Data is the backbone of any research paper or dissertation and is an important tool in a researcher's arsenal. The data collection methods used by researchers depend on the nature of their research topic.
Data serves as the fundamental support for every research paper or dissertation and plays a crucial role in a researcher's toolkit. The data collection methods employed by researchers are contingent upon the characteristics of their research subject. If you are engaged in the research of consumer behaviour, it is necessary to gather data through the means of surveys, observations, and experiments.
There are several tools available for use, such as SPSS, Excel, NVIVO, STATA, SAS, or any other similar software. This article examines the application of SPSS for data analysis in research.
What are the capabilities of SPSS in data analysis?
SPSS, also known as Statistical Package for the Social Sciences, was created in 1968 by SPSS Inc. and has gained significant popularity as a research software during the past 50 years. Its purpose is to verify data, deduce outcomes, scrutinise data, and generate statistical reports. It facilitates individuals in comprehending their study data, hence empowering them to derive significant conclusions from the entire process.
Social scientists can utilise it for surveys, experiments, and observational research as well. SPSS is widely utilised by scientists and researchers globally, including numerous prestigious universities and scientific institutions (Arkkelin, Daniel, 2014).
What is the functioning mechanism of SPSS?
SPSS is a useful tool for investigating the "why" aspect of research. The capacity to extract this kind of information from your data is one of the factors that scientists and academic professionals appreciate about using SPSS. SPSS can be utilised to discern patterns and trends, facilitating the anticipation of future results within your specific domain.
SPSS facilitates the comprehension of the correlation between the variables. The data analysis process in SPSS typically comprises three distinct stages:
SPSS is primarily utilised for the subsequent phase of data processing. During this phase, you will transfer your data into SPSS, categorise it into distinct categories, compute new variables, and generate graphs to effectively summarise your data.
SPSS Features
According to Alchemer (2021), SPSS provides six primary features that assist researchers in analysing intricate data. The following items are:
1. Modelling Software
The Modeller programme by SPSS enables analysts to create and validate predictive models using advanced statistical techniques.
2. Statistical Software
The SPSS Statistics programme offers a wide range of essential statistical functions, including frequencies, cross-tabulations, and bivariate statistics.
3. The SPSS Visualisation
Designer software enables scholars and researchers to effectively create a diverse range of visual representations, such as density charts, histograms, and spiral box plots, using their accessible data.
4. Data Management
Furthermore. SPSS allows researchers to efficiently organise and manipulate their data, enabling them to make informed decisions, draw conclusions, and restructure their documents.
5. Text Analytics of the Surveys Programme
The Text Analytics for Surveys programme from SPSS helps survey administrators extract valuable insights from the responses to open-ended survey questions.
6. Data Documentation
SPSS also provides data documentation, allowing scholars to store a metadata dictionary. This metadata lexicon serves as a comprehensive repository of information pertaining to data, such as definitions, links to other data, origins, usage, and format.
SPSS One-on-One Research Assistance
Learning to use SPSS independently in research might be challenging for novice users who are unfamiliar with its interface and language. In light of this, The Scholars Professional Editing Group has introduced the SPSS 1:1 Research Assistance Programme. Our services offer comprehensive data analysis and related assistance to doctorate students, encompassing qualitative, mixed-method, and quantitative approaches. These services aim to enhance candidates' understanding of their data, improve the effectiveness of their dissertation writing, and support them in defending their conclusions.
Our statistics work closely aligns with the student, guiding them systematically and assisting them in navigating the full data analysis process till it is finished. The data analysis procedure offers adaptable scheduling and is often finalised within a timeframe of 1-2 weeks.
This programme is segmented into three distinct phases, which are delineated and explained as follows:
Phase I: involves importing, preparing, organising, and scoring the data.
Phase II: involves the process of data filtering and analysis.
Phase III: includes tasks such as data visualisation, generating result reports, creating sample write-up templates, and reviewing narrative submissions.
