Oct 16, · When carrying out dissertation statistical analyses, many students feel that they have opened up a Pandora’s blogger.com of the common issues that cause such frustration in the dissertation statistical analyses include a poorly developed methodology or even an inadequately designed research framework. But if the foundation of your research is completed logical, then statistical analysis Qualitative research presents “best examples” of raw data to demonstrate an analytic point, not simply to display data. Numbers (descriptive statistics) help your reader understand how prevalent or typical a finding is. Numbers are helpful and should not be avoided simply because this is a qualitative dissertation For example, for a dissertation entitled Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words
Research Methods for Dissertation | Research Prospect
Sampling can be explained as a specific principle used to select members of population to be included in the study. In other words, due to the large size of target population, researchers have no choice but to study the a number of cases of elements within the population to represent the population and to reach conclusions about the population see Figure 1 below.
Figure 1. Population, sample and individual cases [2]. Brown summarizes the advantages of sampling in the following points [3] : a Makes the research of any type and size manageable; b Significantly saves the costs of the research; c Results in more accurate research findings; d Provides an opportunity to process the information in a more efficient way; e Accelerates the speed of primary data collection. The process of sampling in primary data collection involves the following stages:.
Defining target population. Target population represent specific segment within wider population that are best positioned to serve as a primary data source for the research. Choosing sampling frame. Sampling frame can be explained as a list of people within the target population who can contribute to the dissertation data collection analysis. For a sample dissertation named above, sampling frame would be an extensive list of UK university students.
Determining sampling size. This is the number of individuals from the sampling frame who will participate in the primary data collection process. The following observations need to be taken into account when determining sample size:. b There are greater sample size requirements in survey-based studies than in experimental studies. c Large initial sample size has to be provisioned for mailed questionnaires, because the percentage of responses can be as low as 20 to 30 per cent, dissertation data collection analysis.
d The most important factors in determining the sample size include subject availability and cost factors. Selecting a sampling method. This relates to a specific method according to which university students in the UK are going to be selected to participate in research named above. Applying the chosen sampling method in practice. Sampling methods are broadly divided into two categories: probability and non-probability. In probability sampling every member of population has dissertation data collection analysis known chance of participating in the study.
Probability sampling methods include simple, stratified systematic, multistage, and cluster sampling methods. In non-probability samplingdissertation data collection analysis, on the other hand, sampling group members are selected on non-random manner, dissertation data collection analysis, therefore not each population member has a chance to participate in the study. Non-probability sampling methods dissertation data collection analysis purposive, quota, convenience and snowball sampling methods.
The Figure 2 below illustrates specific sampling methods belonging to each category:. The following table illustrates brief definitions, advantages and disadvantages of dissertation data collection analysis techniques:. Explanation Advantages Disadvantages Random Sample group members are selected in a random manner Highly effective if all subjects participate in data collection High level of sampling error when sample size is small Stratified Representation of specific subgroup or strata Effective representation of all subgroups.
Precise estimates in cases of homogeneity or heterogeneity within strata Knowledge of strata membership is required. Complex to apply in practical levels Systematic Including every Nth member of population in the study Time efficient.
Cost efficient High sampling bias if periodicity exists Multistage Sampling conducted on several stages High level of flexibility at various levels Complex to conduct. Impacted by limitations of cluster and stratified sampling methods Cluster Clusters of participants representing population are identified as sample group members Time efficient.
Usually higher sampling errors compared to alternative sampling methods Judgement Sample group members are selected on the basis of judgement of researcher Time efficiency. Personal bias Quota Sample group members are selected on the basis of specific criteria High level of reliability than random sampling. Difficult to estimate sampling error Convenience Obtaining participants conveniently with no requirements whatsoever High levels of simplicity and ease.
Selection bias Snowball Sample group members nominate additional members to participate in the study Possibility to recruit hidden population Over-representation of a particular network. Reluctance of sample group members to nominate additional members.
My e-book, Dissertation data collection analysis Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods.
The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection.
Important elements of dissertations such as research philosophyresearch approachresearch designmethods of data collection and data analysis are explained in this e-book in simple words, dissertation data collection analysis. Effective representation of all subgroups Precise estimates in cases of homogeneity or heterogeneity within strata. Knowledge of strata membership is required Complex to apply in practical levels.
Complex to conduct Impacted by limitations of cluster and stratified sampling methods. Clusters of participants representing population are identified as sample group members. Group-level information needs to be known Usually higher sampling errors compared to alternative sampling methods.
High level of subjectivity Difficult to estimate sampling error. Over-representation of a particular network Reluctance of sample group members to nominate additional members.
Dissertation Data Analyses Road Trip
, time: 27:55Data Collection | A Step-by-Step Guide with Methods and Examples
Nov 09, · Choosing the right research method for a dissertation is a grinding and perplexing aspect of the dissertation research process. A well-defined research methodology helps you conduct your research in the right direction, validates the results of your research, and makes sure that the study you’re conducting answers the set research questions. The research title, research questions, For example, for a dissertation entitled Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words The most popular quantitative data collection methods are closed-ended questionnaires, experiments, correlation and regression analysis methods and others. My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of quantitative methods
No comments:
Post a Comment