The document discusses various techniques for analyzing qualitative and quantitative data in research. It describes different types of statistical analysis that can be used for organizing, summarizing, and drawing conclusions from data, including descriptive statistics, correlation analysis, multivariate analysis techniques like multi regression analysis, discriminant analysis and factor analysis. It also discusses using t-tests to analyze experimental research data by comparing experimental and control groups.
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It is about Data analyzis that refers to sifting, organizing, summarizing and synthesizing the data so as to arrive at the results and conclusions of the research.
Quantitative research is the systematic investigation that plays attention to numerical or statistical values in a bid to find answers to research questions.
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In educational research, Research errors may be grouped under some headings:
1. Sampling errors
2. Measurement errors
3. Statistical errors
4. Interpretation errors
along with suggestions to reduce them
·Quantitative Data Analysis StatisticsIntroductionUnd.docxlanagore871
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Quantitative Data Analysis: Statistics
Introduction
Understanding the use of basic statistical strategies is part of being a critical consumer of published research literature. Unless they plan to conduct research themselves, it is not as important for counselors to understand the mathematical calculations of the statistical techniques as it is to be able to recognize the names of the common ones and what kind of information they provide. There are several commercially-available software packages for analyzing quantitative data, one of which is described in detail in Chapter 14 of
Counseling Research: Quantitative, Qualitative, and Mixed Methods
.
Descriptive and Inferential Statistics
In quantitative studies, statistical techniques are used for data analysis. The two main categories of statistics are descriptive and inferential. Descriptive statistics are used to summarize the data. Some common descriptive statistics are the measures of central tendency: the mean, median, and mode. They provide information about where the middle is in distribution of scores. On the normal distribution, the mean, median, and mode are the same. Distributions are said to be skewed when extreme scores draw the mean away from the middle of the distribution. Measures of variability, such as the range, variance, and standard deviation, provide information about how widely a distribution of scores is dispersed (Erford, 2015, p. 250). The standard deviation is a measure of how the scores cluster around the mean. The greater the standard deviation, the greater the spread of scores.
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Inferential statistics are used to make inferences from the sample to the population. All inferential statistical procedures are based on probability theory. They are used to test hypotheses. Three commonly used inferential statistics are chi square, t-test, and analysis of variance (ANOVA). Chi square is used with nominal data to determine if the observed expected frequency differs significantly from the expected frequency. A t-test is used to determine whether there is a statistically significant difference between the means of two groups. ANOVA is used to determine whether there is a statistically significant difference between the means of three or more groups.
Statistical Significance
When a quantitative study tests a hypothesis, it is technically the null hypothesis being tested. The null hypothesis says there is no difference between the groups, or relationship between the variables (depending on the research design). If the statistical procedure indicates there is statistical significance, the null hypothesis is rejected, meaning that the probability is high that there really is a group difference or strong relationship between the variables.
Rejecting the null hypothesis is not equivalent to proving the research or alternative hypothesis. Researchers can embrace the research hypothesis as one plausible explanation, but because only .
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2. Data Analysis
Data analysis refers to organize and summarize all the
data and hace results on the conclusion of the research
and are involve a variety of techniques for analyzing
data.
3. Quantitative Research
In a quantitative research the data is in a numerical
form and statistics make the research more manageable
and efficient.
4. Qualitative Research
The acquired data is non numerical and using
qualitative procedures have a heavy burden on the
research.
5. Pragmatic and non pragmatic
Statics.
The pragmatic statics have a number of set assumptions
but also are more powerful than non pragmatic statics.
The non pragmatic statics are use for numina and
ordinal data but there are less powerful in sense that is
not possible to use them to establish hypothesis.
6. Analyzing Qualitative Research
Data
In a qualitative research some data is collected by
certain procedures for example constructed
observations, open interviews and diaries. The data is
usually in the form of words and written documents.
7. Analyzing Descriptive Data.
The data obtained from a descriptive research analyzed
the aids of the descriptive statics. The different types
of descriptive statics are central tendencies and
variability.
8. Analyzing Correctional Data.
Correctional techniques are used for analyze the data
from a descriptive research. Also examines existing
relationships between variables. A correlation is very
useful for the different purposes of a research.
9. Analyzing Multivariate research
Data.
This can be analyzed through a set of techniques where
the number of dependent variables ore one number of
independent variables are analyze tremendously.
10. The three multivariate research
data:
Multi regression.
Discriminant Analysis.
Factor Analysis.
11. Multi Regression Analysis.
This examines the relationship and the predicative
power of one or more independent variables.
12. Discriminant Analysis.
This is concerned with the predication of membership in
one or more categories or a dependent variable from
scores on two or more independent variables.
13. Factor Analysis
Helps the researcher make large sets of data more
manageable by identifying the factors that underline
the data.
14. Analysis Experimental Research
Data.
When two groups experimental and control are being
compared. The researcher will use the T-test which is
capable of comparing two groups on a given measure.
The T-test helps to determine how confident the
researchers can be with the differences found between
two groups.
