0 Employer Employee Scheme.pptx
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Employer-Employee Insurance Scheme is an insurance arrangement between an employer and employee where the employer purchases an insurance policy for the employee. This arrangement benefits both parties as it is based on the principle that the employer has an insurable interest in their employees.
0 Employer Employee Scheme.pptx
- 1. Dr. J D Chandrapal MBA – marketing , PGDHRM, P HD, CII (Award) – London Development Officer - LIC of India – Ahmedabad - 9825070933
- 2. EE Scheme – What? • Employer-Employee Insurance Scheme is an insurance arrangement between the two.... • Where, the employer purchases an insurance policy for the employee. • This arrangement is based on the principle that the employer has an insurable interest in his/her employees. • The interesting fact is that both the employee and the employer is benefited through this arrangement.
- 3. Qualitative vs. Quantitative Content Analysis Area of Research - First, the research areas from which they developed are different. Quantitative content analysis is used widely in mass communication as a way to count manifest textual elements, an aspect of this method that is often criticized for missing syntactical and semantic information embedded in the text. By contrast, qualitative content analysis was developed primarily in anthropology, qualitative sociology, and psychology, in order to explore the meanings underlying physical messages. Inductive vs. Deductive - Second, quantitative content analysis is deductive, intended to test hypotheses or address questions generated from theories or previous empirical research. By contrast, qualitative content analysis is mainly inductive, grounding the examination of topics and themes, as well as the inferences drawn from them, in the data. In some cases, qualitative content analysis attempts to generate theory. Sampling Technique - Third, the data sampling techniques required by the two approaches are different. Quantitative content analysis requires that the data are selected using random sampling or other probabilistic approaches, so as to ensure the validity of statistical inference. By contrast, samples for qualitative content analysis usually consist of purposively selected texts which can inform the research questions being investigated. Outcomes - Fourth, the products of the two approaches are different. The quantitative approach produces numbers that can be manipulated with various
- 4. Content Context Users Content Analysis – What? Text (Any Occurrence of communicative Language) Words Images Video Features Physical Items Signage etc. It is use to systematically summarize written, spoken, or visual communication in a quantitative way., It’s About Patterns and Relationships Texts can be defined as books, book chapters, essays, historical-documents, headlines-articles, conversations, interviews, discussions, speeches Context can defined as: “The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood • Managers • Organization • Competitors • Customer • Employees • Share Holders • Suppliers • Other Stakeholders
- 5. Six Questions - Addressed in Content Analysis Data Which data are analyzed? (do not carry any specific meaning) Six Questions Context What is the context relative to which data are analyzed? Information How are they defined? (Carry specific and logical Meaning) Population What is the population from which the data drawn? Target What is the target of the inferences? Boundaries What are the boundaries of the analysis?
- 6. Content Analysis - Glossary Understanding Participant’s View: Transcribed interview texts and read & re-read to get a sense of whole, i.e., to understanding of participant’s View. Meaning Units: Start dividing up the text into smaller parts, namely, into meaning units. meaning unit is also refereed to as 'unit of analysis' Condensation: Condensation is a process of shortening the text. Condenses these meaning units further, ensure that the core meaning is still retained. Coding: Label/name condensed meaning units by formulating codes in a way that most exactly describes what this particular condensed meaning unit is about. Theme: Themes are expressing data on an interpretative (latent) as it is expressing an underlying meaning. It is intended to communicate both an intellectual-emotional level. poetic and metaphoric language to express meaning Categorization: Grouping together those words with similar meaning or connotation and the codes that are related to each other through content/context.
- 7. Manifestation vs. Latent (Abstraction) A b s t r a c t i o n Higher Level Reflects the interpreted, latent meaning of the text Lower Level Close to the text and manifest content Theme Category Code Condensed Meaning Unit Meaning Unit Price doesn’t matter, quality of product matters Product Quality Dissatisfaction Unhappy with the use of product Unfortunately, I haven’t good experience for the use of the product ‘A’. I think, there is some better options.
- 8. Types of Content Analysis • Determines the existence and frequency of concepts in a text. i.e. quantifying and counting its presence. • The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. • Develops the conceptual analysis further by examining and exploring the relationships among concepts in a text • Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.
- 9. Types of Content Analysis Conceptual content Analysis Relational Content Analysis Decide the level of analysis Decide how many concepts to code for Decide whether to code for existence or frequency of a concept Decide on how you will distinguish among concepts Develop rules for coding your texts Decide what to do with irrelevant information Code the text Analyze your results Decide the level of analysis Reduce the text to categories and code for words or patterns Explore the relationship between concepts Strength – Sign - Direction Code the relationships Perform statistical analyses Map out representations
- 10. Three Approaches - Content Analysis Coding categories are derived directly from text data. Generally used with a study design whose aim is to describe a phenomenon and research literature on a phenomenon is limited. Starts with theory or relevant research findings as guidance for initial codes. The goal of a directed approach is to validate or extend conceptually a theoretical framework or theory. Involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. Study starts with keywords and center on how initial codes are developed Conventional Directed Summative Coding schemes, Origins of codes, and Validity.
- 11. Major Differences Type of Content Analysis Study Starts With Timing of Defining Codes or Keywords Source of Codes or Keywords Conventional Observation (Inductive) Codes are defined during data analysis Codes are derived from data Directed Theory (Deductive) Codes are defined before and during data analysis Codes are derived from theory or relevant research findings Summative Keywords (Mixed) Keywords are identified before and during data analysis Keywords are derived from interest of researchers or review of literature
- 12. Content Analysis - Why? • Researchers use content analysis to find out about the purposes, messages, and effects of communication content to provide knowledge and understanding of the phenomenon under study • Content analysis explores human experience and they are expediently studied through analyzing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. • The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organized and concise summary of key results and examining trends and patterns in documents • They can also make inferences about the producers and audience of the texts they analyze. • Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.
- 13. • Content Analysis can be Applied to…. Examine any piece of writing or occurrence of recorded communication; thus it used in a dizzying array of fields such as Marketing and media studies, Ethnography and Cultural studies, Sociology , Political science, Psychology and Cognitive science, and many other fields of inquiry. • Uses of content analysis: • Detect the existence of ideas, Concepts and truth hidden in the texts. • Identify the intentions, focus or communication trends of an individual, group or institution • Describe attitudinal and behavioural responses to communications • Determine psychological or emotional state of persons or groups Uses of content Analysis
- 14. Reliability Accuracy Closeness of categories Consistently re-code the same data in the same way over a period of time Tendency for group of coders to classify categories in the same way. Extent to which classification of text corresponds to a norm or standard statistically Concept category can be broadened to include synonyms or implicit variables. Do conclusions correctly follow the data? Are results explainable by other phenomena? Generalizability of the results to a theory Stability Reproducibility Conclusions Generalizability Validity
- 15. Advantages of content Analysis Disadvantage Advantage Unobtrusive Data Collection Highly Flexible Communication & social interaction can be analyzed without direct involvement of participants, so presence of researcher doesn’t influence results. You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources. Transparent and Replicable content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability Reductive Time Intensive Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings. Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively. Subjective Content analysis involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions.
- 16. Thank You
- 17. General steps - Conceptual Content Analysis 1. Decide the level of analysis: word, word sense, phrase, sentence, themes 2. Decide how many concepts to code for: develop pre-defined or interactive set of categories or concepts. Decide either: A. To allow flexibility to add categories through the coding process, or B. To stick with the pre-defined set of categories. Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question. Option B allows the researcher to stay focused and examine the data for specific concepts. 3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process. When coding for the existence of a concept, count a concept only once if it appeared at least once in the data and no matter how many times it appeared. When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.
- 18. General steps - Conceptual Content Analysis 4. Decide on how you will distinguish among concepts: Should text be coded exactly as they appear or coded as the same when they appear in different forms? The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes. 5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity. 6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?
- 19. General steps - Conceptual Content Analysis 7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize error far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding. 8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified
- 20. Reliability: Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis: 1. Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time. 2. Reproducibility: tendency for a group of coders to classify categories membership in the same way. 3. Accuracy: extent to which the classification of text corresponds to a standard or norm statistically. Reliability and Validity Validity: Three criteria comprise the validity of a content analysis: 1. Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables. 2. Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could
- 21. How to Conduct Content Analysis If you want to use content analysis in your research, you need to start with a clear, direct research question. Example research question for content analysis Is there a difference in how the US media represents male and female politicians in terms of trustworthiness? Next, you follow these five steps. 1. Select the content you will analyze Based on your research question, choose the texts that you will analyze. You need to decide: The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy) The criteria for inclusion (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product) The parameters in terms of date range, location, etc. If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample. To research media representations of male and female politicians, you decide to analyze news articles and opinion pieces in print newspapers between 2017–2019. Because this is a very large volume of content, you choose three
- 22. Unobtrusive data collection You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results. Transparent and replicable When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability. Highly flexible You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources. Disadvantages of content analysis Reductive Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings. Subjective Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions. Time intensive Manually coding large volumes of text is extremely time-consuming, and it can Advantages of content Analysis