This document discusses different methods for collecting and analyzing quantitative and qualitative data in research. It describes the following key points:
- Quantitative data involves numerical data that can be statistically analyzed, while qualitative data involves non-numerical data like text.
- Common statistical analyses for quantitative data include descriptive statistics like frequencies, means, and variability measures. Correlational research examines relationships between variables. Experimental research compares means between groups using t-tests or analyzes variance between groups using ANOVA.
- Qualitative data analysis involves deriving categories from text and identifying patterns. It requires intuition to understand the data.
- The document outlines various multivariate techniques like regression, discriminant analysis, and factor analysis that can analyze multiple
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Part of a course I run introducing quantitative methods. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful.
Data Analysis in Research: Descriptive Statistics & NormalityIkbal Ahmed
A Presentation on Data Analysis using descriptive statistics & normality. From this presentation you can know-
1) What is Data
2) Types of Data
3) What is Data analysis
4) Descriptive Statistics
5) Tools for assessing normality
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Types of Data, Difference between Primary and Secondary Data, Collection of Primary Data, Questionnaire, Schedules, Interview, Survey, Observation, Secondary Data, Sources of Secondary Data, Tabulation of Data – Meaning and Types
Part of a course I run introducing quantitative methods. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful.
Data Analysis in Research: Descriptive Statistics & NormalityIkbal Ahmed
A Presentation on Data Analysis using descriptive statistics & normality. From this presentation you can know-
1) What is Data
2) Types of Data
3) What is Data analysis
4) Descriptive Statistics
5) Tools for assessing normality
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Types of Data, Difference between Primary and Secondary Data, Collection of Primary Data, Questionnaire, Schedules, Interview, Survey, Observation, Secondary Data, Sources of Secondary Data, Tabulation of Data – Meaning and Types
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.
Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis
·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 .
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
2. • Data analysis or data collection consists in organizing,
summarizing and synthetizing the data to get the results.
• The data analysis technique will depend on the research
problem, the design chosen and the type of data collected
3. Data collection in quantitative research
In quantitative research the data will be numerical or the
data will be converted in numbers and the analysis will need
statistics
Qualitative data will deal with non-numerical data. Most
likely, in linguistic units of oral or written speech.
4. It is usually taught that not using statistics makes the research
easier to conduct. On the contrary, using them makes the
research more manageable. There are many statistical packages
which can help to manage the data.
5. Data collection in qualitative research
Qualitative requires intuition and understanding concerning the
data. It is a more complex task.
There are several assumptions related to using parametric
statistics. They are not strong enough to reject a whole hypothesis.
One assumption is that the variable studied is normally distributed
in the population.
Since most variables are normally distributed the assumption is
usually met.
A second one is that data represent an interval or radio scale of
measurement. Measures used in second language acquisition
represent interval data. The assumption is also usually met.
The third one is that subjects are selected independently to study.
So, selection of one subject does not affect the selection of others.
This will be the case whenever the sample is randomly selected.
6. Qualitative requires intuition and understanding concerning the data. It is a
more complex task.
Two main techniques can be identified when analyzing qualitative data.
Deriving a set of categories for dealing with text segments. This is a
procedure merely inductive. Once the categories have been set, they are
applied to the reminder data, which leads to the refinement of categories and
the discovery of new patterns. This type of research is descriptive and
exploratory.
These studies are more confirmatory and aim at some kind of explanation.
7. Descriptive research
Data is generally analyzed with descriptive statistics. These will provide
information such as how often certain language phenomena occur, typical
use of language elements, and the relationship between variables, among
others.
The types of statistics used in descriptive research are frequencies, central
tendencies and variabilities.
The Frequencies are used to know how often a phenomenon occurs and are
based on counting the number of occurrences. Very useful in second language
acquisition research, where the main interest lies in how often elements of
language are used. They also provide information about the performance of
the subjects on tests and questionnaires before the results are used for
analyzing the data.
8. Correlational data
It is obtained from the descriptive research and examines the relationship
between variables without manipulate them.
Multivariate data
Obtained from the multivariate research. It can be analyzed through a set
of techniques where a number of dependent and independent variables
are analyzed simultaneously.
These techniques can be applied when researching language aptitude,
personality or learner’s background.
9. There are three multivariable procedures.
The first is multiple regression. This permits examine the relationship
and predictive power of one or more independent variables with the
dependent variable.
The second is discriminant analysis. This is about which combination of
independent variables distinguish the most between two or more
categories of the dependent variable. An example could be male/female,
monolingual/bilingual, etc.
The third is the factor analysis. This helps the researcher to manage
larger sets of data by identifying factors that underline the data. This
type of analysis is based on the assumption that variables that measure
the same factor will be highly related, while the ones which measure
different factors will have low correlations.
This kind of analysis has been used in second language learning to
validate factors that are believed to underline different language
constructs such as proficiency, aptitude and attitude to learn the second
language.
10. Experimental data
In experimental research, when two groups (experimental and control)
are being compared, the researcher will use something called the t-test.
This is used to compare the means of two groups. The results gotten
with this test are called t-value. That value is entered in t-values chart.
One way analysis of variance is another technique used to collect
experimental data. This analysis is performed on the variance of the
groups and is focused on whether the variability between the different
groups is greater than the variability within each of the groups. The F
value is the radio of the between variance over the within variance. A
significant F will occur when the variability among the group is greater
that the variability within the group.
11. Chi square
This data analysis procedure helps the researcher to address
questions about relations between two nominal variables.
During the procedure, the researcher compares the
frequencies observed in a sample with the expected
frequencies.
Using the computer for data analysis
Most of the data analysis techniques described here can be
performed with the computer. There are many packages which
can help you to do it. Nevertheless, it is important to know
how to use them in order to have good results. A computer
analysis must be planned and attention to small details must
be given.