድብ በምድብ በመከፋፈል የተወሰነ ወካይ ቁጥር ከየምድቡ የሚወስድበት ዘዴ ነው፡፡
ለምሳሌ #ተማሪዎችን በትምህርት ውጤታቸው ከፍተኛ፣ መካከለኛ እና ዝቅተኛ ውጤት በማለትና በመከፋፈል ከእያንዳንዱ ምድብ ናሙና በመውሰድ መረጃውን የተሟላ ለማድረግ ወይም በነዚህ ባሕሪያት መካከል ያለውን ልዩነት ወይም ግንኙነት ለማጥናት ይጠቀምበታል፡፡
#ይህንን እድል ሰጭ የናሙና አይነት እንደገና በቦስት ከፋፍሎ ማየት ይቻላል፡፡ የመጀመሪያው ተመጣጣኝ ያልሆነ የተከፋፈለ ናሙና (Disproportionate Stratified sampling) በሚል የሚታወቅ ሲሆን ከእያንዳንዱ ምድብ የሚወሰደው ናሙና በየምድቡ ካለው የናሙና ቁጥር (ብዛት) ሳይሆን በአጥኝ የግል ውሳኔ እንደየሁኔታው ናሙና የሚወሰድበትና ብዙ ጊዜ ደካማ የናሙና አወሳሰድ የሚባል ነው፡፡ ምክንያቱም መረጃውን ሊያዛባው ስለሚችል ነው፡፡ ሁለተኛው ደግሞ ተመጣጣኝ የሆነ የተከፋፈለ ናሙና (Proportionate Stratified Sampling) የሚባለው የተከፋፈለ ናሙና አይነት ነው፡፡ ይህ አይነቱ ናሙና አወሳሰድ ለሁሉም ምድቦች እኩል እድል የሚሰጥ ሲሆን ሦስተኛውና የመጨረሻው ሁለንተናዊ ተመጣጣኝ የተከፋፈለ ናሙና (Optimum
Allocation Stratified Sampling) የሚባለው ሲሆን ጎናዊና ወርዳዊ ሁኔታዎችን በመፈተሽ የናሙናውን ውክልና ሁለንተናዊ ምጥጥን እንዲኖረው በማድረግ መረጃው በሚሰበሰብበት ወቅት አስተማማኝነቱን ለማጠንከር የሚያስችልና የተሳለው የናሙና ዓይነት ነው ለማለት
ይቻላል፡፡
🧲 #ጥምር ናሙና (Cluster Sampling)
ይህ ግምት ሰጭ የናሙና አይነት በናሙናዎች ቁጥር ላይ ሳይሆን አጠቃላይ ቡድንን በመምረጥ ላይ ያተኮረ ነው፡፡ ይህ ማለት ከበርካታ የተጠኚ የትኩረት ቡድኖች (ለምሳሌ ት/ቤት፣ ትም/ተቋም ሊሆን ይችላል) (Target Populations) መካከል የተወሰኑትን ትምህርት ቤቶች ወይም የትምህርት ተቋማት በ% በመውሰድ በውስጡ ያሉትን ተማሪዎች ወይም ተጠኝዎች በሙሉ በመውሰድ የጥናቱ አካል ወይም ተሳታፊ የሚያደርግ የናሙና አወሳሰድ ዘዴ ነው፡፡
ዋና ዋናዎቹ ከላይ ከ1 እስከ 4 የተጠቀሱት ግምት ሰጭ የናሙና አወሳሰድ ዘዴዎች ሲሆኑ ከዚህ በተጨማሪ ሌሎች የናሙና አወሳሰድ አይነቶች እንዳሉ ታሳቢ ማድረግ ያስፈልጋል፡፡
2.ግምት የማይሰጥ ናሙና (Non-probability sampling)
ግምት የማይሰጥ ናሙና በጥናቱ ለመታቀፍ እንደሚችሉ የሚገመቱ ሰዎች ወይም ሁኔታዎች ለጥናቱ የመመረጥ እድላቸው ምን ያህል እንደሆነ የማያሳይ የናሙና ዓይነት ነው፡፡ ይህ ዓይነቱ ናሙና በቀላል ወይ ባልተወሰነ ሁኔታ እንዲካሄድ ለሚፈለጉ ትናንሽ የመነሻ ጥናቶች የሚያገለግል ነው፡፡
በዚህ ዓይነት #ናሙና የሚካሄድ ጥናት ውጤት ሊሠራ የሚችለው በናሙናነት ለተመረጡ ሰዎች ወይም ሁኔታዎች ብቻ ነው፡፡ የጥናቱ ውጤት ከናሙናው ውጭ በአጠቃላይ ስብስብ ውስጥ ላሉት ሰዎች ወይም ሁኔታዎች ለማጠቃለል አስቸጋሪ ይሆናል፡፡ ግምትየማይሰጥ ናሙና በሦስት ዋና ዋና የናሙና አመራረጥ ስልቶች ይወሰዳል፡፡ እነርሱም #የሚከተሉት ናቸው፡-
a.የምቾት ናሙና (Convenience sampling)
b. የድርሻ ናሙና (Quota sampling)
c. የታለመ ናሙና (Purposive sampling)
d.የምቾት ናሙና (Convenience sampling)
የምቾት ናሙና በቅርብ በቀላሉ የሚገኙትን የመረጃ ምንጮች የሚጠቁም የናሙና ዓይነትነው፡፡ ለምሳሌ አንድ መምህር በሚኖርበት ወረዳ ባሉት ቀበሌዎች# በHIV ትምህርት ያላቸውን ግንዛቤ ለማጥናት ይፈልጋል፡፡ መምህሩ ለጥናቱ የሚያስፈልገውን መረጃ በቅርቡበመኖሪያው አካባቢ ካሉት አምስት ቀበሌዎች ይሰበስባል፡፡ ይህ አይነቱ አሠራር ከምቾትአንፃር የሚካሄድ ይሆናል፡፡
Data Collection for #Research(የመረጃ መሰብሰቢያ ዘዴ)
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
it is a methodical process of gathering and analyzing specific information to #proffer solutions to relevant #questions and evaluate the results.
It focuses on finding out all there is to a particular subject matter.
#Data is collected to be further subjected to #hypothesis (መላምት) testing which seeks to explain a phenomenon
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
it is a methodical process of gathering and analyzing specific information to #proffer solutions to relevant #questions and evaluate the results.
It focuses on finding out all there is to a particular subject matter.
#Data is collected to be further subjected to #hypothesis (መላምት) testing which seeks to explain a phenomenon
Call#0920560391
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0920560391
https://t.me/mamaker
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0920560391
https://t.me/mamaker
Telegram
SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy))
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
Forwarded from SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy)) (abyfamilies123)
Call#0920560391
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0920560391
https://t.me/mamaker
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0920560391
https://t.me/mamaker
Telegram
SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy))
#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
Forwarded from SETOTAW Consultancy Service ((Research (የሪሰርች ;ጥናታዊ ፅሁፍ; የቴሲስ ) and Engineering Projects consultancy)) (ABYD Families)
#MODELING THE RESEARCH
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
የትኛው አይነት የሪሰርች ጥያቄ (research question) ወይም Dependent Variable ምን አይነት Analysis ያስፈልጋል ብለን ስንመለከት ፣
1. Dependent Variable በቁጥር የሚገለጽ(Numerical variable) ከሆነ በብዛት የሚንጠቀምበት Analysis models:-
🔢 bivariate correlation (Pearson, partial etc..)
🔢 Linear regression( simple, multiple LR)
🔢 t- tests (one sample, paired, independent t test)
🔢 ANOVA (one way, two way, ANCOVA, MANOVA...) የሚባሉ ሞዴሎች ስሆን እያንደንዱን በስፈት እንመለከታለን ፣
2. #Dependent variable Categorical ከሆነ
⏸ chi square test
⏸ Logistic regression( BLR, MLR, OLR)
በአብዛኛው ጊዜ የሚንጠቀምባቸው ናቸው፣ እያንዳንዱን በጥልቀት እንደስሳለን፣
#ሌሎች multi level (Mixed model) እና Survival Analysis የሚንለቸውን በጊዜ ህደት እናያቸዋለን፣
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
#Sourc from research tetorial cited,2020
https://t.me/mamaker
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
የትኛው አይነት የሪሰርች ጥያቄ (research question) ወይም Dependent Variable ምን አይነት Analysis ያስፈልጋል ብለን ስንመለከት ፣
1. Dependent Variable በቁጥር የሚገለጽ(Numerical variable) ከሆነ በብዛት የሚንጠቀምበት Analysis models:-
🔢 bivariate correlation (Pearson, partial etc..)
🔢 Linear regression( simple, multiple LR)
🔢 t- tests (one sample, paired, independent t test)
🔢 ANOVA (one way, two way, ANCOVA, MANOVA...) የሚባሉ ሞዴሎች ስሆን እያንደንዱን በስፈት እንመለከታለን ፣
2. #Dependent variable Categorical ከሆነ
⏸ chi square test
⏸ Logistic regression( BLR, MLR, OLR)
በአብዛኛው ጊዜ የሚንጠቀምባቸው ናቸው፣ እያንዳንዱን በጥልቀት እንደስሳለን፣
#ሌሎች multi level (Mixed model) እና Survival Analysis የሚንለቸውን በጊዜ ህደት እናያቸዋለን፣
፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨፨
#Sourc from research tetorial cited,2020
https://t.me/mamaker
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#ጥናታዊ ፅሁፍ -ማማከር (Bio & Eco-stat ®SPSS,STATA, R, EVIEWS, Python, Areana, MATLAB...-GIS )
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
#ለተማሪ
#ለድርጅት
#ለመንግስት
#የቢዝነስ ፕላን -feasibility Study
፨የፕሮጀክት ስራ ፤
#የኮንስትራክሽን ስራ እና ማማከር
የ Software አቅርቦት
የፊልምና የትያትር ስክርፕት ዝግጅት
@ህጋዊ #የሙያ ፍቃድ ያለው!
Call#0970461746
Panel data, also known as #longitudinal data or cross-sectional time series data, refers to a type of dataset that contains observations on multiple individuals or entities over a period of time. Each observation in panel data represents both the cross-sectional variation (variation across individuals) and the time-series variation (variation over time).
Some key #characteristics of panel data include:
1. Individuals or entities: Panel data includes observations on multiple entities such as individuals, households, firms, countries, etc. These entities remain constant over time.
2. Time dimension: Panel data consists of multiple time periods or waves. These time periods can be evenly spaced (e.g., annual data) or unevenly spaced (e.g., quarterly data).
3. Observations: Each observation in panel data represents the values of variables for a #specific entity at a specific time point. These variables can be economic, social, demographic, etc.
4. Inclusion of #fixed effects: Panel data often includes fixed effects, which capture entity-specific characteristics that remain constant over time. Fixed effects account for unobserved heterogeneity across entities.
In econometrics and statistical analysis, panel data allows researchers to examine both the within-entity variations (over time) and the between-entity variations (across individuals or entities) simultaneously. Panel data analysis methods, such as fixed effects models, rando m effects models, and first-differences models, are used to estimate relationships and causal effects using the panel data structure.
Some key #characteristics of panel data include:
1. Individuals or entities: Panel data includes observations on multiple entities such as individuals, households, firms, countries, etc. These entities remain constant over time.
2. Time dimension: Panel data consists of multiple time periods or waves. These time periods can be evenly spaced (e.g., annual data) or unevenly spaced (e.g., quarterly data).
3. Observations: Each observation in panel data represents the values of variables for a #specific entity at a specific time point. These variables can be economic, social, demographic, etc.
4. Inclusion of #fixed effects: Panel data often includes fixed effects, which capture entity-specific characteristics that remain constant over time. Fixed effects account for unobserved heterogeneity across entities.
In econometrics and statistical analysis, panel data allows researchers to examine both the within-entity variations (over time) and the between-entity variations (across individuals or entities) simultaneously. Panel data analysis methods, such as fixed effects models, rando m effects models, and first-differences models, are used to estimate relationships and causal effects using the panel data structure.