Analisis Data (Mean, Median, Modus)

Statistics Branch of science that deals with the collection, organization, analysis, interpretation, and presentation of data. Purpose of statistics is to make an inference about a population by examining a sample. Collection of Data Refers to the process of gathering information. Primary source: questionnaires, interviews. Secondary source: journals, books. Presentation of Data Organization and arrangement of data in tabular form (tables), graphical representation (graphs). Analysis of Data Application and interpretation using statistical tools to derive meaning. Inspection of Data Descriptive Statistics: Involves describing the basic features of the data in a study. Provides simple summaries about the sample and measures. Inferential Statistics: Makes judgments and reaches conclusions about populations based on samples. Population vs Sample Population: entirety Sample: only part of population Fundamental or Descriptive Statistics: Measures Of Central Tendency: Mean (average) Median (middle value) Mode (most frequent) Measures Of Dispersion/Variability: Range (difference between highest & lowest) Variance & standard deviation Measures Of Position: Quartiles & percentiles Measures Of Relationship Between Variables: Correlation coefficient Types Of Data: Primary Data – original source. Secondary Data – derived from primary. Major Characteristics Of Data Sources: Primary Source – direct from respondent; more accurate; costly; time-consuming. Secondary Source – characteristic not

Teaching English Introduction to the course: Language learning and teaching A 2 Characteristics of the language learners: Studying a system that aligns with international standards. A3 Cognitive factors in language learning: Addressing questions and obtaining necessary information regarding phenomena such as transfer, interference, and generalization; A4 Inductive and deductive language learning: ability and intelligence; and the phenomenon of systematic forgetting A5 Language learning methods and strategies: Familiarizing with foreign language learning methods, strategies for learning foreign languages, and communication strategies A6 International Assessment System of language skills in CEFR, IELTS, TOEFL: effective methods and strategies used to improve language skills (listening, reading, writing, speaking) A7 Psychological factors in language learning: Exploring various psychological factors such as self-esteem, shyness, risk-taking, anxiety, attitude, and motivation A8 The effectiveness of authentic materials during the learning process: The role of tasks and games in teaching foreign languages A9 Errors in language learning: Discussing types of errors, identifying and describing errors, causes of errors, and fossilized errors A10 Error correction or error analysis approaches: developing students' ability to apply their knowledge in practicing error correction A11 Age-related factors in language learning: Exploring types of comparisonand contrast, focusing on topics such as the age hypothesis and bilingualism, and providing a detailed explanation of these concepts A12 Teaching grammar: Studying grammar teaching methods; deductive and inductive approaches in grammar teaching; A13 Teaching grammar through context: linguistic intuition; language phenomena; using grammatical dictionaries; analyzing grammatical tasks; and designing exercises, tasks, and tests through completing grammarbased activities. A14 Teaching vocabulary. Seeking answers to questions such as 'What is a word?' and 'What does it mean to learn a word?' A15 Teaching vocabulary in context: teaching lexical units/phrases/collocations; introducing new vocabulary; using corpus data for pedagogical purposes; developing students' vocabulary learning strategies A16 Assessing vocabulary tasks: designing vocabulary tasks, exercises, and tests. In international assessment systems such as CEFR, IELTS, and TOEFL, grammar accuracy and lexical resource A 17 Teaching pronunciation: Understanding the importance of pronunciation for successful communication; teaching stress; teaching intonation A18 Modern technologies in teaching pronunciation A19 Error correction methods: watching to various experiences in this area and analyzing video lessons from international experts in the field A20 Analysis of skill integration in language learning: Understanding the stages of developing skill integration; integration of the four language skills; task-based integration; and project-based integration. A21 Teaching listening comprehension A22 Modern technologies in teaching listening comprehension A23 Teaching Speaking A24 Modern technologies in teaching Speaking A25 Teaching Reading A26 Modern technologies in teaching Reading A27 Teaching Writing A28 Modern technologies in teaching Writing A29 The role and importance of translation in teaching a foreign language A30 Module 2. International standards for teaching and assessment Classroom Language: The teacher's actions; the teacher's voice; the teacher's intonation; using the foreign language in the classroom A31 Foreign language environment: asking questions in the foreign language, giving instructions in the foreign language, providing oral explanations in the foreign language, and issues related to the use of the native language in the foreign language class. A 32 Designing curriculum: Studying, analyzing, and working with curricula designed for schools, lyceums, and colleges. A33 Planning lessons and the structure of lesson plans: determining thesequence of lessons, objectives, tasks, and expected outcomes; choosing the lesson structure for planning A34 Designing tasks for different stages of the lesson: Starting the lesson; concluding the lesson; connecting tasks within the lesson A35 Time management: allocating appropriate time for tasks during the lesson; and providing homework assignments A36 Educational materials and resources: Effective use of existing educational materials and resources; anticipating and addressing potential issues Planning and adapting materials: to the situation during teaching and working on lesson planning for groups of students with different abilities. A37 Classroom research: Stages of classroom research, data collection, analysis, and planning; creating/preparing the materials needed for data collection; distinguishing between the positive and negative aspects of the research A38 Data analysis: creating/preparing the materials needed for data collection; distinguishing between the positive and negative aspects of the research. A39 Peer lesson observation: Observing lessons; conducting interviews; questionnaires for teachers and students; maintaining a daily record; discussing problematic situations/events; notes and other aspects; the process of lesson observation: stages of observation; presenting observation results both orally and in writing. A40 Educational materials and national values: important tool for implementing and promoting educational standards, as well as national values. A41 Differences between methods of teaching foreign languages: practical application of modern methods in language teaching; foreign experiences in language teaching: the grammar-translation method; the method of conducting lessons entirely in the foreign language; the audio-linguistic method; and communicative methods. A42 Methods used in the local environment and their analysis: Discussion of the positive and negative aspects of various methods; language and culture; teaching/learning processes; the role of the native language in learning a foreign language; and the psychological foundations of foreign language teaching. A43 Teaching a foreign language through computer technologies A44 Types of independent work and its implementation A45 Principles of Assessment in foreign language teaching А46 Issues in Language Assessment А47 Alternatives in Assessment А48 Test methods. Methods and criteria for assessing language aspects: written expression, reading, listening comprehension, speaking, А49 integrating language skills: vocabulary; grammar; alternative forms of assessment; planning assessment; critical analysis; principles for designingtest tasks: scientific rigor, consistency, conciseness, clarity, informality, logical sequence, and systematic approach. А50 Foreign language for ESP. Studying and analyzing needs; setting objectives for teaching a foreign language in a specific field or professional area; defining teaching approaches in curriculum development; and discussing topics related to these areas. А51 Selecting textbooks, materials and resources А52 Content-based Instruction (CBI) А53 Strategies-based Instruction А54 Lifelong Learning: Teacher development, PreSETT, InSETT А55 The Role of Teaching Practice A56 Organization and implementation of compulsory and non-compulsory course process in foreign language teaching A57 Organization in and outclass activities A58 Defining the goals and content of foreign language teaching at various levels of the education system in the Republic of Uzbekistan: evaluating educational materials; adapting educational materials; creating educational materials; and discussing the role of the foreign language teacher in specialized fields to gain relevant information. A59 The role of independent study skills: foreign language focused on reading, research and study skills; make revision questions. incclude mcq question. answer the question. true false

Steps in Thematic Analysis.Step 4: Reviewing themes Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better? If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate. For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust. Step 5: Defining and naming themes Now that you have a final list of themes, it’s time to name and define each of them. Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data. Naming themes involves coming up with a succinct and easily understandable name for each theme. For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”. Step 6: Writing up Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach. We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions) and explaining how we conducted the thematic analysis itself. The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question. In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

ANALISIS DATA

ANALISIS DATA KELAS 8

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